Travel Charactenrstics in Cities of L(IDeveloping and Developed Countries

SWP230

World Bink Staff Working Paper No. 230

March 1976

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This paper is for staff use.The views expressed are those ofthe author and not necessarilythose of the Bank.

INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPPUFNT

Staff Working Paper No. 230

March 1976

TRAVEL C -ARACTERISTICS IN CITIES OF-DEVELOPING AND DEVELOPED COUNTRIES

The main purpose of this paper is to explore the systematiccomponents of travel characteristics in cities of both developing anddeveloped countries. The results may assist in the review of urbantransport studies, in testing the consistency of forecasts and in dis-cussions about alternative plans for urban form and transportation.

The results suggest that there is a basic similarity in thetravel characteristics and the general behavior patterns in both developedand developing countries. This implies that stable and predictableeffects follow from the fundamental restraints of time budgets, moneybudgets and system supply.

The travel characteristics described in the paper suggestthe existence of a basic mechanism relating the demand for, andthe supply of, urban transport facilities, and the arrangement ofland uses in urban areas.

@

Prepared by:

Yacov ZahaviConsultantTransportation and UrbanProjects Department

Central Projects Staff

Ac knowledgements

The author would like to extend his thanks to the many who havebeen helpful in providing data, comments, criticism and encouragement,and especially to:

D. S. Gendell U. S. Department of TransportationS. L. Zimmerman

P. B. Goodwin The Greater London CouncilM. J. H. Mbgridge "

J. C. Tanner Transport & Road Research LaboratoryF. V. Webster "

R. J. Smeed University College London

A. Swiebel Verkeersdienst Rotterdam

E. V. K. Jaycox Transportation & Urban Projects Department, IBRDG. J. BeierA. ChurchillH. B. DunkerleyC. G. HarralS. HennemanG. J. Roth tR. Venkateswaran "A. A. Walters t

The responsibility for the analyses and the interpretation of theresults rests, however, with the author alone.

(i)

C O N T E N T S

Introduction (v}

Prefaoe (vii)

Chapter lt The Vehicles

Introduotion 11.1 Increase of car population 21.2 Motorization 41.3 Estimating the saturation level of motorization-countries 6;

1.4 Estimating the saturation level of motorization-cities 8

1.5 Forecasting motorization levels ICi1.6 The effects of income on motorization 1l2

1.7 Effects of income on motorization - within cities 14.

1.8 Households owning vehioles vs. motorization l'

1.9 Motorcycles 18

1.10 Commercial vehicles 201.11 Buses and taxis 22'

1.12 In conclusion 24

Chapter 2: The Transportation System

Introduction 2';

2.1 The arterial road density 2l;

2.2 The expressway road density 213

2.3 Dynamic capacity of the road network 3t)

2.4 The rapid transit system 34

Chapter 3: Travel Charaoteristics

Introduction 353.1 Expenditure on travel 365

3.2 Rail vs. bus 333.3 Modal Split 403.4 Public transport trip rates 4:2

3.5 The daily trip rates of cars and public transport 443.6 The concept of Traveltime Budgets 453.7 The car daily traveltime 46

3.8 Tripmakers 483.9 Daily traveltimes per tripmaker 50

3.10 Daily traveltime per person.,and the effects of

income on the TT-budget 51

3.11 Car average trip distance 543.12 The ranking of trips by purpose 563.13 Proportions of trip purposes 583.14 The perceived value of mobility 60

3.15 The value of saved traveltime 623.16 Cross sectional vs. temporal travel characteristics 633.17 Travel behavior in the public transport mode 653.18 Captive vs. choice public transport tripmakers 66

3.19 The interaction of rapid.transit with other modes 683.20 Car and,motocycle occupancy rates 703.21 Commercial vehicle trips 7'2

3.22 In conclusion 73

( ii )

Chapter 4 ' The City and its Mobility

Introduction 754.1 Effects of city sieg population density and

motorization on road density 764.2 The city and its mobility 79403 The city structure 81

References 83

Appendices

1l Peroent of households oming a car kg income 852. Percentage of households o0iing v&oioles vs. motwOAation ?63- Proportions of motorcycles 874. Modal Split 895- Car daily traveltimes in selected cities 916, A procedure for macro-assiwant cm a road system 27. The Cities mentioned in the report 95

T A B L E S A N D F I G U R E S

Chapter Table Figure Description Page

1. 1.1 1.1 The world's car population 31.2 1.2 The world's car motorization 51.3 1.3 Motorization rate of change vs. Mot.- countries 71.4 t " " " " - cities 8

1.4 "I gS of to Pers. Mot. 91.5 ., tl it IS HE Mot. 9

1.5 Trends of motorization growth in three countries 101.6 Car motorization forecast in the U.K. 111.7 Trends of motorization growth in three countries 11

1.6 Motorization vs. income 121.8 Motorization vs. income - countries 131.9 It " " - cities 131.10 Percent of households owning a car vs. income 151.11 Percent of households owning a vehicle vs. HH Mot. 171.12 Proportions of motoroycles 19

1.7 1.13 Proportions of Comm.Veh. vs. car motorization 20/211.8 Buses as percentage of Comm.Veh. - countries ;231.9 Bus motorization vs. car motorization 231.10 Buses per 10,000 population 23

2 2.1 2.1/2.2 Arterial network density 26,/272.2 2.3 Expressway network density 292.3 The arterial Alpha-value 31

2.4 Daily average arterial flow vs. speed 312.4 The expressway Alpha-value 32

2.5 Daily average expressway flow vs. speed 322.5 2.6 Effect of lane number on expressway Alpha-value 332.6 Rapid transit systems in a selection of cities 34

3 3.1 3.1 Expenditure on travel vs. HH income, U.K.-1972 36/373.2 HH expenditure modal split vs. income,U.K.-1972 '37

3.2 Expenditure on travel vs. HH income, London-1972 383.3 HH expenditure on rail and bus vs. income 393.4 HH expenditure modal split between rail and bus

vs. income, U.K. & London, 1972 393.3 Public transport modal split vs. trip rate 40

3.5 " " " " vs. motorization 413.6 " " trip rate vs. motorization 433.7 Private transport modal split vs. motorization 433.8 Public transport trip rate by car trip rate vs.Mot. 443.9 Car daily traveltime vs. motorization 473.10 Car daily trip rate vs. trip time 47

3.4 Tripmakers per person & per household 483.11 " " HR vs. HH motorization 493.12 " " person vs. motorization 49

3.5 Daily traveltime of car tripmakers - U.S. 50)3.6 " " per avg. tripmaker - U.S. 503.7 " per person by population -

density, U.K., 1972 513.8 per car driver vs. income -

Washington, D.C., 1968 52

(Contd)

(cont.)

Chapter Table Figure Dasoription ______

3 3.13 Daily traveltime per person vs. populationdensity9 U.K.o 1972 53

3.14 Daily traveltime per car driver vs. HH income9Washingtong D. 1968 53

309 3.15 Car daily avg. trip distancs vs. radius 553.16 Car driver & passenger index of elasticity by

trip purpose vs. car daily trip ratep U0 S0 573a10 3a17/3 0 18 Proportions of trip purposes in the private mode

vs. car trip rate 58/59311 3.19 Perceived relative value of car mobility, U.S0 60/61

3.20 Car driver and passenger index of elasticity ofperceived value of trips vs. car trip rate 61

3.12 Travel characteristios of car tripmakers9Washingtong DC.0 1955-:1968 63

3ol3 Travel characteristics of xoadl t-aBsitg UoSo 653.14 ER by combination of mode usage, Washington &

Twin Cities 673o21 Elasticity of household weekly expandiLture on

travel4 by modeg vso Incomes U.K. 0 Iondon, J1972 6930 15 Car occupancy rates 703.16 n if It by trip purpose 703.17 Proportions of commercial vehic&e trips 72

322 it it It i vs, motorization 73

4 401 Arterial road density by motorization and popaulation density vso city size 77

_ 4T2 he city and its mobility O_

(v)

INTRODUCTION

The problem of urban transport and its interaction with the struc-

ture and size of cities may be analyzed in terms of economic demand and supply.

The purpose of this Staff Working Paper is to present in a systematic way

the available data required for this analysis. The demand for space in acity arises from the fact that the productivity of its factors and particu-

larly labor is generally higher in the cities than in rural areas. The

economies of large-scale production and th.e efficient division and diver-sification of labor can be more readily achieved in the large city. Incomesand standards of material well-being are usually higher than in the smalltown or rural areas. Consequently, there is a continuous drift of populationto the large urban centers.

The demand for mobility, which is closely related to income

levels, creates demands for cars and for road space. The car ownership pat-

tern follows fairly predictable tendencies, depending upon the size of

the household, population growth and the level and variation in incomes,

but there appears to be a saturation level of about 50 cars per 100 popu-.lation. Most cities in the developing world, however, have motor car popu-

lation ratios of less than 10 percent and are far from saturation. Broadly

speaking, a similar systematic trend applies to commercial vehicles. Bus

and taxi populations are, however, largely determined as a consequence ofindividual city rules, regulations and conditions.

The supply of road space is predominantly the responsibility of

the authorities. Variations in the quantity of road space supplied will

generate different vehicle speeds and costs for a given quantity of traffic

(measured in vehicle kilometers). An increase in road space will reduce

vehicle density and so increase speeds and reduce costs by amounts that

are roughly predictable. These values may be interpreted as the supply con-

ditions describing the "output" of urban road networks. The vehicle speed

is a surrogate for the cost per vehicle kilometer of that supply - or ineconomic terms - the "supply price."

When we consider the demand side of the urban transportationsystem, the dominant features which constrain demand are budgets of money

and time. Expenditure on transport seems to increase rather more rapidlythan income up to a maximum value of about 17 percent of income. The timespent by the average car on urban road networks seems to be remarkably stable.

There is also evidence that tripmakers (as distinct from vehicles) spend on

the average roughly the same amount of time on travel, irrespective of

trip speed. The evidence for stability of travel time is still tentativeand rests primarily on data for developed countries. When travel conditionsdeteriorate and speeds become slower, the tripmaker makes fewer trips (with

a minimum of 2.1 per day for public road transport) or travels shorter dis-tances. Travel time tends to be stable, except for those, often of very low

income, who travel longer than the average to obtain employment. Consequently,

most of the increases in speed are traded off aqainst longer trips, or, to a

small extent, for a larger number of trips. Within his travel time budget,

the tripmaker will rank trip purposes according to their order of priority and,

as transport conditions change, he will move along his priority ranking.

(vi)

One of the main determinants of the travel characteristics ofall cities is the spatial dispersion of residences and employment centers.As wealth increases, and as transport improvements take place, the citybecomes larger and trip distances increase so that the distance betweenresidences and jobs becomes larger. The trip distances tend to Increasenot proportionately to the radius, but according to the square root of theradius, and the spatial distribution of employment becomes less concentratedand more responsive to the distribution of residences.

The supply and demand conditions summarized above Indicate thatthere is an underlying unity in the spatial development of a city and itstransport characteristics. They suggest the existence of Tmportant con-straints that should be incorporated in any discussion of transport andurban structures.

(vii)

P R E F A C E

Purpose of the Paper

The principal purpose of this paper is to compare travel charac-teristics in cities of developing and developed countries, in order toidentify trends that are similar and isolate tendencies which are specificto each group. Therefore, this paper presents a search for both regularand irregular travel characteristics in a wide spectrum of cities fromdifferent parts of the world. The information being analyzed can alsoshed light on whether or not there is a basic similarity in travel charac-teristics in cities of both developing and developed countries.

The need for a better understanding of tra.vel behavior I.n cities ofdeveloping countries is real aiid urgent. These cities are expanting at arapid pace, some doubling their population in a decade, and difficultiesseem to outrun solutions. Of special concern is the transportation problem,since it reflects the dynamic life of the city and its inhabitants.

Most, if not all of the transportation studIies conducted in citiesof developing countries are based on methodologies and traffic modelswhich had been developed in highly developed countries, under the assumptionthat travel behavior is basically the same in all cities of the world.Nonetheless, not only are cities different from each other, but cities indeveloping countries are known to be different in many respects from citiesin developed countries, both in urban structure and distributions of socio-economic factors. The main purpose of this paper may, therefore, be definedas the search for the generalization of uniquieness.

The comparisons in the paper, which are mostly macro, based oninter-city rather than on intra-city anal]yses, suggest a new understandingof the interaction between travel behavior - system supply - and urbanstructure. Nonetheless, it should be realized that both the indicationsand their interpretations are only tentative at this stage, and that muchmore search and research are still needed.

It is hoped that this paper can be of assistance in the review ofurban transportation studies since it presents reasonable ranges for vari-ations in both base year observations and des-ign year forecasts, andsuggests a possible mechanism for the interaction an.d feedback processesbetween travel demand and transportation system suppl.y.

Scope of the Paper

The paper i.s divided into fouir chapters:

Chapter 1 presents the population of vehicles i.n countries and cit:ies e.ndmethods for estimating and forecasting their numbers and pro-portions, by type;

Chapter 2 shows the road system that has to acconmiodate and serve thevehicle popule.tion, and the laws that seem to govern theinteraction between the vehicles and the rc-.^ system supply;

(viii)

Ch-ter 3 introduces the hum!n aspect into the transportation system andsuggests how travel behavior may both affect and be affectedby the first two factors;

Chapter ditscusses the city and the mobility of J.ts people andpresents a few reflections on city struicture and its possiblechanges over time.

At this stage two reservations about the scope of the paper shouldbe pointed out: (i) it deals writh macro-conditions only, namely with totalaverage conditions in the ciiy, and not within its various parts; and (ii)it does not define In nuantified terms the interaction between urbanstruct-ure and travcl behavior and how each factor may both affect and beaffected by the other0 It is hoped, however, that comments and suggestionsby the readers with respect to this paper, together wiTh the on-going workon the above two additional subjects, will be integrated in a future paper.

Format of the aper

The paper is based on tables and graphical presentation of relaLion-ships, with a minimum of supporting text. The reasons for this format aretwo-fold: to keep the paler to a manageable size, and to leave some leew.ayfor other initerpretations of the same basic data. Thus, although much morecould - and possibly should - have been said on each separate subject, theohosen format is telegraphic in natuire, presenting in no more than one pageof text a short discussion and possihle interpretation of each travelphenomenonO

The AvailRble DFta for Analysis

The available data for analyses is a suoject for much improvement,Let it only be said here that the lack of international standards for theconduct nnd presentation of comprehensive urban transportation studiesmake it very difficult to carry out inter-city analyses0 Thus, whendifferent definitions may result in different sets of data even for thesame city, one can imagine the difficulties encountered while tying tocompare studies conducted by different firms or agencies, with differentprocedures and techniques, in different cities of different countries0

Thus, the smallness of the number of study cases presented in thispaper, as well as the partial information for each one of them, is theresult of (i) the limited number of study reports that were available tothe author, and (ii) the difficulties in deriving the necessary data foranalyses from the available reports0 The author would, therefore, begrateful if the readers could supply him with additional reports, and/orwith complete sets of data for the studies mentioned En this paper,

All the data are presented in the metric system.

The analyses

Because of dat-a limitations,, the analyses cover only internal travel

(ix)

made by the inhabitants of the study area during an average weekday.Furthermore, the trips are by mechanized vehicles onily. Althoughwalking trips are recognized to be an important travel mode, espe-cially in cities of the developing countries, no reliable data couldbe found on this subject.

The analyses were .imited to one independent variable at a time.This could be considered as a grave limitation but, on the-other hand,multi-variable analyses have their own dangers, such as hidden dependencebetween variables which are assumed to be independent.

Whenever a quantified relationship was derived, it was based onthe least-squares regression technique, and the equations were limited.to those that can be applied on a standard scientific hand calculator.

It should also be noted that basically three forms of relation-ships were used, after testing the data on natural, semi-logarithmicand full-logarithmic scales, as follows:-

(a) whenever a straight line became apparent on natural scales, the linearrelationship was in the form -

Y = a + bX ;

(b) A straight line on semi-logarithmic scales was defined by an exponentialfunction in the form -

Y = a ebX (ln Y=ln a + b X) ;

(c) A straight line on full logarithmic scales was defined by a powerfunction in the form - b

Y = a X (log Y = log a + b log X);

It should, however, be emphasized that the chosen fmnctions in eachcase are illustrative only, and other forms might, from the strictly statis-tical point of view, serve equally well. This is why the full set of datais detailed in each case where a quantified relationship was derived, sothat the reader may choose and test his own preferred forms.

Readers' Remarks and Suggestions

Comments and suggestions by the readers would be appreciated. They

should be addressed to:-

The DirectorTransportation & Urban ProjectsDepartment

The World Bank1818 H Street, N. W.Washington, D. C., 20433, U.S.A.

Chapter 1: The Vehicles

Tntroduction

Two kinds of' population explosions have been worrying theauthorities in many parts of the world during recent times - of peopleAnd of cars. Surprisingly enough, the two explosions seem to be reci-Procally related in mos-t cases, so that when the average size of houoe-holds decrease, people seem to adopt more cars. The most criticalsituation, howTevrer, is when the two population explosions take placesimultaneously. This usually happens in cities of developing countries,where both the birth rate and the househol-d size are high, where theadded migration of people to the cities may double a city's size in adecade, and where the increase in motorization is exceptionally high.

In a way, the vehicles may be regarded as a separate populationin a city, witl their own space requirements, activi.ties and patterns.IFurthermore, this population is not homogenous, but is composed ofdifferent types, each with its own specific trends of growth and inter-act5ions with the environment.

This chapter discusses some aspects of the vehicle population,such as its trends of growth in absolute and relative terms, and theproportions of the different types between themselves and when relatedto people. The comparisons cover countries and citiesin the developedand developing areas in order to see whether all show the same trendsand characteristics.

Furthermore, some of the current vehicle forecasting techniqueswill be mentioned, in order to suggest that they may have to be reviewed,especially in light of the present economic difficulties and the increasingreluctance of the authorities all over the world to allow the unrestrainedgrowth of private cars in cities.

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1 01 Increase of Car Population

The increase in the world's car population durinig 1950-1972 wasphenomenal. Table 101 and Figure 101 present the trends of increase bymajor parts of the world, where the increase seems to accelerate withtime0 (_, 2).

The increase in the total number of cars follows a classicalexponential growth curve, namely where the rate of growth is propor-tional to the stage of growth. Such a growth is a well-known phenomenonin many fields0 For instance, in economics it represents the increaseof an investment when interest is compounded continuously over time0

The crucial question, therefore, is whether the exponentialgrowth of car population should be expected to continue indefinitely0Namely, if the number of cars increased by more than four-fold duringthe last 22 years, from 51 to 220 million cars, should we expect it toincrease to over 1,300 million by the turn of this century, and continueto accelerate with no slacking?

Logically, it cannot happen for two principal reasonsg First, thehnman population itself cannot increase indefinitely along an exponentialcurve and, therefore, there must be some critical point after which theincrease in the number of cars must slow down as well; and secondly, thegrowing difficulties with energy sources and prices, economic slow-downsand environmental deterioration are expected to affect the growth of thecar population0 Indeed, a slow-down has already been noted since the energycrisis of 1973 and the following economic sliunp.

Referring back to Figure lol, it should be noted that the sixcontinents of the world have been ranced 'oy their absolute number ofcars0 Foremost is North (and Central) Amerjca, followed by DEropD,Asia, Oceania, Southl America and Africa.

Already at this stage a striking featuire becomes :ipparent-although the first two, North America And Europe, incllide only 23percent of the world's total. population, they have otrer {RN pernent of8ll cars0 The ratio is even more remarlable if only the UOS. isconsidered: less than 9 percent of tne world's pupulation owns nearlv)0 percent of all cars0 Tt may, therefore, be inferred that the ratmo

between the number of cars and population mRy be of more significancethan the absolute number of cars0 Inldeed, this ratio,V usually referredto as Motorization, is recornized to 'he one of the rnost imporfRnt fact.orsin transportation analyses and the basis for forenasting travel character-istics to a future date0 Motor i,ation will, therefoTre, be discussed inirre detail in the following sections0

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240

Af ricaSouth AmericaOceaniaAsia

200

Europe

0

.H

North America~4100

1950 1960 1970-2Y e a r

aigure 1.1: The World's Car Population, Cumulative

Table 1.1: The World's Car Population (Million)

Continent 1950 1955 1960 1965 1970 1972

North & Central-America 42.610 55.780 66.860 81.770 97.870 106.955

Europe + USSR 5.440 12.980 23.560 45,940 69.930 80.016Asia .500 .920 1,730 4.350 12.250 16,497Oceania 1.010 1.890 2.470 3.670 4.880 5.585South America .760 1.150 1.650 3.010 4.590 6.528Africa .800 1.250 1.890 2.500 3.440 3.672

Total 51.120 73.970 98.160 141.240 192.960 219.253

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1.2 Motorization

Motorization is defined in this paper as either the number of carsper 100 population or the number of cars per household. The first defi-nition was used to express the world's motorization in Figure 1.2 (Table 1.2.(Ta.ble 1.2 was derived from Fig. 1 in Ref. 1)o

The slopes of the lines in Figure 1.2 represent the rate of moto-rization increase in the major parts of the world during 1950-1972. AlthoughFigure 1.2 shows very general trends, averaged for many countries, it alreadyindicates a principal characteristic of motorization: when motorization isvery low, as is shown by the line representing 'Rest of the World', itsincrease over time is slow; when it passes a motorization level of aboutcars per 100 population it starts to accelerate rapidly, as indicated

by the line representing 'Europe'; this rate seems to remain stable overa wide range, as seen by the line for 'Oceania'; however, after a highlevel of motorization is reached, say 25-27, a slackening in the rate ofincrease is apparent, as expressed by the line representing 'North America'.Thus, combining the three trends together would result in a flat 'S'-shapedcurve representing the increase of motorization over time, with an expectedsaturation level somewhere near 50 cars per 100 population, or one car per2 persons. Tndeed, this general trend is currently applied as one of themain procedures for forecasting motorization to a future date, as will beexplained later on.

At this stage, however, it becomes apparent that a procedure bywhich past trends are projected mechanically into the future could bedangerous since many additional and crucial factors might be hidden withinthe past trends without being recognized as such; for instance, the economiclevel. Indeed, the recent economic difficulties in many countries in theworld have already been reflected in a significant slow-down in the increaseof motorization, and many millionsof cars are awaiting buyers. In otherwords, motorizatLion cannot, and should not,be isolated from the overalleconomic background of a country.

Referring back to Figure 1.2 it should also be noted that theincrease in motorization over time is much slower than the increase inthe absolute number of cars .(as expressed in Figure 1.1). The reasonfor this difference is the corresponding increase in population. Thus,a doubling in the number of cars can take place by a doubling of popu-lation with no increase in motorization;.by a doubling of motorizationwith no increase in population; or by increases in both population andmotorization in various proportions between the above two extreme cases.Hence, the exponential increase in the number of cars, as seen in Figure1.1, is not an absolute law of nature, and changes may be expected ifchanges will take place in the growth rates of population and motorization.

