29

CHAPTER 3

STUDY METHODOLOGY

3.1 INTRODUCTION

The mode choice does not rely on a single parameter. It is

associated with many parameters. In this chapter, the methodology for

estimating the mode choice used in this study is explained in detail. The

factors influencing the mode choice are first identified and then quantified.

The parameters which have significant impact are analyzed in detail. The

analysis is done for the parameters like land use activity, station

characteristics, opinion survey and travel pattern.

The methodology of this work is shown in Figure 3.1. Essentially,

the steps involved are data collection, analysis, model building and

forecasting.

3.2 STUDY AREA

Chennai city is situated on the North-East end of Tamil Nadu on

the coast of Bay of Bengal with a total area of 178.20 Sq. Km. It lies between

120 9' and 130 9' of the latitude and 800 12' and 800 19' of the longitude on a

sandy shelving breaker swept beach. It stretches nearly 25.60 Km along the

Bay coast from Thiruvanmiyur in the south to Thiruvottiyur in the north and

runs inland in a rugged semi-circular fashion.

30

Figure 3.1 Methodology Chart

Chennai has three rail corridors such as southwestern corridor

connecting Beach and Tambaram, western corridor from Beach to Avadi and

northern corridor from Beach to Thiruvallur. In addition to these rail transport

system, MTC operates 2990 buses on nearly 640 routes serving the travel

demand of the people in the city. The MRTS rail corridor in Chennai has been

Secondary Data 1. Demography 2. Employment.

Objective

Study Area Selection

Land Use Study of MRTS Corridor

Data Collection

Demand Matrix of MRTS/ BUS

Model Development and validation

Sensitivity Analysis

Formulation of scenarios and analysis

Primary Data 1. Opinion survey 2. OD survey of Mass Transport passenger (bus, MRTS) 3. Frequency, Accessibil ity, Travel time, and Travel Cost of Mass Transport Systems(bus, MRTS).

Study of operational characteristics of the

Mass Transport System

Identifying Tangible and Non Tangible parameters influencing Mode choice

Findings and Conclusion

Artificial Neural Network (ANN)

LOGIT MODEL

Secondary Data 1 Demography 2 Employment.

Objective

Study Area Selection

Land Use Study of MRTS Corridor

Data Collection

Demand Matrix of MRTS/ BUS

Model Development and validation

Sensitivity Analysis

Formulation of scenarios and analysis

Primary Data 1. Opinion survey 2. OD survey of Mass Transport passenger (bus, MRTS) 3. Frequency, Accessibil ity, Travel time, and Travel Cost of Mass Transport Systems (bus, MRTS).

Study of operational characteristics of the

Mass Transport System

Identifying Tangible and Non-Tangible parameters influencing mode choice

Findings and Conclusion

Artificial Neural Network (ANN) Model Logit Model

31

taken for the study purpose. The MRTS influence area (i.e. about 1.5 Km on

either side) has been taken as the study area (Figure 3.2).

Figure 3.2 MRTS Influence Area

32

The MRTS alignment passes through the Cooum and Adyar rivers

and runs parallel to the Buckingham Canal for about 10 Km. There is a good

scope for the passenger patronage to MRTS from the influence area which has

mixed land use activity. 35 percent are residential, 25 percent are institutional

and about 15 percent are open and vacant land, 12 percent are industrial and

13 percent are commercial. The total MRTS system was planned for 41.47

Km in four stages; till date only two stages have been constructed, that is,

Phase I from Park to Thirumailai and Phase II from Thirumailai to Velachery.

The total length of MRTS in operation is only 19.34 Km. The average inter-

station spacing between MRTS stations is about 1 Km, whereas the distance

between Beach and Fort station, Light house and Thirumailai stations is about

1.71 Km. The study focuses only on the constructed portion of MRTS.

The section of the line encompassing the first three stations -

Beach, Fort and Park Town, is at a ground level; after Park Town the line is

elevated. All the stations after Park Town - Chintadripet, Chepauk, Triplicane,

Light House, Mandaveli, Greenways Road, Thirumailai, Kotturpuram,

Kasthuribai Nagar, Indira Nagar, Thiruvanmiyur, Taramani and Perungudi are

elevated and Velachery station is at ground level.