Up to this stage only three simple factors have been mentioned,namely the number of cars and how it can be affected by both populationand motorization. In the following sections more attention will be givento motorization and some of its characteristics will be discussed, espe-cially with respect to procedures for estimating it for a future date,

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35

Nor h America

30.r4a

-P ~~~~~~~~~Oceani

co0

0

0

20

C) ~~~~~~~~~~~Europe

0

04)3

o10

World Average

Rest of World

1950 1960 1970-2Y e a r s

Figure 1.2: The World's Car Motorization

Table 1.2: The World's Car Motorization

Continent 1950 1955 1960 1965 1970 1972

North & CentralAmerioa 19.7 23.5 25.0 28.1 30.6 32.1Oceania 8.2 13.4 16.4 21.0 25.4 28.0Europe 1.2 3.0 5.4 10.2 15.0 16.8World Average 2.7 3.5 4.0 5.4 6.9 8.0Rest of World 0.3 0.5 0.6 o.7 1.1 1.3

__ - _ . - _ _a __ -__

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1.3 Estimating the Saturation Level of Motorization - Countries

The 'S'-shaped curve of motorization mentioned in the previous

section can be expressed by the so-called 'logistic' function, which

can be solved if three basic parameters are given: the current actual

level of motorization, the current actual percentage rate of motorization

increase, and the future saturation level of motorization. (for a

complete explanation see, for example, Ref. 3).

While the first two parameters can be derived from available data,

the third one has to be estimated. One way of estimating the saturation

level of motorization, expected to take place in the future, is from

extrapolating past trends of changes in the rates of motorization increases.

More specifically, if the rates of motorization increase are plotted

against motorization, a clear trend emerges, where the rate of change

decreases with an increase in motorization.

This trend can be seen in Figure 1.3, where the average yearly

percentage increasesin motorization during 1965-1970 in 22 selected

countries are related to the average motorization level during this

period. (Table 1.3 and Figure 1.3 are based on Fig. A-1.9 of Ref. 3).

The interpretation of Figure 1.3 is thet the rate of increase in

motorization decreases with an increase in motorization itself, until

the rate reaches zero at a certain motorization. Thus, this certain

motorization can then be regarded as the saturation level, since no

further increase in it can be expected.

Aftey,1cancelling three extreme cases, out of 22, namely points

3, 4 and 22,- and deriving the average trend by least-squares regression

analysis, it becomes apparent that the saturation level of motorization

for the 19 countries is expected to be about 40.7 cars per 100 population,

or 1 car per 2.5 persons.

The point to note at this stage is that when the saturation level

is estimated by the above procedure, the full range of the 'S'-shaped

curve can then be defined, thus enabling to project past and present

trends of motorization into the future, within the constraining factor

of saturation. An example bf such a projection is presented in Figure

1.6, but first the above analysis will be extended to cities as well, as

presented in the next section.

1/ The reason for cancelling the two extreme cases, points 3 & 4, from the

regression is that they seem to result from unique local conditions

which are not representative of the main range of the majority of countries.

The reason for cancelling point 22 is, however, entirely different - when

past trends are checked progressively for the same countries, it seems that

the average line changes gradually with time into a flat curve, thus

extending the point of intersection with the x-axis further away to the right.

Consequently,the saturation level for regions or cities in the U. S. is

further away to the right, beyond the 41-mark.

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40

33o Y= 13.579 -0.334X

~30

o @3

20

-_~ 100.11

10 2 1 - 20 .12.15

C 9 13%5 17114 @

016 -2

0 21 -... 22

0 -II

0 10 20 30 40 50Motorization , Cars per 100 population

agure 1.3: Motorization Rate of Changevs. Motorization in SelectedCountries , 1965 - 1970

Table 1.3: Motorization Rate of' Change vs. M-otorization - Countries,1965 - 1970

No. Country Change, % Motorization

1 Czechoslovakia 14.3 3.72 Israel 9.0 3.83 Spain 23.7 4.04 Japan 31.0 4.35 Venezuela 9.3 4.76 Portugal 13.7 4.87 Argentina 9.6 5.48 Finland 9.3 12.29 Austria 8.5 13.510 Italy 12.8 13.811 Netherlands 12.2 15.012 Norway 9.2 15.613 Belgium 7.8 17.714 Switzerland 7.4 18.515 Denmark 9.2 18.816 U.K. 5.0 19.017 W. Germany 7.5 20.018 France 6.1 21.519 Sweden 4.2 25.320 Australia 3.7 28.321 Canada 2.2 28.522 U.S. 2.0 41.0

104 Estimating the Saturation Level of Notorization Gitias

Table 1 0h and Figure 104 present the rate of motorc-iration chaiagevs0 motorization for a selection of cities (i)0

Applying again the regression analysis to the above data resultsin a saturation level of 402.0 Surprisingly encagh, tGhis -value is vse-.ysimilar, practically identical, with the satsuration 13-931 fo7'~ fouwc.rSiesderived in the previous section, namely 40.2 vs0 4O0 r es;oectively0 A'>though the close similarity between the two estimates should be considleredas coincidental, it does indicate a consistent saturatvion level o. about40 cars per 100 populatiom for both countries and citiesO

Referring back to Figure 1 h it becomes noticeable that there is apronounced scatter of cities, especially at low levfels o2i rlotox4;s>ation 0

One possible explanation for this scatter is the def-'iiti-on of mnotori-,ationwhich has been used until now,, namely the number o. cars per iCO ponp'`ationrather than per household0 Since citGy average househo'.d size ma.y vary fromless than 3 persons to over 7 persons, it becomes evident t.at motoTriZationmay vary by a factor of two, depending on whether 1iA is based on po7Jlationor households when used in in'Ger°city comparisons 0

To test whether motor-Lzation by hou,sehold -i 3 a 9-tei ; te2ioufor deriving the saturation level than motorization by population, thesame exercise as presenlted in Figure 1 0 was repeated rorz aotorizatficn: byhouseholds, as shown in Figure 1.5. Indeed, the scatter is markedl-ylower and the relationship significantly better0

From the above exrnple it may be i-,.erred that the expecte.dsaturation level oL motorization by households is about 1.1 cars pS3household, regardless of household size. It should be poinbed out atthis stage that household motorization is considered as lo-re rep:eese-.ta-~tive of travel behavior because it represents the avai7ability of a carto a closely united group of people, rather than to 100 independrntpersons0 The analyses in this paper wfll cover both types of i20otorization.according to the available data.

Table 10h44 Motorization Rate of Change vs0 Notoization GC-l;ios.~ ~ -

No.fl City Population1H!M Size Year lHouseholds Cars ot-,. Hot0 .Changeo; P(tooo) pulati>Pcp. %_~~~~(00 ._ __ _ __Q _ _op==_ =

San Jose 43.5 5.49 1973 79,235 20 18804 h 8 0 26' l0o92 Bangkok 3,090 5080 1972 532,760 154;500' 5s0 029 l2cO3 Kuala Lumpur 755 5082 1973 129,725 39,260l 5o2 0°30l 1l.3

Athens 2 416 3049 !i 1962 4 692,260 1.7,380, 601 O.21,I 2 8H Singapore 2,110 n.l6 1972 408 9915 15h4;0301 7.3 0.38 6.76 Caracas 2,277 6OO0 1966 379,500 207,210 9ol Oo5 8037 London 107 h47 2o87 1962 3,674,910 2,341,430 22o2 o064' 5.28 Paris 8,44h8 2.70 1965 3,128,890 209 5,1 00): 24A<8 0.671 6.59 Washington 757 2 .9 6,610968 2 2696io 239,210, 3106 0.93. 2 .0

-9-

16 ,

^ 14 Y= 12.46 -0.31X

; ~ ~~~ *412 2 2

, 3

o 10 43R 06

MtraisCsp 107ut8

0 4

0 ~ ~ ~ ~ ~ ~~ 9.

0 1.0 2 0 3. 0 4 0 510

Motorization, Cars per 100 Population

Figure 1.4: Rate of' Motorization Changevs. Motorization byor Personsin Selected Cities

1 4 Y= 14.9 7-13.96 X

.41 12 -02

C.) ~~~3o 10

tq8 06

o 05 @8436

: -8 ~~~~~70

0 4

0 .2 .4 .6 .8 1.0 1.2

Motorization, Cars per Household

Figure 1.53 Rate of Motorization Changevs. Motorization by Householdsin Seleoted Cities

10=

1.5 ___ Forecastin Motorization Levels

An actual example of a recent motorization forecast in the U.K. ispresented in Figure 1i6 (Based on Fig0 1 of Ref. 3). The figure shows theflat tS'-shaped curve, where the past increases of motorization are expectedto flatten out and approach asymptotically the satuz,ation level. The accuracyof a forecast is for the future to tell. But uhat is unique in the aboveexample is the attempt to integrate economi-c factors, such as income andcost of travel, in the logisti.c function, thus showing three alternativesaturation levels of 43, 44 and 45 cars per 100 population0

At this stage, however, the previous remark about the possible shiftof the saturation level with increase in iaotorization, should be mentionedagain0 This problem may be best explained in graphical form, as shoam inFigure 107 (Table 1 5). As can be seen, the actual motorization trendsduring 19501973 in three countries have been combined in one diagram,where a continuouis trend is apparent. When estimating the saturationlevel for Israel in 1965, a level of less than 20 was indicRted, but in1975 the expected future saturation is already much higher. The samehappened in the U.K., where a previous forecast, based o±i past trendsuntil 1960, indicated a saturation leval of 40 (5), but current estimateshave already raised it to three alternatives, 437h4!-45. Tn the Uo 0S., onthe other hand, the expected saturation level is about 50.

Table 10 50 Trends of Motorization Growth in Three Countries

Te r X M o t o r i z a t i o n M o t o r i z a t i o nYear Year

Israel IUK. 1 U.S. Israel j UOEO UOSO

1951 0.70 o 1 963 2.Oi 14-1 36-71952 51 28a0 1964 2-50 15-8 37-71953 0°74 5-8 29.3 -1965 2.90 17.0 39.01954 0O78 6.1 302 1966 3 .,' 18.2 4001955 0o82 7o2 31o9 1967 3q58 19-5 40.81956 o088 7°9 32.6 1968 3X86 20-3 4201957 0.94 8.4 32.8 1969 4050 2100 43-01958 9oO 32.9 1970 4o93 214 44.01959 lo01 9.8 33.6 1971 5°53 220O 45-01960 1l15 110 34.3 1972 6.39 23.0 46.41961 1-37 11.8 34.9 1973 7U26 o1962 L68 12.8 _ 1 =7

It way, therefore, be inferred that tcy-; P.1 , jor factors caninfluence the estimation. of the motori,at.13:s3, o:2 o.f theestimation is based on local past trends only, iA '" resuLt i ,., under-estimation of both the saturatiox level and the yE-.v moto J ti.on forecasts. Coonversely, recent experience has s.:o.tly z..icat;d that escono cfactors miust ba considered as vell, since dsr-xc:C s1ov.ed in suchfactors zs Gross Mational ;c':oduct (GOPP) c:- diespu e ; income may rewiltin overestimations of botb. the baturatai.- 1el evat & *'ar, t7' ot)'-,zaVtion forecasts0 Therefore,q the efi-ac, Is :. .'-' :uatov zaiA 7be ovaluated next,

50

0

ld 40

00

$4 30CD

.. 20

o

R1 0.4

190 0 1960 1970 1980 1990 2000 2010

Y e a r

Figure 1.6s Car Motorization Forecastin the U.K. , 1975 - 2010

50

40 .

.U. S.0 3 0~~~~~~~~~~~~~

N ~~~~~~~~~1950 60 7 0

2 20 *

20 U.K.

O0 t I2* srael60 7u

1950 60 70Y e a r

F-igure 1.7: Trends of Motorization

Growth in Three Countries

1951 - 197 2.

- 12

106 The Effects of Income on Motorization

When countries are ranked by average income per person, it becomesapparent that motorization and income are closely related. Figure 108presents a selection of 25 countries, where the average annual income percapita is based on the U.S. dollar and motorization is expressed as carsper 100 population (3). Indeed, the general trend is quite clear:motorization seems to increase with income. There is9 of course9 anoticeable dispersion of points in the diagram, but such a dispersion is tobe expected when considering the many distortions that are unavoidable whentrying to evaluate different and diversified countries by a single commondenominator such as income. For instance9 two countries may have the sameaverage income per person in U.S. dollars, based on the official rate ofexchange9 although the actual value of these incomes may differ to a largeextent: income per capita will depend on household size; the distr-ibutionof incomes within the populations may be different. disposable incomes maybe entirely different because of different leve'Ls of local taxation9 or theduties and taxes on the purchase and operation of cars may differ by a factorof 3 between countries. Thus, although income seems to be a strong indicatorfor motorization, many more factors have to be considered as well.

Figure 109 presents the same relationship9 of motorization vs0income, for a selection of cities (4) 0 Once again, a clear direct dependenceof motorization on income becomes apparent0

It should be noted at this stage that both countries and cities seemto be distributed along a continuous linear relationship9 with no cleardividing line or point of inflection between developing and developed countries0Thus, the data presented to this point do not indicate a specific thresholdwhich a 'developing' country, or a city, may cross over to become a 'developedione0 The attention of the reader is called to this subject at this pointbecause one of the aims of the following analyses was to find whether theremight be some specific travel characteristic in the so-called 'developing'countries that might change after the country crosses an invisible threshold0If such characteristics do exist, they may be of prime importance whenprojecting base year trends into the far future, since they would then beexpected to change significantly between the two periods in time0

Table 16: Motorization vs0 Income in Selected Cities, 1970

No. City Motorization | Annual Income (8)

1 San Jose 408 4302 Abidjan 7.6 5003 Bangkok 5-0 5254 Kuala Lumpur 5.2 6605 Bogota 2.2 7606 Singapore 7-3 gl,oo7 Mexico City 7.8 192258 Caracas 9.1 196009 London 22.2 2955010 Paris 24.8 3D53011 Washingtonp D.C. 31.6 59390

-13-

1 Portugal 50 13 Finland2 Spain 14 New Zealand3 Argentina 25 15 Netherlands4 Greece 40 16 Belgium5 Ireland . 17 Norway6 Italy 0 18 France7 Israel R 30 14 *19 *23 19 Australia8 Libya @d8 *24 20 W. Germany9 Japan * 20&*22 21 Switzerl.and

o *12 *16 -W21 2-emr10 Austria 4 20 60 *15 17 22 Denmark11 Puerto Rico ° *I) 23 Canada12 U.K. * *13 24 Sweden

10 25 U.S.0 20*3 09

1 7z 0 8

04

0 1 2 3 4 5 6Annual Income per Capita , $ (ooo)

Figure 1.8: Motorization vs. Income in

Selected Countries , 1970

1 San Jose 50 ,2 Abidjan3 Bangkok4 Kuala Lumpur 40

5 Bogota6 Singapore 07 Mexico City .p 30

Cd8 Caracas d 0109 London h 09

10 Paris . 2011 Washington, D.C. C

10 -02 6.007

00 1 2 3 4 5 6

Annual Income per Capita , $ (ooo)

Figure 1.9; Motorization vs. Income inSeleoted Cities , 1970

o1h4

107 Effects of Income on Motorization within Cities

In The previous section income was shown to have an effect onmotorization on both national and city levels. This section will dealwith the effect of income on motorization within cities, on the house-hold level.

Appendix 1 and Figure lolO show the percentage of householdsowning a car vs. their income level. The six cases have been rankedby their motorization, where the motorization follows the city name(6 - 11). It should also be noted that although the average income inabsolute terms is different in each case9 the range of incomes, fromzero to maximum, have been presented in the same scaloO

Figure lolO shows the expected trend, where motorization isstronglyr related to income levels. Of particular interest, however, isthe shape of the curves, which reflect the avexage motorization in thecity. This characteristic is best shown in the case of New York, wTherethe two curves represent conditions in the city proper and the-regionoutside the city0

Thus, a gradual shift in the curves may be noticed, from Tel Avivthrough KuA.la Lumpur, Rotterdam, London, New York to "All the U.S030 (One

slight divergence, however, is noticeable in KoLo, where the curve seemsto rise too steeply for the local average income or motorization0 Noexplanation for this difference is given in the study report)0 It maybe inferred from the general trend that both the absolute income and itslocal relative value (such as disposable income vs. prices) have a markedeffect on motorization0

The subject of income and its effects on travel behavior will befurther discussed later on, but the attention of the reader is drawm atthis stage to another significant phenomenon, shown in the diagram forNew York: urban structure, and especially population density, seem tohave an additional effect on motorization0 Thus, households in the sameincome bracket have different motorization levels depeaudin.g on whetherthey live in New York City or outside it0

The reasons for the above phenomenon are maDy and varied0 Forinstance, higher population densities reduce the need for motorizedtrips because of shorter distances between potential origins and des-tinations; higher population densities reduce the avaLlability of landfor roads, thius increasing traffic congestion and pa.Lkin- difficultiesand, as a result, also discourage the increase in motorizattion5 higherpopulation densities in cities above a ceLtain size v.aso encourage andjustify the introdaction of rapid transit, thus supplying a certainsubstitute Lor the personal car0

Although these subjects will be discussed --ater aa, it may beconcluded even at this early stage that income, though.a a strcng indi-cator for motorization, is not the only one foY arnlyzhig base easxdata or projecting them into the future.

- 15 -

100 100

0

o o:

o o

0~~~~~~4)0

0) / r~~~~~el Aviv -5.8 /Kuoula Lumpur-7.2p1 ~~~~~~~~~~~~0

1 2 3 4 5 6 7 8 12 3 4 567 8 9

100 100

0

o Rotterdam - 13.1 London - 14.1

0 I I0 I l I I I1 2 3 4 5 6 1 2 3 4 5 6

100 100

m c~~igr l.lOs Pecn fHushlsOnn

a Car vs. In¢ome GroupCH~~~~~~~~~Ct

0~~~~~~~~~~~~~~~

*A ~~~New York - All U.S. -42.9

0 ii.j i a ILI0

1 23 4 56 7 8 9 12 3 45 6 7 8

Income Group Income Group

Figure 1.l0i Percent of' Househol.ds Owin3ng

a Car vs. Income Group

-16=

1.8 Households Ovrning Vehcles vs. J4otorization

An additional important relatitonship is the percent,ge of house-holds at each given car motoriza.:;tilon level not owning cars and owninga motorcycle or 1-2-3 cars.

Appendix 2 and Figure 1.11 present these distributions for 6 cases,-including 1 cities and 2 countries. The dqtq were derived by consideringthe proportions of households owning and not olvrning vehicles at each incomegroup, while the motorization level is the av,erage for that group. Thereason for this procedure is to keel) a seque;itial order in thn varionsrelationships presented until now so that after deriving the averagemotorization level by income, as presented in thie. orevious section, thesame group is furthier stratified by households owming 0-1-2-3 cars ormotorcycles. (6-12)

In the first two cities, where moLorization levrels are stillrelatively low, motorcycles Are included in thc distributions, whilethey are lacking in the remaining cases. Tt should also be noted. thatthe motorization scale in the last case is different from all the rest.

When comparing the first two ciii es, Kuala TLumpiur and .iig:.pore,it becomes evident that althoLgh -the general trends are simiLlr, thereare significant differences in the proportions of households at eachmotorization level. The same applies to the comparisons of 'Ill othercases without motorcycles.

But the most conspicuous reslult is the relatively high percentageof households owning no car even at high average income and motorizationlevels. Although this resuilt is already evident in Figure 1l10, it becomesmore prominent in Figure 1lol, For instance, even at an average motorizationlevel of 1 car per household in Singapore, still over 21 percent of hou.se-holds do not own a vehicle, not even a motorcycle, while over 25 percent ofhouseholds own over 2 cars. The same trend is apparent in all other cases aswell. It may, therefore, be inferred again that income is not the onlyfactor which decides whether a household will own or not own a car, andthat many other factors have to be considered as well, such as populationdensity, availability of public transport and of ctrivi.ng licenses.

The above result should be borne in mind -Then later discussingtravel characteristics and modal splits, since it might come as a surpr_sethat even households owning a car still uise - and appreci.ably so publi.ctransport, while households owning no car conduct a relatively highlproportion of their daily trips by car.

Another point to remember is the danger of using average valuesof imotorization, since even when the average motor ization is 1 car perhousehold, about 20 percent of households do not own a car, while another25 percent ow.n two cars.

-17 -

100 , . . , .100 . . . . , Kuala Lumpur Singapore

0fI 1.0 1 .4 1968

02 0 0

0

V4 m ~~~~~~~~~~~~~~~~~~~2a) o M/C

0 010 1.0 1.4 0 1.0 1.4

X !O' ' ' ' ''100 . . , . .

RoiterdamLodno 1866 16

A 0 0a)

0 1.0 1.4 0 1.0 1.4

loo~ ~igr 1.1 eretgeo oueodU. in K.ioe vs. S.se

ri-I ~ ~ ~ ~ ~ ~ od ooizto

192 297

02~~~~~~~~~~~~~~~~~~~~~~~~

02

0 00 1.0 1.4 0 1.0 1.0

0 1870~~~~~od 4toiato

1.9 Motorcycles

While all attention up to this point has been directed to themotorization of cars, it was indicated in the last section that motor-cycles may constitute a substantial proportion of vehicles at lowmotorization levels0 This section will, therefore, deal in more detailwith motorcycles (hence-forth M/c for short). Figux-e 1012 (Appendix 3)presents 6 diagrams, showing different aspects of this subject,

Figure 112-1 describes the increase in car and M/e, motoriza;;ionin Israel during 1951730 The M/c absolute motorization kept its grounduntil a car motorization of sbout 365 was reached, after which it begarto decline0 (13)

A better representation of the above relationship is presentedin Figure 112-2, whlere the M/c motorization is shown as percentage ofcar motorization and plotted against the latter. The relationship seemsto indicate a consistent trend0

Figure 112-3 presents the same relationship as above 'out nowJfor two cities, Bangkok and Tel Aviv, where again the same general trendseems to hold true0 (iii, 6).

Figure 10124 shows the percentage chnnge of N/c motorization vs.car motorization for Bangkok, Tel Aviv and Israel0 It becomes apparentthat a car motorization of about 305 may be considered as a criticalpoint;- before it is reached, M/c motorization tends to increase togetherwith increases in car motorization, but after this critical point ispassed, the M/c motorization seems to decline and lose to the car moto-rization.

While the above examples are based on time-series, namely changesover time, Figures 1.12-4 & 6 present cross-sectional data for KualaLumpur(1973) and Tel Aviv(1965), where the proportions of householdsowning a K/c or a car are stratified by income0 It becomes evident thata M/c is a substitute for a 3ar at low income levels, and that it isbeing replaced by a. car when 'income increases0 Thus, there seems to bea kind of interchangeability between the two. Of particular interestis the similarity between the two diagrams, although income grouipingsand levels in the two cities, as well as total average motorizationlevels, are entirely different.

It may, therefore, be concluded that the above relationships,and similar ones in other cities, may be used for estimating 14/c moto-rization in a city in a future date after the car iaotorization forecasthas been established0 It should, however, be noted that th> velationshipdeveloped for a city is more appropriate to apply than ths~ one developedfor a country, since motorcycles tend to concentrate in cities, wheretravel distances are relatively short0

-19 -

210 100

-0.265X*Totol Y= 107.54e

8 -M/ a 80 ie

50 670 78 0 2 4 6 8 1

0

Ca and M/c Motorization by Years Proportons of Motorcycl60 -0 0

60 ~ ~~~~ * * )Bnkk 2 rtcil OBigo

035

Me.80 *> / * 2,8 *.0 * \ -c60 \/ Cos 0 \/

2 20 -0* 0.000 ** ~~~~Isra'el Israel

0- 0

50 60 70 78 0 2 4 6 8 10Year Motorization -

Car ando n/c Motorization by Years Proportions of Motorcyclesto Cars vs. Car Motorization

3 460I T

'0~~~~~~~~2 (E) Bangkok ~~~Critical G Bangkok

55-~~' *Tel Aviv * Motorization * Israelo 'a'a 0 +10 . ~~~~~~~~~~~~~~~ *IelAviv

C50 '

o 'a~~~~~ 0 I

40 - ~ 0\. ~o ~ ~ ~ ~~ -100

yto 7771* e -20 0

351 I I I IaI

1 2 '3 4 5 6 7 8 0 1 2 3 4 5 6 7

Motorization Motorization

Proportions of Motorcycles Changes in N/c vs. Car Motorization.

5 ~~~~~~~~6100 - 10

Cars n

~80 * / ~80

6~0 w60rd ~ 0 Kuala Lumpur 'd Tel Aviv

40 -40 -

20/ 0 /2

M.C. M.C.

123 4 567 8 9 10 1 23 45 6 7 8

Income Group Income Group

Proportions of Households owning Proportions of Households owning

a Car and a M/c vs. Income a Car and a N/c vs. Income

Figure 1.12: Proportions of M4otorcycles

- 20 -

1.10 Commercial Vehicles

The previous sections presented several approaches and methods

for estimating future car and motorcycle motorizations. In this section

commercial vehicles will be discussed and methods for their estimation

will be suggested.

The group of commercial vehicles (henceforth - C/v) is a difficult

one to-assess since it may cover a variety of vehicle types, from small

three-wheel delivery vans up to large articulated trucks. Furthermore,

when motorization is low and the price of cars relatively high, many buy

and use C/vs as private cars. Indeed, in at least two studies, Tel Aviv

(1965) and Helsinki (1966), C/vs had to be divided into two basic groups:

family-attached and non-family-attached C/vs, and the former group was then

added to that of private cars.

Table 1.7 and Figure 1.13 present data on C/vs in a selection of

cities, covering a wide range of motorization. It is apparent that the

C/vs, as percentage of cars, can be related to the level of motorization

with a high reliability. The relationship can be expressed by the

equation:

C/v * 13.14 (1.1)

(% of cars) log M - 0.284

where M is the motorization.

Another possible relationship is the number of C/vs per 100 popu-

lation, as presented in Table 1.7. Although the range of values in this

case is relatively small, no strong relationship could be defined for the

available data. Therefore, it is suggested to apply Eq. (1.1) for estimating

the number of C/vs, while referring to Table 1.7 for confirmation with

respect to the C/v rate per 100 population.

Table 1.7: Proportions of Commercial Vehicles vs. Car Motorization

No. City Year Population Cars Mot. Comm. Veh. C.V. as C.V.Iper% of Cars 100 Pop.

1 Athens 1962 1,900,000 39,000 2.1 23,000 59.0 1.21

2 San Jose 1973 656,670 30,466 4.6 14,806 48.6 2.25

3 Tel Aviv 1965 817,000 39,643 4.9 13,070 33.0 1.60

4 Singapore 1972 2,150,000 136,440 6.3 31,700 23.2 1.47

5 Kuala Lump. 1973 912,490 65,436 7.2 15,400 23.5 1.69

6 Caracas 1971 2,100,000 200,000 9-5 50,000 25.0 2.38

7 Merseyside 1966 1,419,450 168,552 11.9 29,261 17.4 2.06

8 Hull 1967 344,890 43,183 12.5 9,469 21.9 2.75

9 Helsinki 1966 637,393 81,488 12.8 13,030 16.0 2.04

10 London 1961 8,826,620 1,249,453 14.1 190,754 15.3 2.16

11 West Midi. 1962 2,529,010 388,353 15.4 53,956 13.9 2.13

12 Cardiff 1967 322,616 56,283 17.4 7,130 12.7 2.21

13 Copenhagen 1967 1,707,000 342,950 20.1 47,240 13.8 2.77

14 Pittsburgh 1958 1,469,375 395,321 26.9 41,903 10.6 2.85

15 Baltimore 1962 1,607,980 437,540 27.2 47,938 11.0 2.98

16 Chicago 1956 5,169,663 1,461,646 28.3 130,000 8.9 2-51

17 Knoxville 1962 241,810 78,374 32.4 9,575 12.2 3.96

18 Baton Rouge 1965 245,076 86,116 35.2 9,101 10.6 3.71

- 21 -

70

60 -1

13.14'log X -0.284

029 CH50 -

fl-I~~~~~~~~~~~~

0

4+' 40v 4 0 3

0

0 ~ ~ ~ ~~~~0cdi 30 -

20

0

| 20 \@'s9@~~~09 10

10 *11 15 *820

0 , X . *

0 10 20 30 .40

Motori zation

Figure 1.13: Eroportions of CommercialVehicles vs. Car Motorization

-22-

1oll Buses and Taxis

Inter-city comparisons did not suggest strong relationships.-rith respect to buses in any conclusive way. The reasons for this result

are mqny and varied. For instance, in some cities buses may be the onlyregulated public transport mode while in other cities buses may be comple-

mented by rapid transit and trams. Moreover, in many cities buses are

also being complemented by shared taxis, either on regulated and official

routes or on a 'pirate' basiso To mention just one example: in Singanore

(1968) there were about 1,200 buses, 200 school buses, 3,800 licensedtaxis and 3,800 'piratet taxis. After only h years, in 1972, when piratetaxis were eliminated, the inventory incluided about 2,000 buses, 1500school buses, and 4,900 licensed taxis. Thus, major changes and shifts

An the number and proportions of public transport vehicles took placein just 4 years, with no clear and quantified relationship.(12)

Nonetheless, and in order to give some indication of the propor-

tion of buses to population and vehicles, three tables are presented. The

first and second tables average the data by continents ( 1l), while the

third table presents data for a selection of cities (16)o It should,

however, be noted that while the first two tables are based on official

registration records of vehicles, the third table is based on reports by

public transport operators in cities and, therefore, may present only

part of the operators and inventory. Furthermore, shared taxis and

similar modes are not represented at all0

It can be inferred from the three tables that although the growth

of the bus population shows some general relationships when aggregated by

continent, it tends to fluctuate when inter-city comparisons are made0It may, therefore, be concluded that the estimation of buses, or any otherpublic transport mode, slhould be based on policy considerations rather

than on quantified relationships. Moreover, the future number and propor-

tions of different public transport modes, such as buses, trams and rapid

transit9 should be strongly affected by the planning alternatives since

the various modes would interact with each other in different p.eoportionsdepending on the alternative0 Thus, no clear past trends or specific

guidelines for forecasts are possible at this stage0

The same problems - and conclusions also apply whe-n anal-yzingtaxis. Nonetheless, and as will be shown later, some measure of the numberof taxi vehicle trips may be defined and, therefore, can be u.d forestimating the amount of vehicular travel in a city.

- 23 -

Table 1.8: Buses as Percentage of Commercial Vehicles - Continents

Continent 1950 1955 1960 1965 1970

Africa 6.4 6.4 6.4 6.4 8.0North & Cent. America 2.5 2.4. 2.2 2.1 2.0South America 6.6 6.6 6.6 6.6 6.6Asia 11.4 10.6 9.6 6.4 5.4Europe 7.3 5.9 4.6 4.2 3.9Oceania 2.5 2.3 2.1 1.9 1.7

Table 1.9: Bus M-lotorization vs. Car IMotorization

Continent Car Mot. Bus Mot. Bus Mot. as %of Car Mot.

Africa 0.77 0.020 2.60North & Cent. America 28.14 0.130 0.46South America 1.69. 0.100 5.79Asia 0.21 0.016 7.72Europe 6.03 0.300 5.05Oceania 20.72 0.024 0.12

Table 1.10: Buses per 10,000 Population in Selected Cities

City Population Buses Bus-Ratio City Population Buses Bus-Ratio(000) -_ __- (oo0)

Addis Ababa 500 135 2-7 Madrid 2j641 1,073 4.1Athens 2,000 1,782 0.9 Milano 1,721 1,241 7.2Atlanta 1,211 472 3.9 Montreal 1,813 2,000 11.0Belfast 600 456 7.6 Moscow 6,400 4,081 6..4Bombay 4,500 1,338 3.0 New York 7,780 2,310 3.0Berlin W. 2,200 1,429 6.5 Osaka 3,160 1,846 5.8Berlin E. 1,100 270 2.4 Paris 7,560 3,508 4.6Casablanca 1,300 169 1.3 Philadelph. 2,715 1,342 4.9Chicago 3,534 2,799 7T.9 Rotterdamn 900 305 3.4Clevelend 1,750 959 5-5 Tel Aviv 750 775 10.3Detroit 2,096 1,163 5.5 Tokyo 10,900 1,816 1.7Istanbul 2,000 498 2.5 Warsaw. 1,253 1,002 8.0Leningrad 3,300 2,148 6.5 Washington 1,500 19192 7-9London 10,236 7,917 7.7 Zurich 540 142 2.6Los Angeles 10,000 1,477 1.5

_ _ _ _ _ _ _

-24-

1l12 In Conclusion

This chapter presented several relationships that may assist inthe estimation of the number and proportions of differenit types ofvehicles in a city for both base and design years.

It should, however, be emphasized that since the relationshipsare based on average trends in many cities, they cannot be more reliableor accurate than the observations or estimates that are p)repared in acomprehensive transportation study. Hence, if a difference between thepresented relationships and the reported observations or forecasts ina certain city becomezapparent, it does not mean that one is more reliablethan the other; it only should call the attention of the reviewer to thedifference, and he should then search for the reasons that might havecaused them, such as some specific characteristics of the city.

In summarizing this chapter, the following points are noteworthy:-

1l Past and present trends of motorization in relation to economic indi-cators are the foundation for foreca.sting future motorization. Nonethe-less, and since the future hides many uncertainties, it should bepreferable to consider a range of possible futures, such as alternativesaturation levels of motorization.

20 After the range of future motorization levels, including that for anyspecific design year, has been established, and after alternativepopulation levels for the city have been considered, the absolutenumber of vehicles, by type, may be estimated, including cars, motor-cycles and commercial vehicles. Further stratification, such as byhouseholds and their income levels, may then proceed according tothe outlines presented in this chapter or by an alternative procedure.

3. The number of buses and taxis is more difficult to estimate on thebasis of inter-city relationships, since they would strongly dependon specific local conditions and policies0 Nonetheless, and as willbe shown later, this can be regarded as a minor problem when estiraatingthe assigned vehicle-kilometers of travel on the road system, as manyassignment procedures do not even include bus and taxi vehicle tripsin the standard process, but estimate them separately0

It should be realized that this chapter presents only a few generalrelationships from a wide and complicated field of research0 However, andas pointed out earlier, the aim of this paper is not to present a compre-hensive manual on travel characteristics as such, but rather to comparetrends in a wide range of cities in order to isolate those characteristicswhnicn may identify the threshold, in transportation terms, which a 'deve-loping' city may cross to become a 'developed' city0 In this respect, nosuch threshold ha.s so far been identified, and all the basic relationshipshave been found to be continuous ones, with no dividing line0

The search for such factors will continue in the following chapters,covering other aspects of the transportation systemo

-2 5-

Chapter 2: The Transportation System

Introduction

This chapter describes some of the major characteristics oftransportation systems that serve cities, such as the road system and

the public transport system. The description covers the physical

aspects of the systems, including length, density and capacity.

The road system may be defined in a variety of ways, depending

on the purpose of its application. For instance, it may include the

full road inventory or only the roads that are coded for the assignment

process. The road system may also be divided according to the opera-

tional. characteristics of its components, such as expressways, arterials,

distributors, connectors and locals, just to name a few of a long list.

When the road system is defined for the assignment process, only

a skeletal representation of the complete system is developed, where the

components of the system are chosen according to their relative importance.

Thus, the assignment networks usually include all expressways, most arterials

and only a fraction of local roads.

The road systems analyzed in this chapter refer to the assignment

networks, as described in urban transport study reports. Furthermore,

only the expressways and arterials are discussed, since the local roads

usually serve as a technical part in most of the assignment procedures

and not as a true representation of the local road system. Expressways

Rre defined in this paper as highwarys with full or nearly full limited-

access with standards usually found in developing countries and Europe.

The arterials are defined as all the major roads that are mainly usedto carry throlugh traffic.

The analyses in this chapter cover road densities, flows and speeds,

and dynamic capacities, while in chapter 4 a simplified and rapid assign-ment technique for macro-distribution of vrehicle travel on expressways

and arterials will be presented.

With respect to the public transport system, it may be divided

into three major components: 'road' public transport, such as buses and

taxis that use the general road system for their operation; 'rapidtransit', rail or bus, that operate on its own right-of-way; and 'composite'

pablic transport, such as trams, that benefits from partial right-of-way.

Since the road system serves all vehicles, both private and public,

bus and taxi travel are included in the analysis of the road system. Rapid

transit and trams, on the other hand, will be discussed separately.

As already mentioned in the Preface, this chapter deals with some

measures of the transportation system characteristics and its physical

interaction wzith the amount of travel loaded on it. Only later, in

Chapter 3, will travel behavior and travel demand of tripmakers be added

to the transportation system, in order to show how the two factors inter-

act with each other.

-26-

2.l The Arterial Road System

Arterial road density can be expressed in three principal waysg interms of land area, nopulation or vehicles0

It should, however, be pointed out that the first method is adoubtful one when considering macro-densities, averanged for the totalstudy area, since the size of the study area is based in most cases uponestimates of future expansion of the urban-r area. Consequently, thedefined study area may cover wide empty- spaces at base year conditionsand, therefore, any attenmpt to relate the base year road network to thetotal study area may reslult in extreme distortions of densities.

Table 2.1 and Figures 2.1 & 2.2 presenit base year -arterialdensities in a selection of cities, by 10,000 population and by 1,000cars respectively vs. motorization up to a level of 25.

The two relationships, especially the first, seem to be consistent 0The most significant result is the indication that although the nrteria'Lroad density increases with motorization, it does not seem to catch upwith the increase in the absolute number of cars at relatively lolw levelsof motorizati-on, althouglh it recovers at higher levels, above 10 cars per100 population0 This trend shlould be expected to affect the speed oftravel in cities with relatively low motorization levels, as indleed itseems to happen0 Namely, in such cities the speed is relsqtively lowertlhan expected by the low motorization level 0

At this stage it should be noted that arterial density is stronglydependent on population density0 That is, if population densities arevery high, there is a physical limit to the addition of arterials, sincethey would become too closely spaced0 Hence, if population density in acity is high, such as in most developing countries, there might be acritical point after which the addition of arterials would not be ableto match the increase in the number of vehicles, and the speed woulld thenbe expected to decrease appreciably0

Table 2.1. Arterial Network Density per 10,000 Population land per 1,000 cars (V I

Arterial D e P1 6 i V MOori-No. City Year Population Cars 1Length j zation

1 Bogota 1969 293399560 559000 241 1003 4 38 2Q352 Singapore 1968 290009000 819000 241 1 21 2098 4 a 053 Bangkok 1972 4s90679000 1759000 775 1l91 4043 4.,,304 Tel Aviv 1965 8179000 399640 172 2;I.l 4034 1 4.855 Kuala Lump 0 1973 9129490 659440 227 2.48 3047 70176 Caracas 1969 290729740 2009000 495 20.39 2048 9.657 Hull 1967 3449890 43,180 96 2-76 2.,22 120528 London 1961 898269620 19 2499450 39109 . ? 2 A49 L4.!69 West Midl. 1964 295299010 3889350 920 .3,64 2.37 15-36

10 Rotterdam 1974 19 0609 000 2609000 (AO 3 6Q 0 269 24 53~~~~~_ _ . .-.i......,__ =

- 27 -

@10

0.H.4-

t 2 ~~~~~~~~~~~~~~~~~~~~~~0.074 X

X 10.02 Y= 11656

0 0 p~~~~~~~~~~~~~~~

4X 6

0 10 20 30Motorization

Figure 2.1: Arterial Network Density per10,000 Population vs.Motorization

t 8 9 *10

X 2

Y =5.740X _

M ~ ~ ~ 0 _

0Z O . ' , I ,I

0

0 10 20 30Motorization (c/ >

Figure 2.2: Arterial Network Density per

1,000 Cars vs. Motorization

-28-

2.2 The Expressway Road Density

While base year conditions in cities with low and medium moto-rization levels included enough cases for analyzing arterial densities,there wasnot even one city with a complete system of expressways withinthis range. Consequently, the analysis of expressway density had to becarried out with proposed systems for design years. Therefore, thefollowing analysis reflects what planners considered as desirable, withno assurance that such plans would actually be implemented0

Table 2.2 and Figure 2.3 present the expressway density as plannedin a selection of cities0 Since expressways are plarmed for the exclusiveuse of vehicles) the expressway density was calculated per 10,000 cars.The surprising result is that although the plans have been prepared duringdifferent years, in widely different cities, the trend is very consistent.

(It should, however, be noted that the (density is based on theexpressway length, although the average number of lanes may vary to alarge extent. Thus, the relationship in Figure 2.3 presents only oneaspect of the subjec-t, and further discussion will take place in Section 2c3)o

Referring back to the relationship of arterial density, presentedin Section 2.1, it becomes apparent that after the maximum arterial densityis reached, the only possible wray to increase vehi.cle speed (or at least toa-void its decrease) with increasing number of vehicles is to add expressways.

The question at what stage in the life of a city an expresswaysystem is justified, if at all, will be discussed later on, when theinterRction between city size, motorization, system supply and travelbehavior will be formulated0 At this stage let it be said tlrt theavailable daLa, such as shown in Figure 2.3, do not indicate that such athreshold exists in practical terms0 Theoretically, it could be inferred.from the equation expressing the relationship in Figure 2.3 that express-ways start to be justified when the number of vehicles exceeds 27,250, sothat 1 kilometer would already be justified at 29,750 cars. This resultis, of course, misleading, since the minimum expressway system seems to beconsidered as justified w'Len the number of cars reaches about l00,O00gNonetheless, even this value should not be considered as a thresliold,especially when the current policy trend is to give priority to publictransport over cars0 Further discussion on this subject, with specialemphasis on methods for establishing thresholds for the ,justificationof expressway systems, will be presented in Chapter It0

- 29 -

Cdmo5 0900

4v 4

43

31 4

2 DTel Aviv 1985 1,225,000Y 0 1 .0004X -10.9

0 2 4 6 8 10 12 14 16Number of Cars (1Q2,000)