3.3 LAND USE DISPOSITION ALONG THE MRTS CORRIDOR

The patronage to any transportation project is highly dependent on

the land use disposition along the corridor. The patronage of MRTS is also

attributed to the land use development along the corridor and the socio-

economic characteristics of the people living along the corridor. The land use

along the corridor and the socioeconomic characteristics of the people are

discussed in this section.

The economically weaker section of the society encroached on

vacant land available on the banks of Buckingham Canal. They have built

33

huts and temporary structures for living. The area of settlement by this group

is called slum. Table 3.1 explains clearly the impact of slum population on

MRTS ridership. It is evident that, if the slum population is higher (10 - 20

percent) there is less scope for MRTS ridership. In case of Thirumailai, the

slum population is 13.22 percent but, it has good ridership. It is because of

high density residential population and slum is available at only on one side.

Hence, the slum population has significant impact on reduction of MRTS

ridership. Because of the encroachment, safety and hurdles in commercial

development other people avoid using the MRTS.

Table 3.1 Details of Population and Slum Population

Station Population in numbers

– 2001

Slum Population

in numbers

Percentage of Slum Population

MRTS Patronage

in numbers

Beach 45635 2405 5.27 5469 Fort 45635 2333 5.11 2995 Park Town 34650 727 2.10 3155 Chintadripet 74381 4590 6.17 1191 Chepauk 28046 1020 3.64 3364 Triplicane 33381 5720 17.14 1277 Light House 82140 16555 20.15 2047 Thirumailai 103398 13668 13.22 5365 Mandaveli 73866 7475 10.12 585 Greenways Road 42289 9137 21.60 875 Kotturpuram 82498 6815 8.00 825 Kasthuribai Nagar 54733 6735 12.30 1025 Indira Nagar 65042 8228 12.60 825 Thiruvanmiyur 46567 6577 14.00 3246 Taramani - I 22439 5105 9.00 350 Taramani – II 22439 2105 9.00 250 Velachery 132584 10235 7.00 3656

34

Table 3.2 and Figure 3.3 give the classification of stations by

population density. Table 3.3 gives the land-use breakup of MRTS corridor.

Correlating Table 3.1, 3.2 and 3.3, it is very clear the stations Triplicane and

Light House come under high density zones but have 20 percent of slum

population. Hence, the ridership at these stations is very low. The stations like

Chindaripet, Beach, Chepuk, Thirumailai and Mandaveli come under medium

density zones and hold a moderate ridership. The stations like Fort, Park,

Greenways Road, Kasturbanagar, Indiranagar, Thiruvanmiyur and Velachery

come under low density zones but, due to availability of institutional and

residential area theses stations hold good ridership. Thus stations with high

density with good mix of residential and Institutional area will have scope for

higher ridership.

Table 3.2 Classification of Stations Based on Population Density in

MRTS corridor

Phase High Density

Zone > 400 ppHa Medium Density

Zone 200-400 ppHa Low Density Zone

< 200 ppHa Phase I Triplicane (586) Chintadripet (294) Fort (195)

Light House (517) Beach (287) Park Town (180) Chepauk (236) Thirumailai (370)

Phase II Mandaveli (204) Greenways Road (158) Kotturpuram (127) Kasthuribai Nagar(120) Indira Nagar (154) Thiruvanmiyur (147) Taramani and

Perungudi (77) Velachery (44)

35

From Table 3.3 and Figure 3.3 it is clear that both ends of MRTS

are having institutional area and commercial area. The residential area is

sandwiched between the two ends. Hence there is a good scope for trip

generation and attraction along the MRTS corridor.