Figure 2.3: Expressway Network Densityvs. Number of Oars

Table 2.2: LEx)pressway Network Density per 10,000 Cars

No. City Year Population Cars Mot. L,km. fl/cars

1 Hall 1981 372,500 97,170 26.1 17 1.752 Tel Aviv 1985 1,225,000 197,570 16.1 .68 3.443 Athens 1980 2,800,000 270,000 9.6 ~.:96 3.564 Brisbane 1981 1,020,800 361,270 35.4 155 4.295 Singapore 1992 3,174,000 504,710 15.9 188 3.726 Bangkok 1990 8,000,000 600,000 7.5 220 3.677 Baltimore 1980 2,161,000 734,270 33.9 250 3.408 Copenhagen 1985 2,077,730 915,350 44.2 374 4.099 Melbourne 1985 3,700,000 1,291,400 35.0 495 3.83

~~~~~~~~-30

2.3 Dnmc Capacity o Ra System

It has already beeni shown that the three basic components ofvehicular traffic in an urban area., namely the road system, the flow

of traffic and the resultant speed of travel, may be linked within anempirical relationship that9 in its approximaqte form, can be expressedin the following wayz-

I = X D I (201)

where: I - Traffic Intensity, velh.km./sq.km.;D - Road Density, km./sq.km.;v - Space-mean Speed, kph.;

- A Constant.

This relationship has been called the Alpha-Relationship (L7)o

It has further been shown that Alpha has the general form of

the kinetic energy of traffic flow, and since it appears to be quite

stable for a given road system during a certain period of time (peakhour or average daily), it has been suggested that the Alpha value may

express the traffic performance of the relevant system.

In its simplified 'kinetic energy' form, Alphs. can be expressed by:=

=q v = C v (2.2)

uhere q is the flou per average unit length of the road system, and Cis the concentration of traffic per unit length of the road system.

Referring back to Eq. 2.1 and multiplying both sides by therelevant ground area will result in the general relationship:-

K = X v 3 (2-3)

where: K - Vehicle Kilometers of Travel;L Road System Length, km.

Furthermore, the K s the speed v and the amount of vehicle

travel time H on the road system are related byg-

H = -K (2 0 4)v

Thus, substituting K from Eq.2 03 in Eq.2 04 will result in the relationship:.

-v 9 (2.<)

or:.

v = L 9 (2.6)

Since both Alpha and L represent a giv£n Y-oad system, the speedof travel may then be estimated for any amount Ca' -. ;ehicular travel time

on the road system.

-31-

The Alpha values in this paper refer to daily travel on the road

system, nsmely on the macro scale, under medium to heavy traffic loads.

If, however, the study is a regional one, with an extensive rural area

aroutnd the urban core, then the Alpha value should be derived, or ap-

plied, to the urban area only.

The procedure for deriving the Alpha value for arterial road

systems is detailed in Table 2.3 for a selection of cities at base year

conchtiois and design year estirntes, w.rhere Alpha, in its approximate

form, is the product of 'q' nnd ilv.

Table 2.3: The Arterial Alpha-value

No. City Year Kilometrage Road Length Flow Speed Alpha

(000) K km. L- kph__ ___ (co m .. _____ p ___ __l

1 Bangkok' 1972 lo,135.5 495 20,500 20.0 410,000

2 Bogota 1969 4,200.0 240 17,500 24.0 420,000

3 London 1962 39,172.0 3,109 12,600 34.6 435,960

4 I-est Midl. 1964 10,100.0 920 10,980 37.0 406,260

5 Hull 1967 1,045.0 96 10,885 38.6 420,160

6 Rotterdam 2 1973 1,185.8 110 10,720 41.6 445,950

7 Copenhagen 1967 12,951.0 1,480 -8,750 44.2 386,7508 Rapid City 1985 1,393.0 193 7,220 43.2 311,800

9 Melbourne 1985 27,400.0 3,060 8,950 47.0 420,850

10 Tuscon____ 1980 6,350.0 _ 935 6,790 50.6 343,650

(n ) Urbanized area; ( 2 ) On a sample of the road network.

Figure 2.4 presents the relationship between 'q' and 'v', where

it beoomes evident that Alpha is quite similar for a wide range of

cities, although the average number of lanes in each road system is

not known.

25 r I I

' 1'200

02 * Base Year

0 Design Year

< 1S \ Y= 400,000o \ X - 1.56

Ca 3 5

40 0 06Ev10 t 7 (3

0 8~-. 010

5 20 30 40 50 60

Speed, kph.

Figure 2.4: Daily Average Arterial TrafficFlow vs. Speed

-32-

The interpretation of this result is That the dynamic napaci,ty ofan arterial road system is relatively stable in many cities, althoughlocal differences sre, of course, to be expected. It would, therefore,be preferable to derive the Alpha value for each city separately, basedon actual observations. If, however, the Alpha in a given city isunobtainable, an average value of 400,000 may be used with an aeceptableuncertainty. Furthermore, if the Alphia value is used for testing alternm-tive transportation plans one against the other, then the Alpha valuebecomes a common denominator whatever its value may be, thus lesseningthe danger of distortions. Fxamples of actu.al application of the Alphavalue will be presented in Appendix 6.

When trying to derive the Alpha value for expressway systers, onlyone base year case was available. Therefore, as in the case of expresswaydensity, recommended plans for design years had to be iised again, as detailedini Table 2.4 for a selection of cities and presented in Figure 2.5.

It becomes evident that the Alplha value for expressways is quli esimilar in a variety of cities, with an alrerage value of about 2,500,000.(It shoul.d be pointed out that this is the only case where the funcl;ionalform of the representative equation was not derived as the best fit. bit waskept similr to the one developed for the arlerial system).

Table 2.4: The Expressway Alpha-value

No. City Year Kilometrage Road Length Flow Speed Alpha(000) km. kph

1 Tel Aviv 1985 3,367.9 64 52,620 53.0 2,789,0402 Hull 1981 728e0 17 42,820 63=0 2,697,6603 Baton Rouge 1985 1,970.0 61 32,300 72.2 2933290604 Knoxville 1982 3,120.0 104 30,000 74.3 29229,0005 Orlando 1985 6,420.0 227 28,280 81.2 2,296,3406 Copenhagen 1967 l 228.0 44 27 ,910 97.7 29726,800

60 , ,

0 Base Year01 )0 Design Year

50 - 2,455,190

r=q40

> ~~~~~~~~3@30

05

20 L L

50 60 70 80 90 100Speed k, kph.

Figure 2.5: Daily Average ExpresswayTraffic Flow vs. Speed

-33-

At this stage it becomes evident that the aveiage number of lanesnust be considered qas well., especially when the expressway systems insome cities showed except.ioiially high Alpha values. Unfortunately, theave-eragre niumber of lanes could be derived for only a limited number ofca3es, as deitLpiled i.n To.ble 2.'• and presented in Figure 2.6.

It c!n be inferred from Figure 2.6 that the average number of lanesIs -indeed an important factor inr thme cs.se of ex1pressway systems and thatit- should be considered when applying the Alpha value in such cases. Thus,the previouis average value of 2, 7.00,000 s:fems to be reasonably applicablefor systems within t;he r-ange of lh to 5 lanes, but it should be increasedfor systems above '; lanes in accordance with the trend i.n Figure 2.6.

As a closing remark, it should be mentioned again that theexpressway Alpha values are based on projected data. Although this iscertainly a serious si,or,comiing, it should be noticed that the applicationof such Alpha values tor testing alternativre proposed expressway systemsis no worse a practice thlan the actual testing of such systems by compre-herisive traffic modieis, since the above Alpha values a,e based on theOUtpR r,sO (Vt'} suchnodocl.s.

Table 2.5: Effect of the Number of Lanes on the Expressway Alpha-value

No. City Year Kilometrage Length Flow Speed Alpha Lanes(000) km kph

1 Knoxville 1982 3,120.0 104 30,000 74.3 2,229,000 4.02 Tel Aviv 1985 3,367.9 .64 52,620 53.0 2,789,040 4.83 Hall 1981 728.0 17 42,820 63.0 2,697,880 5.04 Athens 1980 5,800.0 96 60,420 65.0 3,927,080 5.85 London 1981 55,200.0 542 101,850 65.0 6,6199930 6.9

8 . , ~~~~.3 .X

Y 442,072 0 38X.5

6 -

0.H

04 ~~~~~~04fr

20.03

013 4 5 6 7 S

Nlumber of Lanes

Figure 2.6: Expressway Alpha-value vs.Ifumber of Lanes

-34-

2?.4 The a-pid Transit System

The analysis of rail rapid transit met wiLth rmny difficulties.

The data were derived from tlh lates6 publi.cation on the subject (16),

which is a compilation of reports recelved from operators of public

transport in many cities0 Hlence, many components of the d:itq were fouind

to be lacking common denomin*tors for inter-city comparri7ons.

Table 2.6 presents some of the availab' e data frotm t.he above souLrce,

4ith the partial a.ddition of motori7.ation from Ref. iJ. The data is based

on both yearly totals and daily averages (whenever available). Alretdy at

this stage some discrepancies between the yearly and the dai-Ly dat-ai may be

noticed; i.e., dividing the annual number of passengers by their average

daily number shoul.d result in the average number of opernting days per year.

However, the resuLlts indicate fluctuations fromn a mfinimum of 16)1 days in

Bost;on to a maximum of 398 days per year Li Stortkhulm! One possible

explanation for such variability is that the data in the above publi.cation

were derived from different sources.

Althoigh no clear-cut relationships cotld be derived from the

available data, the general tendencies are that the number of passengers

per kilometer-line seem to: (i) decrease with increasing motorization; and

(ii) increase with increasing size of' the city. Thus, when considering

cities in developing countries, where motorization levels are still

relatively low and city size increases rapidly, a range of about 15-30,000

daily passengers per kilometer-line may be expected, subject to limitations

on motorization increase and the extensiveness and coverage of tne system.

Although no better estimates can be derived from the data in

Table 2.6, it will be shown later how a new method may assist in a more

precise estimation.

Table 2,6s Rapid Transit Systems in a Seleotion of Cities 1973

City Popul. Mot. Length Annual Pass. Pass/km Pass/Pop Daily Pass/km Dawys

(000) km Million Million Million Pass. per(000) Year

Athens 29540 610f 25.7 92,3 3O59 101 300 117670 308

Boston 2,754 13.5 48.0 95.0 1.98 17,4 580 129080 164

Buenos AireE 85,800 7n40 31.6 242.1 7.66 3.6 662 209950 365

Chicago 69715 143o0 103.5 Oo72 2103 400 29800 259

Hamburg 2,450 20.2 90,7 187.2 20o6 37,0 540 59950 347

Leningrad 49066 45,0 399o3 8.87 111 19321 299360 302

London 79418 22,20 388.0 655.0 169 52.3 2,250 59800 291

Mexico City 89600 7.2 40°0 390°0 9°75 406 1V200 30,000 325

Milano 29560 '34o2 125.6 3A67 13.4

Montreal 2p743 20.6 25.6 127,4 4,98 9.3 400 15,630 318

Mosoow 7,300 150,4 19628.0 1082 20,6 4,840 329180 336

New York 179800 26o0 429.0 19272.9 2.97 24.1 49670 109890 273

Paris 89197 24,8c 248.1 19224.0 4,93 30o3

Philadelph. 39800 34.3 23.3 9.5 0,41 6o1 38 19630 250

Roma 2,800 11.0 21.8 198 3,9

Rotterdam 19064 23.8 7,5 28.0 3.73 7.0 100 13,330 280

San Franoisc 49600 121.0 26.4 0.22 26.3 120 990 220

Stockholm 1,486 70.5 187.0 2.65 47.4 470 6,670 398

Tokyo 219600 8.30 149.7 1,497.6 10.00 20.6 49840 32,180 336

Toronto 29628 42.0 169.2 4.03 16.0

liena 29000 26.7 72.5 2M72 13.4

( 0 d 1Q70

- 35-

Chapter 3: Travel Characteristics

Introduction

It has already been shown in Chapter 1 that income has a majoreffect on motorization levels. It can be inferred that income may alsobe a major factor in shaping travel behavior and determining itscharacteristics, such as trip rates, modal splits and trip distances. Itcan also be assumed that income may not be the only factor constrainingtravel behavior and that other constraints may play a role in shaping travelcharacteristics. Several such factors will be presented and discussed inthis chapter.

An important aspect of data on travel characteristics should bementioned at this stage, namely their presentation in aggregated ordisaggregated form. Most of the current traffic models are based onaggregated data, e.g. all travel parameters and characteristics areaveraged on a zonal basis. Hence, the aggregated data reflect differencesamong zonal averages rather than among different levels of travel behavior.Indeed, detailed studies have already indicated that the variations intravel behavior within each zone may far exceed the variations between zonalaverages and, therefore, that much information about travel behavior is maskedby aggregation. It may be inferred that disaggregated data are moreappropriate for traffic analysis, especially since travel forecasts should bebased on causal, behavioral relationships rather than on descriptive, zonalrelationships. However, disaggregated models are still in the developmentstage, and most available travel data from comprehensive urban transportationstudies, such as those presented in this report, are still based on zonalaggregation. This may be regarded as a serious limitation when trying toderive a mechanism of travel behavior; even so, the mechanism investigatedseems to be strong enough to emerge above all the zonal 'noise', and topresent a coherent explanation of the underlying principles of travel behaviorin all cities. Moreover, the principles seem to follow, or at least to beconsistent with well-known phenomena in other fields such as economics.

Although this chapter follows the structure of the previous chapters,in that each section deals with a separate topic, it should be noted that eachof the seemingly separate relationships described forms a part of a singlemechanism of travel behavior, which describes how tripmakers make decisionsunder constraints. This chapter deals with several such constraints; thenext chapter will discuss others.

-=36

3,1 _xpedi~ture on Travel

It has already been shown in Sections 106 and 107 how income affects

the level of motorization, It is to be expected, therefore, that income

should also affect travel belhavior. Indeed, this seems to be the case,

where households are found to have travel budgets that are proportiona.l

to their incomes.

Table 301 anid Figure 301 present the results of a recent studyconducted in the U.K. on the relationships between household average

weekly incomes and expenditures on travel (18)o

The expenditures were grouped in Figure 301 into 3 classes: (i) public

transport, including bus and rail; (ii) private vehicles, including purchase,

maintenance and operation; and (iii) other modes. Referring to Table 3.1 and

Figure 301, it becomes apparent that expenditure on travel, as a percentage of

income, becomes stable at relatively low levels of income - group 4 in the

table - with an average value in the range of 10-13 percent.

Table 301 also presents expenditures on public transport as apercentage of total expenditure on travel. Such ratios may be regarded

as modal splits by expenditure, complTmentary to the better known moda.l

splits by trips. These ratios, in percent, versus income are shown in

Figure 3.2, where the standard shape of a modal split curve is evident,

such as the one shown in Figure 3.5.

The last point of interest at this stage is that an increase in

income increases expenditure on travel by all modes. Thus, although the

expenditure on travel by public transport seems to decline versus expen-

diture on travel by private transport in relative terms, it does continue

to increase in absolute terms with increasing incomesO One possible

interpretation of this phenomenon will be presented in the next section.

Table 301s Household Weekly Expenditure on Travel vs. Weekly Income,L, U.K. 1972

NO. Inoome Private Bus Rail Tot.Pab. Other Grand Expend. Public/ Raill__ ____________ __Total J % Income Total Public

=___..._.__ ____ ___ ___ __ ___ ___ _ _ ____ _ _ _ _ __ __ _ _ _ __ _ _ _ _ p 1

1 0 10 0.06 014 0001 0015 0023 0o25 303 60.o 6-7

2 10 - 15 O,34 0-24 OO5 0.29 0.03 Oo67 504 4303 17023 15 - 20 o.62 0.33 0.12 O045 006 113 6-5 39.8 26.7

4 20 - 25 170 0.41 0.07 0O48 009 2.26 1001 21.2 14.65 25 - 30 2.43 0051 0010 0.61 022 3.26 11.9 18.7 16.46 30 - 35 2.77 0050 013 0.63 015 3055 lOo9 17-7 20.67 35 - 40 3045 049 0o15 064 0.18 4.27 11.4 1500 23-48 40 - 45 3.66 0-57 o.16 O073 022 4.61 lo9 15.8 219

9 45 - 50 4-72 0-57 020 O077 0.25 5074 12.1 13.4 260O10 50 - 60 5-73 O.63 026 o089 0.61 7,24 13.2 12.3 29.211 60 - 80 7000 0o68 039 1.07 0-53 8.6o 12.3 12.4 36.4

12 80 plus 1023 o066 0-83 149 1.06 l2-77 12.8 117 5507e _A _3 I 0o_ o) 03 f _

Average 3.92 0000221(07 0.32 49

- 37 -

14 ' ' ' ' ' ' ' ' ' *Total

, 12 - Other

E-4 10 -

0

g 1 2 '^ s * ;' t Io 1; ' r '~Co r

6 C 6

4

'00-1~

F-igure 3.1: Household Expendliture on

Travel vs. Household WeeklyInoome U.K. -1972

w 0 , , I , . . _ . . Publ .0

0 0

1 60 3

;9 20 20 40 60 80 100

Income , E , and Income Group

Figuxre 3.1s Household Expend.iture Mona Travel vs.ee Houbehlcad Weeklye

Trvlv.Income , U.K. - 1972

q4)

oe 4004 0 o .0

.1-I ~Icoe LadInoe ru

Fiue32-40shldEpnitr oa

Incove, vs I nd-come, UGroup97

-38-

3.2 Rail versus Bus

It can be sihown that the rail and bus modes behave as two differentmodes of transport, with their own modal split relat tonship, at least withrespect to expenditures.

Table 3.2 details preliminfry res-ults from a study carried outrecently in London on expendituLres on travel (19), sliniTlr to the studyfor the whole IJo K. but stratified into somewhat different income groips.

Figure 3.3 presents e;xpenditures on rail and buis maodes vs. income,where the U.K. income scale is slhown below the -diagram nmd the Londlonscale above. Two interestinlg indications may be inferred fron thlisdiagram: (i) expenditures on bus transport are practically idenati.cal inboth London and the U.K. (as are expenditures on the private transport)0Fapenditlires on rail transport, on tthe other h-sand, are erltirely different,where the urban rail transport in London takes the tipper hand. Tt may,therefore, be inferred that the urban rail, transport, or what is usiallcalled 'rapid transit', plays an important role in the; urban transporta Lionsystem, even for households with high incomes; and (ii) there seems to bea clear modal spli.t relationship between rail and bus expenditures. However,it is not yet clear whether rapid transit sl-dolcl. be regarded .s competitiveor complementary to the ij'us rnode.

The rel.ationship between rall and buns (KpetLditurns is shown ittFigure 3.4, .There the proportions of r3il over bus seem to be a linearfunction of income in both London and the [U.K. As cmn be seen, this ifodal.split relationship is different in stape from the one. between the pri.vnteand public modes, thus indicating some basic difference between the twro.Indeed, the most intriguing result is tl,at while expenditures on the privateand the public miodes are similar i.n both London and the U.K., they -resignificantly higher for the rail mode in London; namely, households seemto expend more on travel when an urban ropid transit system is avallable.It coul.d, therefore, be inferred that a rapid transit system seems togenerate, or induce, more travel. Whether this indication is ri.ght orwrong will be discussed later, but let it be mentioned here that theintroduction of the Victoria underground line in London, in 1969, wasestimated to have induced an increase of about 20 percent more trips (20).

The following sections will concentrate on the subject of the modalsplits between the private and the puiblic modes in different cities, wi.thspecial attention to rapid transit,

Table 3.2s Household Weekly Expenditure on Travel vs. Weekly Income,L,London 1972_ _~ ~ ~~__ ,__ __ ___-- .- __ _ _ _ _ _ _ _ _ _ ._.. - _ __ _ _

No. Income Private Bus Rail Tot. Pub. JGrand Tot 0. Rail/Publlc Split

1 0 - 15 0.09 019 0.09 0.28 0.37 32.12 15 - 30 0,63 0,39 0.32 0,71 l.34 45.13 30 - 40 3,04 052 0,48 1.00 4°Q4 48304 40 - 50 3.27 0.60 0,77 1.37 4.64 56.,25 50 60 7,60 0o67 1.02 1.69 9o09 60(46 60 plus 8,49 0.71 1.31 2.02 l0o5.l a,9

Average 4,67 0.531078 _5.98

- 39 -

1 2 3 4 5 6 _ London1.6

* 1.4 -Ril * LondonRol0 U. K.

0 /~~~~~~~~

fr 1.2 // Bus * LondonI 0~~~~~ U. K.

U 1.0 S0~~~~~~~~~~~~~~~~~~o /'

3 b 0t':/Z" @""'~~~~~L_C c

1 :94 56 010 11 .

6~~~~~~~~~~~~~~

0 20 40 60 so 100

Income, LJ, and Income Group

Figure 3.3: Household Expenditure on,Rail& Buis Travel vs. Income, U.K.and London -197 2

1 2 3 4 S 6 -- London

0 - _

E-4 ~.,.2

a Ef 60 2 0

*~~~~ad Lodo 20 1972*ono

t O1 2 3 4 5 6 7w L 0 1nd ' - nK8 2 0 204 08 I

%X 4-noe ndIcm ru

$4~co0

Fiur 3.:HueodEpedtr oa

E- 40.

oE-4~~~~~~~~~~~~~~4

020 ~ ~ ~ s -noe U.K. 'Loondnon7

6 0\j:. +, ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~0 .K

0~~~~~~~~~~~

40204 0s 0

~~~~20 v..A *nom,L.KonLodoo17

-40-

3.3 i4odal Split

lhen stratifying person trips by mode vs. motorization, i rlearmodal split relationship comes to light, both inter-city and intra-city.This relationship is to be eypected when rememberinig thiat both theexpenditure on, and the availability of cars incrense with income, asalready seen in Figures 1.10 and 3.1.

Table 3.3 and Appendix hI detail the public transport modal splitvs. motorization for a selection of 2i cities. iRotoriz-ition, especiallyin cities at low levels, are per vehicle (cars + motorcyrles) wheneveravailable.

Figure 3.5 presents the above results in a gr"phical form. Ttshould be noted that cities 21-2' include both bus and rapid transitsystems, although only the former is considered in this diagram.

As can be seen in Figure 3.-, a clear relat-onship is indicniLed,where the proportion of road public transport trios decreases sharply withincreasesim motorization, especially at low and mediam levels. There is,of course, some dispersion of points, some of which might have been causedby different definitions and procedures used in the various studies.Nonetheless, and as will be shown lAter, an additional possible explanationis the efficiency level of the road systerit vs. the public transport system,as well as the differences in travel. costs.

Table 3,3: Public Transport Modal Split and Trrip Rate (Appa4)

No. City Year Mot. Modal Split | Trip Rate

1 Athens 1962 2.1 75.8 1o082 Bogota 1969 2.4 83.3 0.913 San Jose 1973 4.6 76.3 0.854 Bangkok 1972 6.1 69.6 0,775 Singapore 1968 6.4 64.0 0.906 Tel Aviv 1965 7.2 61.0 1027 Caraoas 1966 8.8 54.7 0,778 Kuala Iumpur 1973 12.2 39.6 0o699 Hull 1967 12.5 36.9 0.5310 Essen 1965 13.0 46.0 o11 Rotterdam 1966 13.1 36.3 0.3212 Mulheim 1964 14.4 40.0 Q

13 Wuppertal 1964 16.1 40,0 Q

14 Hamm 1965 18.2 30,0 Q

15 Krefeld 1968 19,5 25,0 Q

16 Baltimore 1962 27.2 18,7 0,3017 Springfield 1964 36.7 407 01018 Monroe 1965 32.8 10.0 0,3019 Cinoinnati 1965 34,8 5.9 0,1320 Baton Rouge 1965 35.2 7°7 0.20

21 London 1962 14.1 42.7 - 52,0 0.46 - 0,662J! London 1971 22.5 29,6 41,0 0,37 0.6122 Berlin W. 1968 15.3 40°9 50°7 0,59 -08823 Hamburg 1970 23.9 19.7 - 37,5 0.34 - 0.8324 Philadelphia 1960 28.5 14.4 - 19,5 0.29 0,4125 New York 1967 32.7 14,5 26,3 0,24 0.51

- 41 -

100 T

G) With Rapid Transit* Without Rapid Transit

20 Y=114.03-66.A81ogX

80'4 ~ ~ *

o 10 03

.4

E-4 S 4 105

60 0

.7

@10

r. 40 8 ~~~1403 1

SOzEq 20 250

' 2 0 23 16

2 X240 25

020017 *19

00 10 20 30 40 50

Motorization

Figure 3.58 Pablic Transport Modal Split vs.Motorization in Selected Cities

-42-

3.4 Pablic Transport Trip Rates

The relationship between the public transport trip rate (i.e.,number of dai-ly public transport tri.ps per person) versus motori.zation,as detailed in Table 3.3 and presented in Figure 3.6, show!s the saimetrend as before, namely a decrease in the trip rate wibli an increasein motorization.

In Figure 3.6, however, the trip rate in cities w¢ith rapidtransit is shown twice, once without and once with rapid transit.The surprising result is that cities vith rapid transit seem to havetrip rates over and above the expected ones by motorization; i.e., asif rapid transit induces more trips in thte arban area, depending onadditional factors such Ps its relative coverage :-nd effIcielncy.

This result corroborates the previous indication, namely thnthouseholds expend more on rapid transit in London than on r.i'l in theU.K. In other words, the bus system seems to hold its place inl therelationship, even in cities with rapid trnnsit wrhile the latter seemsto generate an additional number of trips. It might, therefore, beinferred that a rapid transit system may not he competitive - or to aminor degree only - with -the bus system.