Table 3.3 MRTS Station Wise Land Use break up in percentage

Station Name

Land Use break up in percentage

Tota

l in

perc

enta

ge

Res

iden

tial

Com

mer

cial

Inst

itutio

n

Ope

n

Roa

d

Indu

stri

al

Vac

ant

Beach 1.29 57.44 23.45 0.00 17.82 0.00 0.00 100

Fort 5.81 6.97 64.87 0.00 22.35 0.00 0.00 100

Park Town 21.87 12.06 39.93 0.00 26.14 0.00 0.00 100

Chintadripet 34.13 16.51 27.83 0.54 19.86 1.13 0.00 100

Chepauk 25.95 15.67 51.02 0.00 7.36 0.00 0.00 100

Triplicane 41.22 10.64 16.33 2.76 28.38 0.68 0.00 100

Light House 40.03 14.77 15.88 5.32 23.99 0.00 0.00 100

Thirumailai 41.05 8.82 15.78 5.28 29.07 0.00 0.00 100

Mandaveli 44.11 14.44 8.00 3.03 30.42 0.00 0.00 100

Greenways Road 62.83 4.04 5.86 5.44 21.12 0.72 0.00 100

Kotturpuram 47.83 2.11 20.34 11.10 18.62 0.00 0.00 100

Kasthuribai Nagar 43.97 7.98 25.63 4.02 18.67 0.00 0.00 100

Indira Nagar 36.53 5.19 25.52 3.38 29.39 0.00 0.00 100

Thiruvanmiyur 48.07 2.97 27.39 7.89 13.39 0.00 0.00 100

Taramani 4.78 1.28 41.64 10.78 11.11 4.15 26.2 100

Perungudi 62.62 3.93 0.90 25.79 6.76 0.00 0.00 100

Velachery 55.27 3.26 0.56 23.43 0.00 1.87 15.6 100

36

Figure 3.3 Population Density along the MRTS Corridor

3.4 MASS TRANSPORT SYSTEMS OPERATED IN THE STUDY

AREA

MRTS and Bus transport system are the two major mass transport

systems operated in the study area. The operational aspects and ridership of

the systems are described in this section.

3.4.1 MRTS Characteristics

MRTS operation between Chennai Beach and Chepauk stations

was unveiled to commuters in 1995. The stretch was built with an investment

of 534.6 million Indian Rupees (INR). The demand for MRTS on

implementation was about 600 per day, since MRTS operated for a small

distance of 5.84 Km with six stations. On extending the MRTS up to

Thirumailai in 1997 with an additional cost of 2069.5 million INR the

37

demand gradually picked up from 600 to 9000 over a period of 7 years. In

2004 the MRTS was further extended upto Thiruvanmiyur with an additional

cost of 6840 million INR. Because of the extension the demand increased

from 9000 to 18000 and over three years it reached up to 28000. On

extending the MRTS section up to Velachery in 2008 again there was rise in

the demand and it is up to 36000 per day on an average. On studying the

growth of demand from 1995-2008 (Figure 3.4) it is evident that the demand

increased in relation with the length of operation.

After commissioning of Phase II MRTS showed remarkable

increase in the ridership. Figure 3.5 shows the trend of average MRTS

passenger loading per day from the year 1995 to 2008 (March). It is evident

that Beach and Thirumailai station has average patronage of 5500. Beach is

the only station which has the maximum patronage of 5469 per day and next

to Thirumaili station the Velachery station which has the average patronage of

3656 per day.

0

5000

10000

15000

20000

25000

30000

35000

40000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Year

MR

TS P

asse

nger

Dem

and

per D

ay

in N

umbe

rs

Figure 3.4 MRTS Passenger Demand per Day from 1995 to 2008

38

29553156

1191

3346

1277

2047

5365

585878 825 825 1025

3246

350 250

3656

5469

0

1000

2000

3000

4000

5000

6000

Beach Fort

Park Town

Chintad

ripet

Chepau

k

Triplic

ane

Light

House

Thirumail

ai

Manda

veli

Greenways

Roa

d

Kotturp

uram

Kasthu

ribai N

agar

Indira N

agar

Thiruv

anmiyu

r

Taramani

Perung

udi

Velach

ery

MRTS Stations

MR

TS P

asse

nger

Dem

and

per D

ay in

N

umbe

rs

Figure 3.5 Average Passenger Loading at MRTS Stations

The other stations like Fort, Park, Chintadripet, Triplicane, Light

House, Mandaveli, Greenways Road, Kotturpuram, Kasthuribai Nagar, and

Indiranagar have average passenger loading of 1500- 2000 per day. The total

ridership on MRTS ranges from 36000- 37000 per day. Table 3.4 gives the

average loading at each MRTS station as on March 2008.