Another possibility is that the rapid tranisit system might becompetitive with the private transport, Indeed, when seeing the modalsplit from the private transport point of view, as presented iLn Figure3.7, there is some indication that the private share in cities withrapid transit, when only the bus system is considered, is somewhatlower than expected,

Nonetheless, the effect of rapid transit on modal split is morecomplicated and indirect than the above explanation has indicated, for thefollowing reasons: (i) rapid transit systems decrease the need for cix.rsand, therefore, inhibit the increase in motorization; and (ii.) rap-id transitsystems usually operate in large and congested nities, where the cardaily trip rate is low with or without rapid trqnsit. Hence, the privatemodal split would seem to sliffer in such cities, although not directlybecause of the rapid transit system.

It may therefore be inferred that when the car trip rateis low, usually because of congestion and low travel speeds, a rapidtransit system may augment and complement private transport, as well.as complement bus transport, which use the same road system as rayrsand are subject to the same congestion and low speeds0 Tndeed, w.henthe scatter of cities in Figure 3.6 is checked more carefully, itbecomes apparent that the 'road' public transport tri.p rate is sensiti'.enot only to motorization bait also to the car trip rate, as shown in t;hefollowing section.

- 43 -

1.4 .I

.- 3 1.2 -9\, Y 1.16 -. 0.6 X

- 1. 0S >

*,~ 0 2 * I .o 6

3 70 202 3034 o23

.L 6 ''

10 0 _j , , , I ,9 0 I.l8S2 ,

8.4 I * A'? 424 t.11~~~

o 5 @ * ~~~Motorization by ar

Figure 3.6 PULI TRANSPORTo TbI RATs v+ MOORZAIO

20 42 *City with Rapid Tr 6 n 2

17 0 1

00

0 1 0 2 0 30 40

M ot or izo ati onm, %

Figure 3.76: PRIVATE TRANSPORT MODAL RALTE vs. MOTORIZATION

100~~~0112i

40 5gU6~~~~~~~~17019-0~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~~~ 20~ ~~~~~~~4

02 *~0011 CiywthRpd3rni

40 6

0 0 0304Motriz tioriato,b Crs%

Figre3.: RIAT TANPOT ODL PLT s.MOORZAIO

The Daily Trip Rates of Cars and Pablic franmort

When the scatter of points in Figure 3.6 is considered with respectto the corresponding car trip rates (as derived from App0 4)s it becomesapparent that the trip rates of both cars and "road" public transport arerelated to each other at each motorization level0

Figure 3o8 presents this relationships -here conditions with highercar trip rates also result in higher public transport trip ratas0 Thissubject will be developed further at a later stagae after discussing first theconcept of the Traveltime Exdgets, as presented in tha following sections0

1.4 Car Trip Rato

010 40

a.~~~~~~~~s

-O -

0 10 20 0 0 40Motorization, %

Figure 3080 PUBLIC TRANSPORT TRIP RATE vs.MOTORIZATION & CAR TRP RATE

3.6 The Concept of Traveltime Budgets

Most transportation models are based on the basic concept thattripmakers value their traveltime in monetary terms, and, therefore,they are prepared to pay in order to save traveltime. Even modal splitsare based on this concept, where the trip time differences between theprivate and the public modes (together with trip cost differences) arethe basis for diversion curves for modal split estimation.

Furthermore, most of the standard trip generation sub-models arebased on the concept that the major factor affecting trip generationis the socioeconomic level of the households. Consequently, once thetotal number of person trips for an urban area in a design year is estimated,the same number is assigned to all alternative transportation systems, withdifferent speeds. Thus, different systems resu-lt in different total amountsof traveltimes, and the alternative that shows the best ratio between thevalue of saved traveltime (and additional benefits) versus the cost ofproviding the system (construction and other such costs to the community)is then regarded as the optimal plan. The concept of saved traveltimesand their value is, therefore, the foundation of most transportationmodels.

One major difficulty in the above methodology is that the totalnumber of person trips remains constant under all alternative systems.That is, it is assumed that travel demand is not responsive to thetransportation system supply. On the other hand, past experience andpresent understanding of travel indicate that travel demand is responsiveto system supply. How, then can this basic conflict be resolved?

It has been already shown in the previous sections that householdsseem to have travel budgets in monetary terms, related to their incomelevels. Is it really inconceivable that they might also have travelbudgets in time terms? It will be shown later that if households - andtripmakers - do indeed have traveltime budgets, then travel demand becomesresponsive to system supply within the constraints of money and time budgets.The question, therefore, is whether households and tripmakers tend to havetraveltime budgets. The following sections will present several resultswhich indicate that they do.

- 46 -

3.7 The Car Da ilyTr~aveltimre

The indications that an average car in an urban area seems tohave a relatively stable traveltime per day were already presented intwo previous reports (219 22).

Appendix 5 details the daily average traveltime per car in aselection of cities at base year conditions, as derived from studyreports either by dividing the (internal daily) assigned car hoursof travel by the number of cars stationed in the study area9 or bymultiplying the car average daily trip timxe by its average trip rate.

Figure 309 presents the above results vs. motorization levels,where it can be seen - for the first time so far - that cities can bedivided into two distinct groups; those having traveltimes above one hourand those having traveltimes below one hour0 Fu-rtherm.ore9 cities withlow motorization levels, such as cities in developing countries9 seem tocluster in the first group, while cities with medium and high motorizationlevels seem to belong to the second group0

Only in very few cases did the study reports also include datafrom which the car traveltime in the recommended plan, at a design year9could be derived and compared with base year values0 Figure 3.9 alsoshows these few cases, with the surprising result that the forecastedcar traveltimes seem to remain stable over time at about Oo8 hours, evenwhen derived from the standard transportation models0 in other words,the plan which is selected is the one in which car daily traveltime remainsstable over time9 while the other alternative plans result in either longertraveltimes (hence the concept of saved traveltimes), or in shorter travel-times (which do not seem to be justified by the economic analysis)0

While the interpretation of and further discussion on the aboveresult will be continued in the following sections, it should be notedhere that one of the possible explanations for the higher traveltimesin cities at low motorization levels is the relative scarcity of cars andtheir high price, which make their usage during the day more intensive,by one or more drivers per car0

The relationship between the car average trip rat,e and its triptime is shown in Figure 310 for the above two city groups0 Of specialinterest is the indication that trip rate and trip Lime are approximatelyreciprocally related in the first group (with an e0l,sticity index ofnearly unity, i.e. -0O912)9 which is an indication, that there is a surongtradeoff between shorter trip times and higher trip rates, and 1rlcs versa0In the second group, however, a lower index of elasti.city, -O-756, indi-cates that shorter trip times may be traded off' nu' only for more tripsbut also for either longer trips distancewfi.e or savings in the dailytraveltimes. Which of the two last possib:;l7.4ies is more pla -,ibie '-1.11be discussed in Section 3.16.

- 47 -

1.8

. 1.6 Base Year *

1.4 @12 *7 Design Year 0

1.2 405 8c) 3 4D6

h 1.0 0140~ ~~~~~~~~1

o .8 109 11 13 c 1 2 017 19*@020

: @^9 12 9 15 016 Ole

.4.

.2

0 I I I

0 10 20 30 40 50

Motorization

Figure 3.9: Car Daily Traveltime vs.Motorization

10

8 1

60

@7

6P4 @16 @10 ~~~~~~~~~~~~~-0.912

43 Y;S9.28X

4 ~~~I~913 02

12 200017 04

@15 19@14

2 - Y 23.13 )c ~~~~0.7562 . 5.3

5 10 15 20 25

Trip Time , min.

Figure 3.10: Car Daily Trip Rate vs..Trip Time

-48-

3.n8 Tripma

It has been shown in the preceding section that cars seem to havestable traveltimes on the road system. Since cars serve tripmakers, theemphasis should now be turned to the tripmakers themselves. It is quitesurprising, however, that very few transportation studies report on thenumber and proportions of tripmakers vs. population and households, thereason being that the trip generation sub-model.s sre usually based on thehousehold unit. Tr avel behavior should, on the other hand, be based onthe tripmakers and their personal travel demand and responses to thetransportation system supply0

Table 304 presents data on tripmakers as derived from the fewstudies a.vailaole to the author0 (It should be mentioned that the datafor Washington, D. C. 1968 refer to the populati'on residing within the1955 cordon line)0

Figures 311 and 3.12 present the tripmakers' ratio per h,ouseholdand per person vs0 household and person motorization, respectively0 ItbecoTmes evident that the tripmakerst ratio per household is more stableand, therefore, also more predictable for design year forecasts0 Thisconclusion can be best explained by comparing Ban'gkok with Tel Avivp-although both cities have the same motorization levels and tripmakersper household, the household size in the former is double the size inthe latter. Conseqluently, Figure 3oll seems to better represent therelationship of tlhe tripmakers' ratio than Figure 3 ol2 (As to thedivergence of poi,nt $ in Figure 312, it might have been caused bylimiting the 1968 population to the 19,1 cordon line of Washington, andfurther testing are to be continued on this subject)0

Although the cases in Figure 3011 are too few for definiteconclusions, the pattern is indicative that the ratio of tripmakersper household may be related to household motorization, e0 g. anincrease in household motorization induces more tripmakers0

In the next section it will be shown that tripmakers also seemto have a stable daily traveltime.

Table 340s Tripmakers per Person and per Household

4 HH ~t 0 ~ 7r aersNO. Ci t y Ye ar 0_lati_r_ SHze H Perss HH - Pore ht

1 Tel Aviv 1965 817.,UU 3o26 4.85 o.16 0049 1i6o2 Bangkok 1972 4s Ju679 = 6.50 IJ3o 0.28 0.25 1.633 Pittsburgh 1958 l,469.,375 3.25 26.9 0.87 0.6u 1954 Washington 1955 1, h25, 320 3,16 2765 0.87 0,61 1935 Washington 1968 l,592.6uU 2.91. 39.0 1.13 o.64 1.876 TTuin Cities-L 1958 1, 294S 521 3,53 32.4 1,15 0.61 2.167 TvJin Cities 1970 ls,453.,040 30.37 40,h 1.36 0o65 2.20

/1 1inneapolis and St0 Paul.

- 49 -

2.4

H- 79

0 22 4 0 a 1

A ~~~~~~~~~~~06

2.0

64 ~0541.8

U4

1.6- 1 0-2

1.43

1.20 .2 .4 .6 .8 1.0 1.4 1.4

Household Motorization

Figure 3.11: Tripmakers per Household vs.Household Motorization

.8

07

.6 00 06P.1 3

14 ~1 05

Ea

a) .4

P43 02

.2

.1

0 10 2-0 30 40 45Motorization

Figure 3.12:. TripmakerB per Person vs.Motorization

3c9 Daily Traveltime per Tri

As already mentioned, studies on the travel behavior of tripmakers,including their daily traveltimes, are very scarce. The following data werederived from two such recent studies in the U.S., as detailed in Table 3.5•(l1l23)-

The traveltimes in Table 3.5 refer to door-to-door times, includingaccess times, as derived directly from the household interview forms, fortripmakers who made all their trips during the day by car.

Table 3-5: Daily Traveltimes of Car Tripmakers

Study Area Year Traveltime, brA

'Washington, D.C. 1955 loO9Washington, D.C. 1968 lollTwin Cities 1958 1.14Twin Cities 1970 113All U.S. 1970 Lb

Although the above results are few, they seem to indicate that thetripmakers' daily traveltime is remarkably stable not only between citiesbut also over time, with a value of just over one hour. Such stabilitywould, therefore, also explain the similar stability of car traveltime, asalready shown in Figure 3.9, especially for the second group. It would,however, be of interest to find out whether the higher car traveltimesof the first group are caused by higher traveltimes per driver, or by morethan one driver using a car during the day. It is hoped that such informa-tion would soon become available.

As to tripmakers who use public transport, their traveltimes showa wider variation than those of car tripmakers. However, this subject willbe discussed later, in Section 317, where the interaction between travelbehavior and system supply will be developed.

Table 3.6 presents the daily traveltime per average tripmaker forall modes, for tho same four cases as in Table 3.5.

Table 3.6: Daily Traveltime per Average Tripmaker

Study Area Year Thavetime, hr .,

Washington, D.C. 1955 lol9Washington, D.,C. 1968 12LTwin Cities 1958 10 14TAin Cities 1970 l.l)All U.S. 1970 lol0

Although in this case the variaLions incr'eased somewhat(because ofgreater variability in bus travel), the tota: daily traveltime per averagetripmaker is still remarkably similar, and seews to show a strong behavioraltrend. For this reason, it is suggested. that we -agaprd trav:lIme per trio°maker as a 'fraveltime Budget. Further exa'-.tes iqd ½olication5 O. the travel-time budget are detailed in the following secticne,

3.10 Daily Traveltime per Person -

and the Effect of Income on the TT-Budget

Table 3.7 details the preliminary results of a recent special

analysis on the total traveltime per average person in the U.K., strati-

fied by population density (24). Hence, low densities represent rural

areas, while high densities represent urban areas. Of particular

interest is the inclusion of walking trips as a separate mode of

transport.

Although the data in Table 3.7 are per person and, therefore, not

comparable with the results in the previous section (as long as the trip-

makers per person ratio is not known for the U.K.), they reveal several

important indicators, as seen in Figure 3.13: (i) the proportions of modes

used for travel seem to vary in consistent ways in the various areas; they

may be explained by population density, motorization levels and quality

of the traffic service; (ii) nontheless, and regardless of what the modal

proportions and population densities are, the total daily traveltime per

person is again remarkably stable for all areas, with a weighted average

of about 0.77 hours. (Assuming that the tripmakers' ratio is-about 0.6

per person - see Figure 3.12 - the daily traveltime per tripmaker,

including walking- is 1.28 hours, namely in accordance with the results

in the previous section).

Table 3.7: Daily Traveltimes per Person by Population Density, U.K. - 1972

Population Density T r a v e 1 t i m e , min.

Persons/Sq.Km. Walking Bus Private Total

Q - 124 10.9 4.2 27.8 45.0124 - 247 12.6 3.8 29.5 49.1247 - 618 16.1 6.4 25.4 49.7618 - 1,235 18.1 5.4 22.2 48.2

1,235 - 1,853 18.1 4.6 19.4 45.21,853 - 2,471 17.6 6.7 20.1 46.12,471 - 3,707 18.9 6.8 18.7 47.03,707 - 4,942 16.6 7.4 16.8 43.34,942 - 7,413 19.6 9.6 12.2 44.47,413 plus 19.9 9.8 12.3 47.2

Average 17.0 6.5 20.4 46.3

The last example is of how income may affect the daily travel-

time budget per car driver in Washington, D.C.(1968), as detailed in

Table 3.8 and presented in Figure 3.14 (22). It becomes apparent that

the traveltime budget (henceforth, the TT-budget) increases slightly

with income, but the variations are very small when compared

- 52

with travelcost budgets 0 In other words, while travelcost budgetsgas expressed by the expenditure on travel in Figure 3.1 vary to alarge extent with income, the TT-budget seems to be relatively stable.This result should be expected since all tripmakers, regardless oftheir income, have the same 24-hours per day for their activities and,therefore, the TT-budget would have less leeway for changes. Further-more, there are also indications that the variations in Table 3.8have resulted more from residence location within the urban area thanfrom income (although the two are closely related)0 It may, therefore,be inferred that income, as such, has only a slight effect on theTT-budget. As will be shown later, this result has an important impacton the current understanding of and procedures for valuing savedtraveltimes.

Table 3-8: Daily Traveltime per Car Driver vs. Income,Washington, D.C. 1968

Household AnnualIncome $ (000) Traveltime, hr0

0- 3 0o703 -4 o694h- 6 0.716- 8 0.798 -10 Oa82

10 - 12 0.8712 - 15 09015 - 20 09120 - 25 0O9125 plus 0090Average 0O85

It should be emphasized at this stage that all the above indi-cations refer to cities and countries that are considered developed 0 Nosimilar data are yet available for other cities and countries, and thereare some indications, such as in Figure 3,9, that conditions in otherplaces may be different, although quantitatively rather thanqualitatively.

- 53 -

60

50 *

-.t e ** ________________ ~~~~~Totol I

A 0 -0~~~~~~~-40.

03 0)

20 03 01

E4-I

I 0

2O 0_____a__ Wal k

-/ Car

10 ;. 4

A

0 2 4 6 8 10

Population Density, 1,000 Pers./sq.km.

Figure 3.13: Daily Traveltime per Person vs.Population Density, U.K.-1972

R 1.2

1.0

.8

0*ri .2

h 0 10 20 30Income , 1 (000)

Figure 3.14 a Daily Traveltime per CarDriver vs. Household AnnualIncome, Washington,D.C.-1968

3.11 Car Average Trip Distance

The size of a study area seems to affect the average trip distancewithin it, where the trip distance increases with the area size. Such arelationship for car trips was developed for a wide range of cities, asdetailed in Table 3.9 and shown in Figure 3.15 for base year conditions.

The relationship between the daily average car trip distance, d,and the radius of the study area (assuming a circular shape), r, can beexpressed by:

dbase = 1.741 r , (3-1)

Although this relationship is reasonably satisfactory at baseyear conditions1 it cannot be applied to design years, the reason beingthat the study area remains constant for both base and design yearswhile trip distances tend to increase with future increases in populationand their spread in the area.

One possible solution to the above problem is to base the tripdistnnce on an expanding study area, according to the following steps:-(i) design year population density is calculated by dividing the fore-cast population by the given study area; (ii) the base year populationis then divided by the design year population density, in order toderive a proxy size for base year urban area; (iii) base year tripdistance would then be related to the base year proxy size, whiledesign year trip distance would be related to the originally given areasize.

Table 3.9 also includes cases for which both base and designyear trip distances were available. Thus, the first step was to relatedesign year trip distances to the original study areas, and the relation-ship was found to be:

ddesign = 2.027 r ; (3.2)

The second step was to calculate the proxy areas for base year conditionsand apply the same relationship to the radius of these proxy areas. Theresulting trip distances at base year conditions were, surprisinglyenough, very similar to those derived by Eq. 3.1.

It is suggested, therefore, to use Eq.3.2 for deriving base anddesign year estimated trip distances, based on the proxy and the originalareas respectively.

Nonetheless, it should be noted that the above simplified procedureis not sensitive for estimating the effects of alternative spatial dist-ributions of population and land uses in a design year on the averagetrip distances and, therefore, other and more sophisticated methods arebeing investigated now, such as the second moment ('Standard Distance')of such spatial distributions.

Moreover, trip distances strongly depend on the definition oftrip-linkage, with the result that the trip distance and the trip rateare reciprocally related, although their product, namely the totaldistance travelled per car per day, remaiiis constant for all possiblepairs.

- 55 -

30 - I

- 20

10 11 10

OS ~1o1%14* 9

E-44

2~~*

4 10 100Radius , km.

Figure 3.15s Car Daily Average Trip Distancevs. Radius of Study Area

Table 3.9: Car Trip Dist4woos

No. City Year Area Radius Dist. DiSt. Pop.Desgn. Pop.Base Area RadiusSj.Ka. Km. Base Desgn. (000) (000) (Base) (Base)

1 Hall 1967 107 5.8 4.15 4.69 372.5 344.9 99 5.622 Belfast 1966 127 6.4 4.653 Tel Aviv 1965 190 T.8 4.09 5.36 1,225.0 817.0 127 6.354 Monroe 1965 200 8.0 4.51 4.29 162.5 96.6 119 6.155 K.L. 1973 337 10.4 5.366 Baton Rouge1965 393 11.2 5.31 7.05 443.0 245.1 217 8.327 Athens 1962 448 11.9 5-57 6.54 2,800.0 1,900.0 304 9.848 Singapore 1968 518 12.8 7.039 West Midl. 1964 970 17.6 5.83 6.28 2,670.9 2,539.0 918 17.1010 Orlando 1965 1400 21.1 6.9211 Springfield11965 2,150 26.2 7.8812 Baltimore 1962 2,220 26.6 9.33 11.40 2,161.0 1,608.0 1,652 22.9313 London 1962 2,450 27-9 7.18 8.97 9,141.0 8,826.6 2,366 27.4414 Bogota 1969 2,520 28.3 6.7615 Copenhagen 1967 2,760 29.6 7.91 9.52 2,077.7 1,707.0 2,268 26.8716 Bangkok 1972 3t100 31.4 7.7017 Washington 1968 39410 32.9 10.5918 Cincinnati 1965 3,495 33.4 8.8519 Tri-State 1964 9,475 54-9 15-9320 Los Angelesl96O 23,300 86.1 13.19

3012 The Rankingof T rips btheir Purposes

Trips are made for certain purposes. If tripmakers have dailymoney and time budgets within which they have to fulfill their daily traveldemand, it should then be expected that they will have to rank their tripsby the value they attach to each purpose.

This assumption was tested for travel behavior in the U.S., whereit was indicated that tripmakers indeed seem to rank their trips by purpose(22)o (No comparable data could yet be found for cities in developingcountries)0

The steps of analysis were as followsg-

(1) The car daily person mobility by purpose (person trips per 100population) versus the car daily trip rates wJere derived from sixrepresentative cities, with comparable data and with a populationrange of 16 million to less than 200 thousand, and a car trip raterange of 2.9 to 4090* The mobility, M, by purpose vs. the car triprate, R 9 were expressed by the following equationsg-

M(home) = 2189 R - 18.23;M(work) = 9.20 R - 2.73;M(personal business) 50250R 7.73;M(shopping) = 10.26 R 18-87;M(soc.rec.) = 12-79 R 27-33;M(serve pass.) = 9.61 R - 22.02;M(other) = 2.71 R - 3.82.

(2) The elasticities of mobility by purpose vs0 the trip rates were thenderived, where elasticity is defined as the percent change in mobilitybrought by a 1 percent change in the trip rate0 The elasticities werecomputed for mid-points of the values0

Figure 3016-presents the results, where the elasticities areplotted against the car trip rates. One possible interpretation of thisdiagram is that when trip rates decrease, such as under congested trafficconditions, the first trips to be cancelled are trips to 'serve passenger',followed by 'social=recreation','shopping', and so on0 Furthermore, themost valued trips are to 'work' and to 'personal business' and 'other'(and, of course, going back 'home')0 These results are in full agreementwith intuitive expectations0

The two most interesting indications that may be inferred fromFigure 3.16 are: (i) the proportions of trip purposes are related totrip rates and may, therefore, be predicted for alternative travelconditions0 This subject is further discussed in the next section;(ii) tripmakers put a value (in relative terms at this stage of analysis)on their trips and, therefore, each trip should also have its own speci-fic total value0 Thus, at least from the tripmakers' subjective pointof view, a certain level of mobility should have a certain level ofvalue0 This subject is discussed in more detail in Section 314.

(*) The six cities areg Tri-State study areas Baltimore, Kansas City,Baton Rouge, Knoxville, Columbia,

- 57 -

10 .

9 Serve Pass.

8

j7Soc. - Oec.