Table 3.4 Patronage at MRTS Stations as on March 2008

Name of the station

MRTS Demand per day in Numbers

Name of the station

MRTS Demand per day in Numbers

Beach 5469 Greenways Road 878 Fort 2955 Kotturpuram 825 Park Town 3156 Kasthuribai Nagar 825 Chintadripet 1191 Indira Nagar 1025 Chepauk 3346 Thiruvanmiyur 3246 Triplicane 1277 Taramani 350 Light House 2047 Perungudi 250 Thirumailai 5365 Velachery 3656 Mandaveli 585

Source: Railways Department

39

MRTS operates 100 services / day between Beach and Velachery

and the cumulative running time from Beach to Velachery is 42 minutes at an

average speed of 21 Kmph. The morning peak Period is identified between

8.00 AM – 10.00 AM and the evening peak period is identified between 5.00

PM – 7.00 PM. The frequency varies from 2 trips per hour to 5 trips per hour.

A survey of boarding and alighting passengers was carried out for 8

hours covering morning and evening peak hours to understand the current

ridership and extent of line utilization. It showed that at present the system

operates 5 transit trips during peak period. The maximum number of

passenger trips observed during peak period is 2700. MRTS system is

designed to carry 13, 00,000 trips for 15 hours, both ways but the total trips

observed for 15 hours is 36000 trips both ways. Hence, the line utilization

2.76 percent.

To estimate the present hourly variation in ridership in MRTS a

survey on passenger boarding and alighting was carried out between 7:00 AM

and 11:00 AM in the morning and 4:00 PM and 8:00 PM in the evening.

At each station the total number of passengers boarding and alighting

was surveyed and passenger trips per hour per direction were calculated.

Figures 3.6 and Figure 3.7 below represent the same for morning and evening

hours.

The morning peak is observed between 8:00 AM and 10:00 AM

and the evening peak is observed between 5:00 PM and 7:00 PM. 75 percent

of passenger trips, south bound, is during 8:00 AM - 10:00.AM. The

frequency of train is higher during this period and these trips should be mostly

work trips. MRTS experiences directional patronage and Table 3.5 below

clearly indicates that southbound traffic is heavy during morning hours. The

directional split during the morning period is 68:32 (South: North). Table

40

shows that a morning passenger trip is 17850 and evening passenger trip is

18150. Peak period is observed between 8:00 AM and 10:00 AM and

5:00 PM and 7:00 PM. This value justifies that the evening passenger trips are

also return work trips.

200287

2256

2675

531

1522

870

2330

0500

10001500200025003000350040004500

7.00

-8.0

0A

M

8.00

-9.0

0A

M

9.00

-10

.00

AM

10.0

0-11

.00

AM

Morning Period

Num

ber o

f Pas

seng

er T

rips

Towards NorthTowards South

Figure 3.6 MRTS Passenger Loading Hour Wise Variation – Morning

Period

527 385

2216

778 1007 1013398

2545

0

500

1000

1500

2000

2500

3000

3500

4.00

-5.0

0P

M

5.00

-6.0

0P

M

6.00

-7.0

0P

M

7.00

-8.0

0P

M

Evening Period

Num

ber o

f Pas

seng

er T

rips Towards South

Toward North

Figure 3.7 MRTS Passenger Loading Hour Wise Variation – Evening

Period

41

Table 3.5 Directional Split of MRTS Passengers

Direction Morning Period

Directional split during

morning hours

Evening Period

Directional split during

Evening hours

Total Directional split for the entire day

Towards South

12524 68 % 5116 29 % 17640 49%

Towards North

5326 32 % 13034 71% 18360 51%

Total 17850 100 % 18150 100% 36000 100%

Inter-station spacing refers to the distance between the two stations.

It influences the journey speed of the transit between the stations. The

sectional speed of MRTS is 72 Kmph. However, the current average

operating speed is 21-25 Kmph from Beach to Velachery. This is due to the

presence of closely spaced stations. A station like Mandaveli has the least

station load of 585 and it hardly contributes to passenger trips. Except

Kasthuribai Nagar Thiruvanmiyur and Velachery station load in all the other

stations in Phase II is less than 1000. Table 3.6 gives the inter-station spacing

and the station load of all stations.

In Phase 1 of MRTS, passenger movement among the MRTS

stations is moderate, i.e. commuters move from Thirumailai to Chepauk,

Beach for various purpose of trips. However, in Phase 2-commuter

movement among the MRTS stations is completely nil. When stations are

spaced so close such as 0.8 Km, the demand in MRTS has been very low.