0

Shopping

.~3

'A Per.s. Bus.o Other

Home1 Work

+ Range of Observaitions 4-0 . I I I I

1 2 3 4 5 6 7

Car Trip Rate

FIGURE 3.16: CAR DRIVElt AND PASSENGEl'S INDEX OF

ELASTICITY BY TRIP PURPOSE vs. CARDAILY TRIP RATE

3.13 Proportions of Trip Purposes

It has been indicated in the previous section that different

car trip rates will be associated with different proportions of car

person trip purposes. This section presents such proportions, as

percentages of total trips, both for the previous example from the U.S.

and for other cities with relatively low motorization levels.

Figure 3.17 shows the proportions of trip purposes, based on the

equations detailed in the previous section. It is of interest to note

that the proportions of trips to 'personal business' and 'other' remain

stable along the full range,while the purposes to 'work' and 'home' are

reciprocally related to the remaining purposes. Furthermore, trips to

'home' decline with increasing car trip rates, indicating that the

proportion of 'Non-Home-Based' trips increases with higher car trip rates.

When trying to develop the same relationship for cities with rela-

tively low motorization levels, a serious problem was met: the well-known

problem of standardization, when each study had its o'-m individual defini-

tions. Table 3.10 presents the available data for cities with relatively

low motorization levels, where it can be seen that no two cities have fully

comparable sets of data. Thus, the comparison between cities could be

made for only two purposes, 'work' and 'social-recreation'. The relation-

ship between the above two purposes and the car trip rate are shown in

Figure 3.1.8.

Tt should be noted at this stage that the two diagrams are not

fully comparable; while in the first c.se 'home' is a separate purpose,

in the second case trips are divided into 'home-based' and 'non-home-based'O

Nonetheless, s.nd although Figure 3.18 cannot be compared percentagewise

with Figure 3.17, it becomes apparent that the two comparable purposes

show the same trends in the two diagrams.

Although the above examples are too few to be regarded as con-

clusive, they suggest that travel behavior seems to be basically similar

in different cities, although the amount of travel may be different due

to the effects of exogenous factors, such as motorization levels, system

supply and urban structure.

Table 310: Proportions of Trip Purposes in the Private Mode

Trip~~ Pros a 2 a 3 b | 4 aTrip Purpose _ ~.: Sigpr _ullmu ____.____

San Jose Singapore Kuala lumpxr Tel Aviv

HB: Work 34.6 30.3 30°5 25.4

School 5-9 3.6 17-8 -Pers.. Business) 493.728 09

and/or Shopping) 4.9 3.7 20.8 20.9Soco Recreation 5°3 109 1205 117Other 15..7 14.7 17-5

NHB- 33.6 36.8 18.3 24o5

Total 100.0 lOOO | lOOoO |lOOo.O

Car Trip Rate 3.81 5.23 6-78 7028

(a ) Drivers only; ( b ) Drivers and PassengersD

- 59 -

50

40

Home

30

20

: 20,\WorkSoc. - Roc.Shopping

10 Sorve Pass.

Pero. Bus.Other

O *

2 3 4 5 6 7

Car Trip Rate

FIGURE 3.17: CAR DRIVER AND PASSENGER'S TRIP PURPOSESPLIT vs CAR DAILY TRIP RATE

50 ,

40

30

W rko

520 2 3 4

0

3 4 5 7 9 Car Trip Rate

Figure 3.18: Proportions of Trip Pauposes in.the Private Mode vs. Car TripRate

-60-

3.14 The Perceived Value of Mobility

Referring back to the indication that tripmakers value and ranktheir trips by purpose (Section 3.12), and after seeing that the pro-portions of at least two purposes follow the same trends in cities withboth high and low motorization levels (Section 3.13), an attempt todevelop a scale for the relative value of car mobility is made in thissection.

Figure 3.19 presents an estimation of the perceived value of carmobility by car tripmakers vs. the car trip rate, as derived from theelasticity indices. Table 3.11 presents the computational steps, asfollows: (i) the total mobility at each car trip rate was derivedfrom the equations in Section 3.12; (ii) the elasticity indices ofmobility vs. trip rates were calculated; (iii) the perceived values ofmobility were considered to be inversely proportionpl to the elasticityvalues, since low elasticities represent high perceived values, andvice versa.

The best fit to the data in Table 311 can be expressed as.-

P = 0.819 logR + 0.129 ; (3.3)

where 'P' is the perceived valuie and 'R' is the car trip rate.

Figure 3.19 indicates that an increase in the trip rate increasesthe perceived value of mobility, although in decreasing steps. But themost interesting result is the logarithmic form of the relationship inEq.3.3, which is well known as the law of diminishing marginal utility ineconomics, or the Weber-Fechner law in physiology, where the perceptionis found to be a logarithmic function of the stimulus.

In practical terms, the value of adding one trip to a trip rateof 3 is about twice as high as the value of an additional trip at a triprate of 6.

4n additional test was to find out at what trip rate perceptionwould become elastic, namely the point where tripmakers would becomesignificantly reluctant to decrease the car trip rate any further, Thistest is presented in Figure 3,20, where it becomes evident that thethreshold is about 2,7 daily trips per car, Indeed, the available minimumobserved trip rates to date are in New York (1964) - 2.89, and in London(1971) - 2,80, namely somewhat above this threshold.

Table 3.11s Perceived Relative Value'of Car Mobility U.S.

MobiliyPerceived ValueCar Daily |Tr,ips/10 Elasticity of of Mobility -

Persons i= 1/Elasticity(2.0) (41-3) 2-569 0.3892-5 77.5 2.064 0.4843.0 113.7 1.779 0-562:3-5 1~49,9 1.615 0.6194.0 186.1 1-508 0.6634,,5 222.3 1.434 0.697500 258.5 1.379 0.725

5-5~~ 294-7) 1.336 0.749~6.o33O-9; 1o020768

(Range of (671) 1r235 027(7.0) (403-3) 27078

(Range of trip rate observationsg 2.89 to 4.92)

- 61 -

.90. Y =0.819 Log X + 0.129;u

_

0

,, .7

*.541

CL. .4 ~

1 2 3 4 5 6 7

Car Trip Rate

FIGURE 3.19: CAR DRIVER AND PASSENGER'S PERCEPTION

OF THE RELATIVE VALUE OF TRIPS VS. CAR

DAILY TRIP RATE

1,2

._ 1.0 ----------- _------___ _ -__ ___ ____ ____- _

°1 .6

C

.2 .1 2 3 4 5 6 7

Car Trip Rate

FIGURE 3.20: CAR DRIVER AND PASSENGER'S INDEX OF

ELASTICITY OF THE PERCEIVED VALUE OF

TRIPS vs. CAR DAILY TRIP RATE

-62-

315 The Value of Saved Traveltime

The standard methods for deriving monetary values for traveltimes,especially saved times, are usually indirect ones, where the differentialeffects of times and costs of travel alternatives, with different time and cost

characteristics, are compared with the actual choices made by the tripmakers.For instance. the proportion of tripmakers using toll roads or bridges.

However, extreme difficulties have been encountered in the analysisof the value of traveltime, some of which are:

(1) Traveltime, per se, cannot be saved for la rainy day', but can only bereallocated to other activities during the same day. Thus, the valueof saved traveltime would be affected by the activity for which it wassaved more than by the activity for which it was measured. Nonetheless,the present methods allocate the value of saved traveltime to the trippurpose at which it was measured;

(2) A surprisingly large proportion of tripmakers has been founds time andagain, to be indifferent to the value of traveltime. This observedresult has forced analysts to divide the tripmakers into two differenitgroups, such as 'traders vs. nontraders' or 'time savers vs. moneysavers', and treat each group separately. A behavioral model, however,should consider and treat all people together, since the same personmay switch over from one group into the other for different tripsduring the same day, or on different days;

(3) The concept that traveltime has a monetary value could lead to theconclusion that tripmakers with high incomes should travel less timeduring the day than tripmakers with low incomes0 However, transpor-tation surveys seem to indicate rather the opposite (see Figure 3.14)0

Nonetheless, it should be noted that out of the ma.ny uncertaintiesand contradictions, one trend did seem to emerge3 those tripmakers who werefound to value their saved traveltimes tended to give it a higher value whentravelling to 'work' and 'business' purposes than when travelling to 'shopping'and 'social-recreation' purposes0

The results of the analyses in this paper suggest a.n entirelydifferent interpretation of the process by which tripmakers value theirtraveltimes, as followss-

(a) A tripmaker has not only a certain stable monetary budget for his trips,but also a stable traveltime budget, both of which have to satisfy acertain level of travel demand0 Thus, when his travel demand (i.e.,trip rate) is high, he would have to achieve it within the two con-straining budgets0 It may, therefore, be concluded that the value oftraveltime should be considered within the context of total dailytravel demand and its constraining budgets, and not just with regardto a single trip;

(b) The value of 'saved' traveltime measured for a certain trip purposeshould, therefore, be allocated not to the purpose for which it wasmeasured but to the additional trip purposes that can be achievedwith the saved traveltime during the sa.me dav, and a.crording to the

-63-

preference ladder of trip purposes. Thus, when a tripmaker isinterviewed in the morning, at the start-of his trip rate andthe TT-budget, the value of saved traveltime would be the highestbecause of his need to make more highly-valued trips during themorning, such as to 'business', and not because he then travelsby coincidence to 'work'. Conversely, the lowest value of savedtraveltime would be found during the afternoon and evening, whenhis travel demand for high-valued purposes has already been satis-fied, and only low-valued trips remain.

Thus, the new interpretation of the value of saved traveltimes,based on Figures 3.16 and 3.19, may not only explain the results ofprevious studies - while avoiding the contradictions in the methodo-logies - but may also permit a better understanding of the basicmechanism of travel behavior. This subject is further developed in thenext section.

3.16 Cross-Sectional vs. Temporal Travel Characteristics

All past and present surveys and analyses of the value of savedtraveltimes are based on cross-sectional analysis, as measured at onepoint in time. Nonetheless, the conclusions derived from such surveysare assumed to apply to the future as well. On such assumptions are basedall evaluation procedures of alternative transportation systems and theeconomic justification of the recomnmended plan, where the benefits ofsaved traveltimes (and similar benefits) are compared with the costs ofproviding the improved transportation systems.

However, when travel characteristics are analyzed temporally,over time, they seem to indicate trends which are contrary to the aboveassumptions. Following are preliminary results of a study which, althoughnot yet finalized, is indicative enough to raise doubts about the currentevaluation procedures of alternative transportation plans.

Table 3.12 details results from an on-going study, part of whichhave already been mentioned in the previous sections (23). The data referto the transportation studies in Washington, D.C., and Twin Cities wherethe travel characteristics of tripmakers during the two periods werecompared side by side. The speeds are based on the perceived door-to-doortraveltimes and the distances are airline.

Table 3.12: Travel Characteristics of Tripmakers who made all their DailyTrips by car, Washington, D.C. 1955-68 and Twin Cities 1958-70

Washington, D.C. Twin CitiesCharacteristic 1955 1968 -Diff. 1958 1 1970 Diff. %

Motorization 27.5 39.0 + 41.8 35.6 43.6 + 22.5Speed, kph. 18.83 23.33 + 23.9 21.45 28.51 + 32.9Trip time, hr. 0.35 0.35 o.0 0.32 0.29 - 9.4Daily traveltime, hr. 1.09 1.11 + 1.8 1.14 1.1 3 0.9Daily trip rate 3.07 3.16 + 2.9 3.62 3.84 + 6.1Daily trip distance, am. 6.68 8.21 + 22.9 6.76 8.40 + 24.3Daily distance traveled, km. 20.51 25.94 + 26.5 24.47 32.26 + 31.8

- 64 -

The results in Table 3.2 are quite astonishing in some respects.

as follous

(1) The speeds increased in a remarkable way, by about 24-=28 percents after

the introduction of expressm-ays and additional road improvements 0 None-

thelessp and contrary to the prevailing blit^,.-S traveltimas were not

decreasedD both the trip tima and the total daJly traveltime per trip-

maker remained practically identical3

(2) The trip rates increased only slightly. thus indicating that the base

year rates wvere considered as satisfactory by the tripmakers3

(3) bst of the speed increases vere traded off for longer trips. with a

corresponding explosive dispersion of residence location in the stucr

areas. Indeed. various measures of popualation distributions have

indicated a remarkable amount of dispersion duvinmg the 13'year period.

over and above the expected differential growth.

It may be inferred from the above results that an increase in

travel speed (such as brought by an improved transportation system) may not

ascessarily save traveltimas, in the short run it Day be traded off for a

combination of more and longer trips, while in the long ru it may mostly

be traded off for shifts in reside1nce location. (See also Section 3-7,

Figure 3olO)o ThusS a process of interaction betuyeon travel demamdn system

supply. and urban structure seems to emerge from the temporal analysis0

This process is subject to many constraining factorso such as incoms, travel

cost and time budgetsg mode availability. required trip rates the housingmarket and land use control.

Although the subject of such a transportation land use model is

beyond the scope of this papers it becom3s evident even at this stage that

man eatabliihed axiomss in the transpor-tation field may have to be revlsweds

especially wYith respect to travel demand forecastlng procedures and to

evaluation techniques of alternative transportation planso

Chapter 4 will provide a few examples of the possible impacts of

the new findings on the established proceduresg but first several additional

relationships are to be explained. as preseated in the foilowing sectionso

3017 Travel Bahavior in the Public Trans port ffo

Oomnercial speeds of buses are found to be about 4U-6U percent of

car speeds on the same road netwsork0 When dooroto-door apeeds are considereds

the proportions are even less favorable to buss 0o Comsequentlyg if bus

tripmakers have the same TT-budget as car tripmakersg they will have to

economize on other travel components. by reducing their trip rates or trip

distance,s or both. If,, on the other handg their trip rate has alreacq

reached its minimum possible values of just over 2 t7xpss they would then be

forced to increase their TT-budgets or use another mods of tran-sports or

change their residence locationo Decisions under such conditions are not

easy to makes especially whaen some choices are diamstrically opposed.

- 65 -

Table 3.13 presents preliminary results of travel characteristicsof tripmakers who made all their daily trips by public transport inWashington, D.C., Twin Cities and in all U.S. (23 22). Travel times aredoor-to-door in all cases. Trip distances are airline in Washington andall U.S., and network in Twin Cities.

Table 3.13: Travel Characteristics of Tripmakers who made all their DailyTrips by "Road" Pablic Transport

Washington, D.C. Twin Cities All U.S.-Characteristic 195 1968 19 1970 1970

Speed, kph. 10.70 iO.o4 11.99 12.08 24.59Trip Rate 2.31 2.12 2.12 2.09 2.03Trip Time, hr. 0.55 o.67 0.50 0.55 0.49Trip Distance, km. 5.89 6.77 5.95 6.61 11.97

Daily Traveltime, hr. 1.27 1.43 1.05 1.15 0.99

/1 All U.S. includes interurban travel.

The following indications may be inferred from the above table:

(1) The door-to-door bus speeds in Washington were 0.57 and 0.43 of thecorresponding car speeds in 1955 and 1968 respectively (compare withTable 3.12). Furthermore, bus speeds decreased not only in relativeterms but also in absolute values. In Twin Cities 1970 the ratiowas 0.41.

(2) Although trip distance by buses is shorter than by cars, the low speeds,within the constraining TT-budget, forced the tripmakers to reducetheir trip rate to minimum values, just over 2 trips. Thus, while theTT-budget in Twin Cities and all U.S. are again about one hour (comparewith Table 3.5), the trip rates are 2.09 and 2.03 respectively. InWashington (1955) travel demand was somewhat higher, with a trip rateof 2.31 and, consequently, the TT-budget was 1.27 hours. But therapid expansion of population increased the trip distance, while thebus speeds decreased, with the result that although tripmakers wereforced to decrease their trip rate to 2.12, their TT-budget had toincrease to 1.43 hours (subjective estimation of their totaltraveltime).

(3) It may therefore be inferred that when travel conditions deteriorate,trips are eliminated, with number of trips approaching the minimumvalue of 2.0 trips per tripmaker. Thus, while the critical valuefor car trips was found to be about 2.7 trips, the critical value forpublic transport seems to be about 2.1 trips.

(4) Conversely, when a rapid transit system is introduced it may be deducedthat it could not only attract trips from the private and the 'road'public transport, but it should also induce additional trips becauseof its relatively high speed.

(5) Referring back to Figure 3.19, it may also be inferred that the perceivedvalues of induced public transport trips, at l5O trip rates, should behigher than in the private mode, especially whon the public transporttrip rate seems always to be very near to its critical value.

At this stage the question may be raised why were the bus tripmakersin Washington forced to increase their TT°budget? They could have changedtheir residence location, in order to shorten their trip distances, insteadof increasing it from 5.89 to 6.77 kms. in 13 years0 This question raisesthe problem of 'captive? versus 'choice' public transport tripmakers , asdiscussed in the following section.

3,18 'Captive' vs. 'Choice' Public Tra,nsport T_rIpmakers

The current modal split techniques may be divided into two prin-cipal groups. In the first one the emphasis is put on the aggregated numberof person trips, which are then split into the private and the public modesby considering several factors such as the difference in time and cost betweenthe two modes 0 Behavioral techniques, on the other hand, tend to consider themodal choice from the individual tripmaker's point of view and his decisionson each trip separately, as a sample representing many other such tripmakersand their trips0

The approach presented in this paper is how¢ever somewhat different,in the sense that it considers the decisions of tripQakers as taking placewithin a more general framework, namely within the daigl budgets of triprate, traveltime and travelcost. Hence, if a tripmaker has to conduct acertain number of trips per day, within his constraining budgets, he willconsider all his daily constraints even when deciding on an individual trip0

As an example let us first consider the standard technique wherepopulation is divided into captive and choice transit tripwakers by house-hold characteristics, either before or while conducting modal splito Hence9tripmakers (or, rather, households) who do not own a car are regarded astransit captive, while those wsho own a car are considered as having a choicebetween using the private or the pnblic modes0

Actual observations seem, however, to suggest that such a techniqueis an oversimplification0 Table 314 shows that a high proportion of trip-makers from car owning households also use transit, 20=30 percent, dependingon the average motorization level in the presented cases0 But what is moresurprising is that a significant proportion of tripmakers from householdsowning no car still use car travel during the day, 5-10 percent, dependingon motorization0 An extreme case was recently reported in the Macclesfieldtransportation study, U.K. -"conventional methodology assumes non-car ownerstravel by public transport, but the surveys revealod that up to 514 percentwent by car" (25).

- 67 -

Table 3.14: Households by Combination of Mbde Usage, Washingtonr D.C.1955-68 and Twin Cities 1958-70

Car Mbde Household PercentageAvaila- Combina - Washi ng D.C. Twin Citiesbility tion _ 1955 1966 1958 1970

Available Car only 43.4 56.4 61.3 71.9MExed 25.0 19.3 24.7 17.7Transit )29.5 )22.6 )27.2 )18.9only 4.5 3.3 2.5 1.2

Not Available Car only 4 3 143 3 0 3 4Mlxed 6.1)1. 3.7) 2.656 1.6)-Transit

only 16.7 13.0 5.9 4.2

Total Percent 100.0 100.0 IOU.0 100.0

Total Households 15o, 680 51479 223 366, 511 430, 707HR Motorization o.87 1.13 1.15 1.36

Thus, although the few cases in Table 3.14 do not allow thederivation of a quantified relationship, they indicate a consistent trend:that a significantly high proportion of tripmakers in households owning andnot owning a car use-the opposite mode.

Referring back to the question raised in the previous section, ofwhy the Washington transit tripmakers did not change their residencelocation in order to shorten their trip distances, it now becomes apparentthat a substantial part of them were "residence captive," namely theybelonged to households owning a car which tended to disperse in the areafollowing increases in car speeds. Consequently, although the transit trip-makers were forced to reduce their daily trip rate, they still had to payfor the reduced rate with an increased TT-budget.

It should be emphasized at this stage that even if the aboveindications are found to be correct (and they are still preliminary andfor only two cities in the U.S.), it cannot serve as proof that they wouldalso apply to other cities, especially in developing countries. Nbrespecifically, whatever the division of tripmakers may be, such as to"captive" and "choice" by car availability or residence location, anadditional group may have to be considered in cities of some developingcountries--those "potential" transit tripmakers who cannot even afford topay a bus fare. It would, therefore, be advisable to widen the scope ofanalysis, as presented in this paper, to encompass more cities, especiallyin cities of developing countries.

68 -

3.19 The Interaction of Rapid Transit With Other Modes

As already mentioned in Sections 301-3,h4 rapid transit seems tobe an independent mode, which differs significantly not only from the privatemode but also from 'road' public transport modes. Although the followinganalysis is based on a single case (the only one available), it does indicatethat rapid transit is indeed a unique mode. The analysis is based on thedata from Table 301-302, from which mode elasticities of the expenditure ontravel were derived. The mode expenditure elasticity is defined as thepercent change in the expenditure on travel for a certain mode associatedwith a 1 percent change in income0 The mode elasticities were computed formidpoints of the values0

Figure 3.21 presents the above elasticities for the private andthe bus modes in the U.K. and London, and for the rapid transit mode inLondon0 Before presenting the conclusions that may be derived from thisfigure, it should be noticed that the elasticities are based on travelexpenditures by different income groups, namely, they represent the incomeelasticity of demand for travel by these modes0 In economic termsg if theelasticity is positive, the good being purchased is a normal good; ifnegative, the good is inferior0 Furthermore, a normal good is usuallyconsidered a "luxury" if the elasticity index is above unity, and as a"necessity" if it is below unity. In this case. a Hluxury" elasticity isinterpreted to indicate that there is an exceptionally strong incentive touse a certain mode by an income group, relative to other income groups0

The indications that may be inferred from Figure 3o2l are.

(1) All travel modes are regarded by households as normal goods, worthwhileto be purchased at a price, although at different levels of priority0

(2) There is a very strong incentive for low income groups to use theprivate mode0 For these groups, travel expenditure is a relativelyhigh proportion of income0

(3) There is a low incentive to use the bus mode at all income level6,declining consistently with income, although the bus mode is regardedby all as a necessary mode0

(4) Rapid transit travel is unique in the sense that it does not generatea classical demand curveo there seems to be an incentive to use rapidtransit by all householdsg regardless of their income level0 It may,therefore, be further inferred that the demand for rapid transit maybe stable over time and independent of motorization levelso Indeed,a comparison of travel characteristics in London bette^een 1962 and 1971(see Appendix 4) indicates that as motorization increased by about60 percent, person-trips by road public transport declined about 30percent during 1962=1971, but the number of person trips on the rapidtransit system remained practically the same over the same period0

Additional analysis using cross-elasticities indicates that theprivate mode is a strong substitute for the bus mode0 Furthermore, rapidtransit seems to be a substitute for both the private and the bus modes,although 4 times more so for the latter than for the former0

- 69

a r

4.5'

0

R a p i Transit

0 20 40 60 s0 100

Household Weekly Income, £

Figure 3.21: Elasticity of household weeklyexpenditure on travel, by mode,vs. income, U.K. & London, 1972

In some respects the last result is of particular interest.Standard modal-split procedures do indeed consider rapid transit as astronger competitor for bus travel than for car travel. On the otherhand, when the time factor is considered, the measured substitution effectof rapid transit for the private mode may well be a residual effect only,one which already includes the inhibitory effect of rapid transit onmotorization levels. This possibility may have far-reaching implicationsfor forecasting procedures, since the analysis of cases "with" and "without"rapid transit may have to be based on two different motorization levels,with two different starting numbers for total person trips.

-This possibility emphasizes the danger inherent in the projectionof cross-sectional relationships into the future; the relationships derived

from past and base year travel conditions without a rapid transit system may

not necessarily be applicable for forecasting travel characteristics withsuch a system.

In conclusion, it may be said that although Figure 3.21 is based ononly one case attwo points in time, it strongly indicates that rapid transit

is an entirely different mode from both the private and the road publicmodes at least from the consumers' point of view. Such a result may havefar-reaching implications for transportation planning methodologies, especiallyin cities of developing countries where rapid transit systems are beingconsidered, and it would seem to justify more research on the specificcharacteristics of a rapid transit system and its possible effects on travel

behavior and demand.

70

3.20 Car and Motorccle eOccupanc RatIes

Car daily occupancy rates vary within a vxalatively narrow range.Table 315 details the occupancy rates vs0 motorisation, car trip rateand household size in a selection of cities. The general trend is forthe car occupancy rates to be directly related to car trip rates andhousehold size, and inversely related to iLotorization0

Tcsblo 30158 C Oac t Dtoo

QMh(Dn's IL962 2.16 2,03 totX 3o49Boffota 1969 I-74 2.35 4o5$

Singapore 1968 66 4.O3 5003 3Q,6Bane_'OT_? 1972 Io69 4-2o 3.50 6o3SE! JcOee 1973 l148 4-64 3.81 3Q49Tel &vU:x 1965 i.60 4.85 7o0 3.2i

1973 1.63 7 o7 50 3Caracas 1966 1.66 8.78 490 6.0O

Mall 1967 1.32 12.5 6C'25Rottao3mm 1966 136 13O1 30 5London 1962 1o31 J40o 314London 2971 1.40 22o5 20B301"n .1968 1.44 13.3 3Q90Hamburg I1970 1.40 23-9 4O1'?1esr yoz 1967 1-45 32-7 7BIalt:tmr :1962 1o48 27o2 I,, 2QSpgrimSio1d 1964 1.42 3L.7 A' 430Nomona03 1965 1.41 32,8 33Q7Cinoinnati 1965 L.52 34-8 3-63Baton Ro-ae 1965 1045 35.1 4Q4A1

Car occupancy rates also vary by trip puvposeS as can be Goenin Table 30160 This table i6 of special interost oince it reflects theuse of the car for the daily activities of its trip2akers0o For inotancsetrips to work and business (including non-horue based) have the louestoccupancy rates. while trips that also serve passengers, such as toschool or for social-recreational parposose have th- higheSto

T3bleo 3.168 Cocv OampoE78) I ' lx ap . D

Trip AZ2t3 O -

H.LB. NOKI 1.41 H. .. 1 ~~~~~~~~~~~~~S-Qp = = = =

P.0, 2.03 ftl. Roo ,---,

3(S!,0- 2.97 002ec)2. u g~~~~~~2Qi. = . . Or --- .

1 IL ,Xo ;6M~Te~y~:2AmDw~agG 1.63,

- 71 -

It has been indicated in Section 3.13 that the proportions oftrip purposes depend on the daily car trip rate. It may, therefore, beinferred that forecasts of car occupancy rates in a design year will haveto consider the alternative transportation system plans, since each onemay result in-a different car trip rate.

With respect to motorcycle occupancy rates, they vary even lessthan in the case of cars, within only 1.15-1.25, mostly because of thelimited capacity of motorcycles to carry passengers. Therefore, assumingan average value of 1.20 for a design year will not introduce any signifi-cant error,, especially Wien motorization levels of motorcycles are expectedto decrease with increase in car motorization.

72 -

3,21 Commercial Vehicle Trips

Commercial vehicle travel constitutes a substantial proportion ofthe total vehicular travel in an urban area, espacially when motorizationlevels are relatively low. Thus, the estimation of this component oftotal travel in a design year is important for assessing the travelperformance of the road system.

With respect to the commercial vehicle (henceforth, C/v) triprate, a wide range of values is found, from less than 4 to more than 24trips per C/v per day. The main reason for such a dispersion is theobjective difficulty in defining and linking C/v trips. For instance,should 10 short service trips along one section of a street be consideredas 10 separate trips or 1 linked trip? The problem becomes even morecomplicated when service trips cover many streets at one service run0An additional problem is that a substantial part of C/v trips, especiallytruck trips may take place outside the study area.

The main characteristics of C/v travel may be summarized asfollows:-

(1) The C/v daily trip rate, on the averags, is about 16 tiw3 the cerdaily trip rates

(2) The C/v daily travel time is relatively very short, seldom above 3hours per average C/v per day. It may, therefore, be taken as tuicethe car daily travel time in the relevant study arGas

(3) The proportion of C/v trips to total vehicle tripe (cars plus C/v)can ba related to motorization as detailed in Tablo 3017o Figur3.22 shows the above relationship in a graphical form,,

Tab1e 3178s ProportionB of Comasoial Vohiolo Tzrps

Moo city | Year rJot. C|2' Trigo I/_ Ti a

1 Athenas 1962 201 3049000 0 127000 43190$0 29052 Bogota 1969 2-4 250,O00 19,0o00 3690000 32023 SingpoOO 1968 4-1 334,000 1179821 431,821 27,34 Tel Aviv 1965 4.9 288,580 1189750 407,330 29.25 Ha1l 1967 1205 238,000 69,100 3079 100 22.56 Blfetst 1966 12.8 3619432 1039252 4649684 22.27 Rotterdam 1966 13.1 4379737 106z,052 5439789 19.58 London 1962 14o1 3c,6489742 1g 06294x9 4 7119161 22.69 West Midlands 1964 1504 192199500 34L,749 195619249 2109

10 Pittsburgh 1958 26.9 1D,2569535 229,409 194859944 15411 Baltimoc? 1962 27o2 194259470 378,029 198039499 21o012 Chi±oeo 1956 28 3 499459382 8279590 597729972 34o313 Palas-i 1964 34.2 4329084 639555 4959639 12.814 Baton Rouge 1965 35.2 4009951 81,431 4829382 160915 Winaton=Salem 1965 35o2 2439345 429560 284,905 U-916 Columbia 1965 35-5 329,892 52,201 382,093 130717 Twin Cities 1970 45.4 39,0134148 4899838 395039986 14.0

- 73 -

40 I , ,

r1p X7~~~~~~~~~~~~~~~~~~0.2891

E-4 \ Y= 42.99X

30

.H

o 20

01410 713;

O 10 _

I O . ' ' I ' .

0

0 10 20 30 40 50 60Motorizatilon

Figure 3.22: Proportions of CommercialVehicle Trips vs. M0otorization

3.22 In Gonclusion

To summarize this chapter, it is apparent that the underlyingmechanism of travel behavior, within a given city structure, may be describedin the following way:

(1) Trip makers are constrained in their travel behavior by travelbudgets, such as cost and time, which are a function of theirincome level.

(2) Within these and similar constraints, they must therefore decidehow many trips to make, by what modes, for what purposes, towhat destinations and at what time of day.

(3) Although every trip maker has a certain freedom in his decision,to make individual trips, in the aggregate trip makers' behaviorseems to demonstrate well-known econmic relationships, such asdecreasing marginal utility. This becomes apparent in the tripmakers' subjective valuation of increases in trip rates or tripdistances.

(4) Furthermore, while the prevailing belief that trip makers tendto save travel time may hold for single trips of some trip makers,when trip making behavior is analyzed over time: it becomesapparent that trip makers tend to have a relatively stabledaily travel time and that they prefer to use time savings dueto increases in speed for more and/or longer trips rather than forother activities.

- 74 -

It may therefore be inferred that increases in income wsill allowtrip makers to transfer to modes vith higher speeds, which uill allow11 themmore freedom in increasing their trip rates and/or in choosing their 'oms-work locations. It follows that speed is a key factor in explalimng., andin reshaping travel characteristics. Speed is also one of the very fewfactors that are under the direct control of the planners and decisionAmakersoHence, changing the travel speeds of different modes9 both in absoluts andin relative termsg may be considered as a lever for cha2ging not only travelcharacteristics but also urban structure0 For instance, . -r.absolute speed of cars by the introduction of expressuays m4y result in arapid expansion of city sizes decrease in population density9 and dete-iorationof public transport. Ifs on the other hands the speed gap betw¢een the publicand the private modes is narrowed9 for examples by the introduction of a rapidtransit system9 the city may expand at a slover rate. into a mora coTpact formsand with less polarization betueen car-ow-ming and non-caar_-owning households0

As can be seen from the above considerations9 travel besharior andsystem supply seem to be closely intertwined with urban structurea This subjectwill be further pursued in the next chapter0

In closing this chapter it should be msntioned once ageai that theprincipal purpose of this paper is to compare travel characteristics in citiesof developing and developed countries. in order to identify trends that aresimilar and isolate tendencies wshich are specific to each group0 It mey nowJbe concluded that the only significant difference in travel behavior betweenthe two city groups seems to be for daily average car travel time0 Ho,.everqeven this difference emerges indirectly. since no informiiaticn is ,tavailable on the daily travel times of tr _makers in cities of devrelopingcountries. For all other relationships. the cities of the ti-wo groups seemeither to be mingled in the scatter diagram or to be arrayed in a consistentway9 w-ith no specific threshold separating them0 It m2ay., tharefozy- bet"ferred at this stage that the mechanism of travel bahavior seems to bebasically the same in all urban areas. whatever their size and rtC'lco

Chiapter 4: The City and its Mbbility

Introduction

A city may be regarded as analogous to a living organism by atleast three principal criteria. First, the larger the organism, the greateris the differentiation between specialization of its cells. In a city thisis reflected in the ever increasing variety of professions practiced by itsinhabitants and the complexity of their specialized activities. Secondly,the development level of an organism may be measured by the intricacy of itsnervous system: a rudimentary network, then ganglia centers, and finally acentralized brain coordinating all activities develops. In a city this isreflected in the evolution from a simple network of communication lines, suchas telephone and telex, to the current "ganglia" of municipal and privatecomputers regulating the city's activities. Thirdly, the higher the activitylevel of the organism, the higher is the velocity of flow within it, such asthe flow of blood. In a city this is reflected in the speed of traffic flow.

When cities are assessed by the above analogy, it becomes apparentthat cities are still on a very low level of development as living organisms.Nonetheless, many cities have passed a significant threshold in their develop-ment during the last several decades, during which their size, cormunicationnetworks and speed of travel have increased at a rapid pace. Of special con-cern in this paper is the speed of travel, which can either assist or inhibita city in its growth, depending on the ratio between the speed and the size ofthe city. Hence, the larger tIe city, the higher must the speed of travel beif a certain level of mobilityY is to be maintained.

The first section in this chapter presents an example of the aboveconclusion, where it is shown that the arterial road density must increase ifa given trip rate is to be maintained in a city of increasing size.

The second section discusses the effects of motorization levels andcar trip rates on the population's total mobility in a city, as well as theadded effect of a rapid transit system.

The chapter concludes with some reflections on city structure and itsdependence on travel behavior and transportation system supply, as inferredfrom an ongoing study on this subject.

1/ Mobility in this chapter refers to She daily number of internal trips byall motorized modes per 100 persons.

76 -

4!.1~ Effects of' City Size, Population Density and Motorization onRoad Density

So far, the analysis in this paper has considered separate relation-ships, each limited to only one independent variable at a time. In thissection an attempt will be made to combine several separate relationships,in order to test the possible implications of their interactiono Theframework for their interaction will be the city9 under alternative assump-tions about size, density and motorization.

The separate relationships considered are as follows.

(1) The average vehicle trip distance increases as a function ofcity size - Eq. 3.19 page 549

(2) The average speed of traffic is a function of the arterialroad length and the vehicle-hours of travel Eqo 2.69 page 30,'

(3) The average daily vehicle trip rate, R. is a function of thedaily travel time9 h, the speed9 v. and the trip distance, d,as expressed in the relationship: R = h v/do It is assumedthat h = 1.20 hr. (See Table 3.6)

Figure 401 expresses the interrelationships between the aboverelationships9 showing the arterial road density required to maintain a cardaily trip rate of 5 under alternative city size9 density and motorizationlevels.

The results are sumnarized below.

Average Trip Distance:

When a city grows in population and size9 average trip distanceslengthen. The analysis indicates that for a given population density9 tripdistances increase roughly in proportion to the square root of the radius ofthe city. For instance9 a growth in city population, say from 1 to 29 4 and 8million9 with population density remaining constant9 may be expected to increaseaverage trip distance by roughly 20, 40 and 65 percent respectively, over theinitial level,

Decreasing population density tends to increase trip distance, sincea lower density implies a larger area for the same population0 Thus' if twocities have the same population, say 2 million, but one has a population densityof 10,000 persons per square kilometer, while the other has a density of 590009the average trip distance in the latter case will be found to be longer by about20 percent than in the first case.

When cities grow in population, population densities tend after a timeto decreaseo Thusg the adverse effects of increase in population and decreasein density tend to cumulate. For instance, if a city grows from 2 to 4 millionwhile its population density decreases from 10,000 to 890009 the average tripdistance may be expected to increase by about 25 percent. If an average car in

- 77 -

15 1,0000

o ./7~~~~~~~~~~~~~~~~~~

15 2,500

10 14000

..o6

0

S0 15 5,000

p40)4 tO~~~~~~~~~~~~~~~~~~1 2.500

15 7.500

15 10.000

5 1,000

)-2rr3 10 Pu i D t

10 7.500

od 2 250

5 5,000

1 ~~~~~~~~~~~~~~~~~~~~~5 7,5005 10.000

00 1 2 3 4 5 6 7Population ,Million

Figure 4.1: Arterial Road. Density by Moto-rization and Population Densityvs. City Size (for a vehicletrip rate averaging 5 trips perda,y)

78 -

the first case makes five trips during its daily travel time of 1.2 hours

at a speed of about 19 kph, the same car will probably have to travel at

a speed of about 24 kph, or an increase of about 25 percent9 in order to

make the same number of trips in the second case. Furthermore, the same

vehicle in a city of 8 million with a population density of 5,000 will needa speed of about 31 kph to achieve the same trip rate, or an increase of

about 63 percent compared with the initial speed. This explains why travel

speeds have to increase in expanding cities if vehicle trip rates are to be

maintained within the same travel time per vehicle per day.

The Effect of Speed on the Need for More Roads

For a given flow of vehicles, higher speeds require more (and/or

better) roads. By considering the relationship between flow, speed and

arterial roads for the previous example, it can be shown that while the ratio

of road length to population in a city of 1 million is likely to be about

00 40 kilometers per 10,000 people for a population density of 10,000 and amotorization level of 5 cars per 100 people, the ratio of road length per 10,000people is likely to increase to about 1048 in a city with 8 million andpopulation density of 5,000 per square kilometer, even if the level of motor-

ization remains the same. Some 307 times more roads per inhabitant will be

required to keep the same level of mobility at the same level of motorization0This appears to be one explanation of the increasing difficulties in providing

adequate mobility in larger cities, particularly those that expand rapidly0

The Effect of Motorization

When the factor of motorization is added, travel conditions are

aggravated even further since more cars per hundred population will require

more roads if speeds are to be maintained within the same travel period.

Thus, under the conditions of this example, if motorization levels

were to increase from 5 to 10 cars per 100 inhabitants, the arterial road

density would have to be increased from 0040 to 2.92, a sevenfold increase0

Conclusions

Very high densities of arterial roads are inpractical since roads will

become too closely spaced to allow efficient land use development 0 Furthermore,traffic speeds on arterials cannot be increased aboveg say 50 kphg by theaddition of further arterials because of operational and safety constraints0There is therefore a practical limit to the extent to which building additional

arterial roads can increase speeds. Beyond a certain city size (depending on

population density and motorization level), it becomes necessary to use high-

speed roads such as expressways and/or reserved right of way for railways or

busways to maintain the same level of mobility as the city grows.

- 79

4.2 The City and Its Mobility

Following the theoretical exercise of travel conditions in expanding

cities presented in the previous section, this section presents the actual

mobility conditions in a selection of cities. Figure 4.2, based on Appendix 4,indicates that the total daily mobility of population in a city can be related

to the motorization level and the car daily trip rate.

The effect of a motorization increase on mobility, at a given car

trip rate, is a complex one: While motorization increases, public transportloses passengers; however, the higher trip rate in private transport results

in an incremental increase in mobility.

The effect of a car trip rate increase on mobility, at a given

motorization level, is more simple, since these two parameters are directly

related. However, it should be noted that two different reasons may underlie

a high car daily trip rate: either the city is small in size, or the speed

is high in a large city. This result is in line with the exercise in the

previous section, where it was shown that when a certain car trip rate is tobe maintained in an expanding city, more roads have to be added in order to

increase the travel speed. However, there is a practical limit to the additionof arterials especially in cities of developing countries, where population

densities are usually high. Referring back to Figure 2.1, it can be.seen that

arterial density in such cities barely reaches 3 km. per 10,000 population.Transferring this value to Figure 4.1 indicates, therefore, the threshold of

arterial density above which expressways must be added if a car trip rate of

5 is to be maintained in an expanding city at different population densitiesand car motorizations. Conversely,~ if expressways are not added, and speeds

are not increased, the expected result is a decline in both the car trip rate

and in total mobility.

At this stage, however, we must ask whether we are interested in-high

private transport mobility or high total population mobility. The answer, bypractically all criteria, is that total mobility is of foremost importance,especially in cities of developing countries, where most of the-population does

not own a private vehicle. In such a case, then, it is the speed of publictransport that should be increased, either by giving preferential treatment to

buses and minibuses,.or. by introducing a rapid transit system on its own right

of way. The effect of the latter on total mobility can be seen in.Figure 4.2,

where cities with rapid transit systems have a significantly'higher total

mobility than expected.on the basis of their motorization.levels and/or car trip.

rates. A striking example is the comparison between Hamburg and Cincinnati(cases 23 and 19 respectively in Figure 4.2); although motorization in Cincinnatiis greater by about fifty percent than in Hamburg (population sizes and car trip

rates are similar) the effect of the rapid transit system in Hamburg is toincrease Hamburg's total mobility over that of Cincinnati.

When considering the speeds of both private and public modes, and their

effects on private and public mobilities as well on overall mobility.in a city,it becomes evident that a so-called 'balanced' transportation plan will be onein which proportion of expressways to rapid transit lines, and their locationsin a given city, are designed so the modes complement each other rather than

- 80 -

0

p400

Fisurxe 4028 Total Daily IEcbility )g 1btoc

rization vs0 C&r Trip Rate(flashed lines represent addedmobility kbr rapid transit)

compete with each other0 Although there are no ready-made directivesf or achieving such a balanced transportation plan, it is hopad that therelationships described in thjis report may assist both the planner and theevaluator in assessing the recoimmended plan and its alternatives0

At this stage, it becomes important to mention the fact that most,if not all, of the standard transportation planning methodologies regard thecity structure as static for the testing of alternative transportation systems0However, it has already been recognized that there is a close interactionbetween the two, ~and that each both affects and is affected by the other0 Thissubject is discussed in more detail in the following section0

4.3 The City Structure

Although the study of city structure is a discipline in itself,it is believed that a few reflections on city structure and transport shouldbe included in this report. The following comments are preliminary, sincethey are based on an on-going study on the subject of city structure and

mobility.

When the spatial distributions of population and employment in acity are compared with each other, starting from the fringe and movingthrough concentric rings towards the center, it is indicated that thereis an accumulation of incremental differences between the distributions ofresidence and employment, with the result that an ever increasing proportionof workers has to travel towards the center in order to find employment.However, at the center itself the differential accumulation drops to zero,since the numbers of workers and employment spaces over all the urban areaare equal (assuming that the urban area has been defined as a self-containedunit).

When such differential accumulation curves from different cities arecompared, the evidence suggests;

(i) The proportion of workers who travel to work across zonesdecreases with increasing size of the urban area. In otherwords, the spatial distributions of population and employ-ment tend to be more even with increasing city size.

(ii) The proportion of workers who travel to work across zonesincreases with increasing motorization and/or speed oftravel. In other words, the difference between the spatialdistribution of population and employment tends to become greater.

These two trends, which are observed to different degrees in differentcities, depending on their spatial size and motorization level, are consistentwith - and corroborate - the indication that the tripmakersl travel behavioris constrained by travel budgets for both cost and time. Thus, increases inspeed will be used to make longer trips resulting in population dispersion andin increasing distances between residence and employment locations. If,however, speed subsequently decreases due to traffic congestion, employment-will begin to follow population movement, in order to reduce trip distances,thus changing a mono-nucleated city into a multi-nucleated one.

A further check shows that the 'distance between the centroid ofthe differential accumulation ourve and the urban center match surprisinglywell with the trip distance derived from standard transportation studies,over a wide range of cities in both developed and developing countries,although the two distances are defined and measured entirely differently.

It may also be noted that the description of the differential accumulationcurves by a Gamma function allows the ranking of all tested cities in a quantifiedand consistent way.

- 82 -

Returning to the subject of the interaction between travelbehavior, transportation system supply and urban structure, it becomesapparent once again that the speed of travel, given a stable daily traveltime budget, becomes a powerful force in shaping and changing urbanstructure, not always in the desired directions. For instance, increasingthe speed of private transport alone may result in an increasing spatialpolarization of households owning and not owning private vehicles.

Another disturbing phenomenon is now becoming apparent in rapidlygrowing cities in developing countries, where the major part of city growthoccurs through the in-migration of poor newrcomers, who cannot afford to payrent and, therefore, settle at the fringes of the urban area. As a result,their trip distances (and costs, in money and time) are prohibitively highand their employment opportunities restricted. The problem, then, is howto plan new employment centers and/or transportation systems that willminimize the need and cost of travel for these groups, especially the tripto work. It is hoped that the on-going study mentioned above will be ableto contribute to the solution of this problem.

In conclusion it may be said that the need to solve transportationproblems in cities of developing countries, coupled with increasing trans-portation difficulties in cities of developed countries, has necessitated acomplete review of the well recognized and established methodologies oftransportation analysis and planning. It is hoped that this report 1illmake some contribution to the present search, in many countries, for a betterunderstanding of the mechanism of travel behavior and the interaction betweentravel demand,transportation system supply and urban structure.

- 83 -

REF BE R E x C IS

1. 'Aianai4jis of some vworld transport statistios' AeJL Telpul, ReportNo. 622, Transport and Road Research Laboratory (TfL), U.K. 1974.

2. 'Aiitomob-le faots & figures', Automobile Manufaoturer Anssoiatioa,nno.,U.S., 1966 Edition.

3. 'Forecasts of vehiole and traffic in Great Britains 1974 revision',J.C. Tanner, Report 650, TRRL, U.K., 1974.

4. 'Urban Transport Sector Policy Paper', The Worll Back, Washingtont D.C.t

1975.

5. 'Research on road Traffio', lSmO U.K., 1965.

6. 'Tel Aviv - Yafo transportation master plan', Kolin & Zahavi, 1968.

7. 'Urban transport policy and planning study for metropolitan KualaLjampur', Wilbtr Smith & Associateos in association with Lloeelyn-Davies Weeks Forestier-Walker & Bor and the WVY Group, 1974.

8. 'Rotterdam-Rijnuond land use and transportation stua4', Freeman, lox,Wilbar Saith & Assoo., in association with XMnagement Scionoes Ltdb1970-

9. 'London traffio survey', Freeman, Fox, Wilbur Smith & Assoo., 1964.

10. 'Transportation and eoonomic opportunity', The Regional Plan Association,The Transportation Administration of the City of eow Tork, 1972.

11. 'Nationwide personal transportation studc', U.S. Department of Trans-portation, Federal Highway Administrationp Washington, D.C.

12. 'The urban renewal & development project, Singapore (1968)', CrooksMiohell Peacock Stewartp 1971; and 'Singapore mass transit xtudyl-phase 1' (1972), Wil1ar Smith & Assoc., in association with ParsonsBrinokerfoff-Tudor-Beohtelp 1974.

13. 'Motor vehicles, 1973', Speoial report, Central Breau of StatistioatJerusalem, 1974.

14. 'Bangkok transportation study', F.HL Kookx KG with Rhein-Buhr Ing.OMB39Ihsseldorf, 1975.

15. '1973/74 Automobile faots & figures', Motor Vehicle ManefaoturersAssociation of the United States, Inc., 1975.

16. 'Statistics of urban transport' International Union of Publio Transport,Bruxelles, 1967 & 1975.

17. 'Traffic performance evaluation of road networks IW the Alpha-relation-ship', Y.Zahaviv Traffio Engineering & Control, U.L, Sept./Oot. 1972.

18. 'Family expenditure survey', Report for 1972, MSC, U.K.

19. 'Family expenditure surwey', London 1972.

20. 'A report on the traffio implications of the Viotoria Llne North ofViotoria', London Transport, London 1973.

21 'M0 e TZ-ov atxBiogs a mifed t o _

Y. zsvi Wafia MignoOC%n & CanE-3D UoZo9 .v .',O| ;..93

22. OT=v2elt:PS 7mdgS en& soblityA iRa u2av> e_s25e_~ Z-_&A U. gS.D>p;EEm2n of T Gpogtio9n X1> i_h4

23a °t;ItIg. roviw of tho of 2gtr3o l S2 ' - ro

fox' 1955 and 1968%9 Ygo,Xc0i in o: < ?

nnd Aso6oo Incp U.S. Popetmz o:O Tmmopiortt9o EMY4 eLD.0 C0. 1975.

24. 'Vrxis.oms In traoGl boft5en individ&13 livin g oP duggo2?tpopulatioa densit°y (db)5, PB0B Goo q GOatoR L Coamod 9Loano 9 1975-

25. 0Traffic Engineoeing & Contlo1%g peso 2379 * 1 975o

26. 'Athens Basin Transportation Survey and Study', W tilbur Smith and Associates,1962.

Appendix li Percent of HOuseholds owning a Car by Income Groups

City Group| Au Inoome E,%

Tel Aviv 1 0 - 3,000 0.61965 2 3,000 - 4,200 2.1(I1.) 3 4t200 - 5,400 6.1

4 5,400 - 7,200 15.05 7,200 - 9,000 26.06 9,000 - 12,000 32.27 12,000 - 18,000 59.88 18,000 & over 67.2

Kuala iumpur 1 0 - 200 4.81973 2 200 - 350 12.0(8) 3 350 - 500 30.2

4 500 - 700 49-55 700 - goo 64.46 900 - 1,200 68.07 1,200 - 1,500 76-58 1,500 - 2,000 92.19 2,000 - 2,500 92.3

10 2,500 & over 95.1

Rotterdam 1 O - 6,000 5.91966 2 6,000 - 10,000 29.9

(Guilders) 3 10,000 - 12,000 48.54 12,000 - 18,000 66.85 18,000 - 24,000 81.0.6 24,000 & over 87.6

.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

London 1 0 - 500 101961 2 500 - 19000 26(e) 3 1,000 - '4500 50

4 1,500 - 4g000 675 2,000 - 3,000 80

3,000 & over 90New York City Region Outside

New York 1 0- 2,000 7 311963 2 2,000 - 3,000 12 44($) 3 3t000 - 4,000 16 50

4 4,000 - 5,000 28 715 5,000 - 6,000 31 846 6,000 - 7,500 55 917 7,500 - 10,000 67 958 10,000 - 15,000 72 989 15,000 & over 73 99

All U.S. 1 0 - 3,000 36.91970 2 3,000 - 4,000 65.2(8) 3 4,000 - 5,000 75.0

4 5,000 - 6,000 83.25 6,000 - 7,500 90.96 7,500 - 10,000 95.1-7 10,000 - 15,000 97.28 15,000 & over 98.8

a O0 0 00 0 00 0 0 00o C i11 ase c CM aO 0C _v >10 0 0 o o o o o o o

0 00 0 0 0 0 o o o o;,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- CM en 0 2-1 CwM r ' L-

00a0 0 000 0 0 000 0 00 00 0

0 ~ ~ ~ ~a 0000 0 00 0000 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - en.\_ l. ._

0000 0 CO v0 000f Z?

(D z O O O O Oo cm 0 E! V- % O Lno %o T O

I 0 0 0 00 c l 0 0 c000 0 0 0 000a%. mn k nc ~ , M

o z1~~~~G %o o 2n f 0 - " 06 G%S .Oe tHo3 Lf cm -Oo> V43 a 0 00 0c,

e3 R I e <x6xcAtcz % n¢<>H ow me V) Et~~~~~~~~~~~~~cm rn \o %

m: C I o o o 0%o o o c en CD o o- o of U o o o 0 o o 24 co 0 nt rc G\ co@ 0

0000000 00 0 0 0 00 0 0 . 0 0 0 c 0 C O

O -1 * G- cm CmJ\% s0 tE t e 0 l \ M m PI

i ~~~~~- en 6XCD L5t c O l)p tn 0 cm tCl en oL S %Oko 0\ rCU eS -O en> \0 r-j rI >1 t. tAv 0> t ZM

0000000 000000 000000 0000 q00 0 0 -00 r - 0 0 0 0000ro

en o NW a F aaoSHN nNwe

g @ S _~~~~~~~Oe~~~~~~~~~~~~~~\ \0 i

I $p OOOCOP 000 OOOfC<:S OSIlu-

PF 0

- 87 _

Appendix 3: Proportions of Motorcyoles

Figure 1.12-1: Notoroyoles in Israel

Year Car Mot. x/C Mot. year Car Mot. M/C Mot.

1951 0.70 0.58 1963 2.01 1.2852 - - 64 2.50 1-4453 0.74 0.60 65 2.90 1.59

54 0.78 0.62 66 3.34 1.4955 0.82 0.63 67 3.58 1.5256 0.88 0.65 68 3.86 1.4757 0.94 0.70 69 4.50 1.4258 - - 1970 4.93 1.4059 1.01 0.82 71 5.53 1.35

1960 1.15 0.88 72 6.39 1.2761 1.37 1.02 73 7.26 1.1362 1.68 1.11 _

Figure 1.12-2: N/C Notorization vs. Car Motorization - Israel

Year Car Mot. N/C Mot. M/C % Change in M/C Not., %

1959 1.01 0.82 81.11960 1.15 0.88 76.5 + 7.3

61 1.37 1.02 74-5 + 8.862 1.68 1.11 66.1 + 15.363 2.01 1.28 63.7 + 12-564 2.50 1.44 57.6 + 12.565 2.90 1-59 54.8 -1062466 6.34 1.49 44.6 - 2.067 3.58 1.52 42-5 - 3.68 3.86 1.47 38M1 - 3.469 4.50 1.42 31.6 - 1.4

1970 4.93 1.40 28.471 5'53 1.35 24.4 - 3.672 6.39 1.27 19.9 5-973 7.26 1.13 15.6 - 11.0

Figure 1.12-3: M/C Motorizsation in Bangkok and Tel Aviv

Ban kok - Tel Aviv

Year Car Mot. N/C Mot.,% Change, % Year Car Not. N/C Mot. M/C, % Change, %

1966 2.0 57.4 OO 1961 3.15 1.80 57.1

1967 2.5 57.3 1.9 1962 3.79 1.96 51.7 - 581968 2.9 58.4 - 18.8 1963 4.50 2.19 48.7 - 15.

1969 3.5 47.4 0'0 1964 5.37 2.33 43.4 4 0

1970 4.0 47.4 - 8.2 1965 6.23 2.59 41.6 41971 4.3 43.5 1.41972 4.6 42.9 - .4 _

pp3pandi 3 (Cont)

aaz& 1012°5/68 PrXoogiono og Emseelacl d-AQ en-ai1lMY :EO9E G2FOUE>

K- ,la IP 1973 -v _________

I1comj E with Cez , BE Th WCq IMWOM m i 95 m El/a,

1 2303 7607 X 2302 76092 308 69.2 2 390- 6O033 55A4 446 3 49.o 50oS4 7208 2702 4 700 30.0)5 810l 18.9 5 02o7 28.16 8500 25.0 6 N-L 53C7 8309 16.1 97 0 30(8 9804 106 8 o00 O9 1OOoO

10 9800 2~0)

- 89 -

Appendix 43 Modal Split

Remarks&

1. The data were derived from different souroes. Blanks in the followingtables were caused by unavailable data.

2. All trips refer to internal trips by the population within the studyarea.

3. In oities 21-25 the upper number 1,000,000) refers to publio transporttrips on the road network only, while the lower number 2,000,000)

refers to all public transport trips, including rapid transit.

Table As Basic Data

No. City Year Population Car V Private o t,o r i z

1 Athens 1962 1,goo,ooo 39,000 2.12 Bogota 1969 2,339,560 55,000 2.43 San Jose 1973 656,670 30,465 4.64 Bangkok 1972 4,060,700 175,000 250,000 4.3 6.15 Singapore 1968 1,536,000 62,385 98,923 4.1 6.46 Tel Aviv 1965 817,000 39,640 58,700 4.9 7.27 Caraoas 1966 1,719,030 151,000 8.88 K.I. 1973 912,490 65,435 111,300 7.2 12.29 Bill 1967 344,890 43,185 12.5

10 EsBen 1965 730,ooo 13.011 Rotterdam 1966 1,060,000 139,000 13.112 Mulheim 1964 190,ooo 14.413 Wuxppertal 1964 420,000 16.114 Ramm 1965 74,000 18.215 KrefeliL 1968 224,000 19.516 Baltimore 1962 1,607,980 437,540 27.217 Springfield 1964 532,188 168,634 36.718 Monroe 1965 96,530 31,648 32.819 Cinoinnati 1965 1,391,869 484,770 34.820 Baton Rouge 1965 245,0G76 86,116 35.2

21 London 1962 8,857,OOO 1,249,450 14.121' London 1971 8,372,000 1,885,700 22.522 Berlin W. 1968 2,149,680 328,670 15.323 Hamburg 1970 1,790,000 427,810 23.924 Philadelphia 1960 3,812,460 1,o87,900 28.525 New York 1967 19,113,600 6,250,150 32.7

(Contd.)

- 9o -

Table B: TripB and Modal Splits

Private Private Publio Total Priv. Pabl.

No. City Car Trips- Vehiole Person Transp. Person Modal Trip_________ Trips Trips Trips Trips Split Rate

1 Athens 304,000 656,640 2,060,ooo 2,716,640 24.2 1.082 Bogota. 250, 000 435,000 2,169,000 2,604,ooo 167 0.913 San Jose 116,116 172,144 555,205 727,349 2347 0.854 Bangkok 612,500 875,000 1,367,000 3,131,ooo 4,498,000 30.4 04775 Singapore 314,000 497,000 773,000 1,377,000 2l50, 000 36.0 0.906 Tel Aviv 288,580 379,690 531,720 833,400 1,365,120 39.0 1.027 Caraoa. 740, 330 1,228,Q40 1,316,717 2,244,757 48.3 0.778 LKL. 443,949 647,822 960,038 629,674 1,589,712 60.4 0.699 hill 238,OOO 314,000 184,000 498,000 63.1 0.53

10 Essen 54.011 Rotterdam 437,737 595,362 339,340 934,702 63.7 0.3212 Malheim 60.013 Wuppertal 60.014Maym 70.015 Krefeld 75.016 Baltimore 1,425,470 2,112,613 2,599,426 2,599,426 81.3 0.3017 Springfield 725,300 1,030,592 51,358 1,081,950 95.3 0.1018 Monroe 183,196 258, 794 28,833 287,627 90.0 0.3019 Cinoinnati 1,898,571 2, 886,617 181,068 3,067,685 94.1 0.1320 Baton Rouge 400,951 580,539 48,593 629,132 92.3 0o.20

21 London 4,119,000 5,411,000 4 039 330 9 450,330 57.3 0.465,873,330 11,284,330 48.0 0.66

21' London 5,280,000 7,377,000 3,194,330 10,471,330 70.4 0.375,119,330 12,496,330 59.0 0.61

22 Berlin 1 281,810 1845800 1,275,000 3,120,800 59.1 0.591,896,500 3,742,300 49.3, 0.88

23 Hamburg 1 786,ooo 2,500t000 615,050 3,115,000 80.3 0.341,786,000 ~~~1,500,000 4,O000000 62.5 0.89

24 Philadelph. 4,308,747 6,476,716 1 093,210 7 569,926 85.6 0.291,568,769 8,045,485 805- 0.41

25 New York 18,750,450 27,188,150 4,600,000 31,788,150 85.5 0.2419,707,0001,36,895,150 73.7 10.51

- 91 -

Appendix 5s Car Daily Traveltimes in Seleoted Cities

No. City Year Population Car Car Dail3y Car Daily Car TripMot. Traveltime Trip Rate Time,min.

_ _ _ _ _ _ _ _ _ _ _b hr.1 Athens 1962 1,900,000 2.05 1.38 7.79 10.62 Bogota 1969 2,339,560 2.35 1.37 4.55 18.03 Singapore 1968 1,536,000 4495 1.10 5-03 12.74 Bangkok 1972 4,06T,900 4.3V 1.27 3.50 21.85 San Jose 1973 656,670 4.64 1.27 3.81 20.06 Tel Aviv 1965 817,000 4.85 1.10 7.28 9.1

7 Kuala Imnpur 1973 912,490 7.17 1.40 6.78 12.48 Caraoas 1966 1,719,030 8.78 1.21 4.90 14.89 Bull 1967 344,890 12.5 0.72 6.25 6.910 Belfast 1966 504,620 12.8 0.81 5.63 i8.6

11 London 1961 8,826,620 14.1 0.75 3.27 13.712 West Midlands 1964 2,529,010 15.4 0.62 3.59 10.313 Copenhagen 1967 1,707,000 20.1 0.74 4.21 10.514 Tri'State 1964 16,303,000 25.7 0-971 2.89 20.115 Baltimore 1962 1,607,980 27.2 0.67 3.26 12.3

16 Monroe 1965 96,530 32.8 0.71 5-79 7.317 Cinoinnati 1965 1,391,870 34.8 0.83 3.63 13.718 Orlando 1965 355,620 38.6 0.70 4.33 9.719 Washington 1968 2,562,030 39.8 0.85 3.28 15.620 Los Angeles 1960 7,595,830 41.1 0.80 3.66 13.1

(1) Inoluding intra-eonal trips

Traveltime, hr. Motorization

city Base Design Design

Athens 1.38 0.70 9.6Bill 0.72 0.72 26.1London 0.75 0.85 27.0West Midllandi 0.62 0.78 30.8

- 92

Appendix 6 A Procedure for Macro-Assignment on a Road System

In Chapter 2 it was shown that traffic interacts with the roadsystem in predictable ways. This principle is the basis for all sophisticatedcapacity restraint traffic assignment techniques. However, current standardtechniques of traffic assignment require lengthy and costly processing beforedelivering information on the total and proportional vehicle-kilometers oftravel and speeds on the various components of the road netwTork, such asarterials and expressways. In this appendix we describe a simplified procedurewhich, although it uses a macro approach at this stage, can provide a firstapproximation of total and proportional vehicle-kilometers and speeds onarterials and expressways under alternative assumptions.

The following example is based on data from the Athens transportationstudy (1962) (26), as forecast for 1980 The data in Table 1 sumarize therelevant information on the planned road network and the traffic assigned toit. Alpha is the product of, the average flow and the average speed.

Table 18 Basic Data

Art;31acS 594509000 458 llv9O 3600 I51LD390 428 4CO_pr_s__a__y 59800OOo0 96 60A4V7 65 0 8 B9D230 39927D080

________ _ I 11D 25__ _ _0_ _ | 4608 | I _ _Referring back to Eq 0 2-5, it may be concluded that the vehicle-hours

of travel on arterials and expressways can be expressed as:

La Xa 196,2079200Ha 2 (6.1)

a ~~~a

Le W e 376,9999,680He 2 V2 (6.2)

a a

This procedure, based on the assumption that daily vehicle travel time remainsstable under various traffic conditions (section 3-7), is detailed in Table 2,as follows:

(1) Assume a speed for the arterial ietwork,

(2) Derive from Eq0 401 the vehicle-hours on arterials, Ha'

(3) Deduct Ha from the total H in order to derive the vehiclehours of travel on expressways, He

(4) Calculate the estimated speed on expressuays by Eq0 402;

(5) Calculate the vehicle-kilometers on arterials and expressways,as the product of speed and vehicle=hours;

(6) Calculate the total average speed, as the quotient of totalvehicle-kilometers and total vehicle-hourso

- 93 -

Daily Avg. Speed on the Exp. Network, kph.30 40 50 60 70 to 00

240 I I

0 0I o

200

0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

30 40e so 00 70' 6o t

Daily Average Speed on the Art. Network,kph

- gure 1: Assignment of Vehicle Daily-Kilometrage on Arterial andExpressway Networks(An Example, Athens - 1962)

Table 2: Assignment

Va Ha He ve Ka Ke K v % K o0

25 (313,932) _30 218,008 22,612 129.1 6 540,240 2,919,209 9,459,449 39.3 30.932 191,609 49,011 87-7 6,131,488 4,298,265 10.429,752 43.3 41.234 169,729 70 891 72,9 5,77 9755 5 167,954 10,93 0739 45-5 47.236 151,394 89,226 65.0 5,450,184 5,7999690 11,249,874 46.8 51.638 135,878 1Q 4742 60.0 5,163.364 6.284.520 11.447.884 47.6 .940 122 630 117,990 56.5 4,905,200 6,666,435 11,571,635 48.157645 96,892 143,728 51.2 4,360,140 7,358,874 11,719,013 48.7 62.850 78,483 162,137 48.2 3,9249150 7,815,003 11,7399153 48.8 66.655 64,862 175,758 46.3 3,567,410 8,137,595 11,705,005 48.6 69-560 54,502 186,118 45.0 3,270,120 8,375,310 11,645,430 48.4 71.9

- 94 -

When the procedure is applied to a range of assumed speeds on thearterial network, it becomes evident that there is a relatively narrow rangeof speeds on both arterials and expressways that can be regarded as reasonable

in the light of past experience. This range is shown in Table 2 andFigure 10 The results of even a sophisticated assignment technique cannotdeviate much if at all, from this narrow range.

Although the above macro assignment procedure is simple in conceptand application, it has far-reaching implications for travel forecasting.Foremost is the result that the derived speeds can be used as inputs forthe estimation of trip rates and distances within the controlling totalvehicle-kilometers of travel on a given road network. Furthermore, manyalternative metworks can be tested and evaluated easily and rapidly on amacro scale, and only the ones found to be satisfactory can then be furthertested on a micro scale by applying sophisticated assignment techniques.An additional advantage of the macro assignment procedure is that it can beused to test the reasonableness of a detailed micro assignment technique,as well as the number of vehicle trips and their average trip distance, forboth base year conditions and design year assumptions.

- 95 -

Appendix 7: The Cities Mentioned in the Report

Study Area State, Country Study,Area State, Country

Abidjan Ivory Coast Montreal CanadaAddis Ababa Ethiopia Moscow USSRAthens Greece Mulheim Fed.Rep. of Germany

Atlanta Georgia, U.S.New York New York, U.S.

Baltimore Maryland, U.S.Bangkok Thailand Orlando Florida, U.S.

Baton Rouge Louisiana, U.S.Belfast U.K. Paris France

Berlin, W. Fed.Rep. of Germany Philadelphia Pennsylvania, U.S.

Berlin, E. German Dem. Rep. Pittsburgh Pennsylvania, U.S.

Bogota Colombia Pulaski Arkansas, U.S.

Bombay IndiaBoston Massachusetts, U.S. Rapid City South Dakota, U.S.

Brisbane Australia Roma Italy

Buenos Aires Argentina Rotterdam Netherlands

Caracas Venezuela San Francisco California, U.S.

Cardiff U.K. San Jose Costa Rica

Casablanca Morocco Singapore Singapore

Chicago Illinois, U.S. Springfield Massachusetts, U.S.

Cincinnati Ohio-Ky.-Indiana,U.S. Stockholm Sweden

Cleveland Ohio, U.S.Columbia South Carolina, U.S. Tel Aviv IsraelCopenhagen Denmark Tokyo Japan

Toronto CanadaDetroit Michigan, U.S. Tri State New York, U.S.

Twin Cities Minnesota, U.S.Essen Fed.Rep. of Germany TucsOon Arizona, U.S.

Hamburg Fed.Rep. of Germany Washington D.C., U.S.

Hamm Fed.Rep. of Germany West Midlands U.K.Helsinki Finland Wienna AustriaHull U.K. Winston-Salem North Carolina, U.S.

Wuppertal Fed.Rep. of. Germany

Istanbul Turkey

Knoxville Tennessee, U.S.Krefeld Fed.Rep. of GermanyKuala Lumpur Malaysia

Leningrad USRRLondon U.K.Los Angeles California, U.S.

Melbourne AustraliaMerseyside U.K.Mexico City MexicoMilano ItalyMonroe Louisiana, U.S.

HG3881.5 .W57 w67 no.

2 3 c.2

Zehavi, YaOakOv.Travel chatracteristics

in

cities of developing and

develoPed countries