42

Table 3.6 Inter Station Spacing of MRTS

Origin Station Destination station Inter station Distance

in Km Beach Fort 1.70 Fort Park Town 0.84 Park Town Chintadripet 0.89 Chintadripet Chepauk 1.57 Chepauk Triplicane 0.74 Triplicane Light House 1.21 Light House Thirumailai 1.71 Thirumailai Mandaveli 1.04 Mandaveli Greenways Road 1.32 Greenways Road Kotturpuram 0.87 Kotturpuram Kasthuribai Nagar 0.93 Kasthuribai Nagar Indira Nagar 0.97 Indira Nagar Thiruvanmiyur 0.86 Thiruvanmiyur Taramani 1.92 Taramani Perungudi 1.14 Perungudi Velachery 1.63

3.4.2 Bus Transport System

The high capacity MRTS had been introduced along an alignment

that has already well established Bus routes. To find the potential passengers

to use MRTS, the assessment of total number of passengers making trips by

bus along the MRTS influence area is presented. In addition, total number of

buses operated parallel to MRTS is also reviewed in this section.

43

The Bus routes operating parallel to MRTS in the influence are

identified based on the following criteria;

Routes touching at least one MRTS station.

Routes running within 1.5 Km from MRTS.

Routes that originate beyond Velachery, Thiruvanmiyur and

end the trips in MRTS influence area.

Routes that originate beyond Beach station and end the trips in

MRTS influence area.

The identified routes taken up for analysis are shown in Figure 3.8.

Parrys Corner, Thirumailai, Thiruvanmiyur and Velachery are the important

nodes which operate buses parallel to MRTS. The list of bus routes, number

of service and number of singles operated per day are shown in Table 3.7.

About 195 services are operated along the MRTS corridor making 2882 trips

per day.

The over lap length of these buses and MRTS ranges from 5 Km to

17 Km. Routes operated parallel to MRTS were identified and total number

passengers making trip per day in these routes are computed as 184571 based

on the following criteria;

Passengers traveling parallel to MRTS to a minimum distance

of 4 Km.

Passengers having origin beyond Velachery, Thiruvanmiyur

and destination in MRTS influence area.

Passengers having origin beyond Beach station and

destination in MRTS influence area.

44

Figure 3.8 Bus Routes Running Parallel to MRTS Taken up for Study

45

Table 3.7 Passenger Movement and Bus Movement along MRTS

Corridor

ORIGIN Route No. Destination Number of Single/Day

Velachery

PP51/EXT Parrys Corner 68 21L Parrys Corner 198 5G Thiruvanmiyur 128 1G Thiruvottriyur 36

Thiruvanmiyur

5G Velachery 128 1C Ennore 56 I Thiruvottriyur 288 6E Tollgate 12 6D Tollgate 164

Adyar

19 Injambakkam 60 5 Parrys Corner 80 19B Soliganallur 48 19K Sirucherri 32 21B Parrys Corner 40 21H Kelabakkam 72 M5 Soliganallur 48 M19 Injambakkam 80

Thirumailai / Chepauk

3A Parrys Corner 22 5K Taramani 144 M15 Medavakkam 48 45E Keelakattalai 48

Parrys Corner

3A Mylapore 22 5 Adayar 80 5C Taramani 84 18D Keelakattalai 44 18P Velachery 16 18 Indiranagar 16 19E Kovalam 10 19G Kovalam 100 19G-CUT Srinivasapuram 28 PP19 Injambakkam 148 PP19 EXT Kovalam 74 21P Indiranagar 96 21H Kelambakkam 192 21L EXT Keelakattalai 28 52 K Keelakattalai 84

Total 2822

46

3.5 DATA COLLECTION

In this section, a detailed methodology is presented for conducting

various primary surveys needed to quantify the factors influencing mode

choice parameters like travel time, travel cost (mode wise) and accessibility to

bus stops and MRTS stations. In addition, to understand the travel

characteristics and travel pattern of the commuters the methodology to

conduct opinion survey is also explained.

3.5.1 Opinion Survey

The opinion survey helps one in identifying the actual desire of the

people. It provides the base for developing any model for likelihood

estimations. In this work the opinion survey was designed in such a way that

it helps to identify the influencing factors for travel of the people by MRTS

and bus. The opinion survey was conducted among the MRTS users, bus

users and employees having offices along the MRTS corridor.

The major objective of the opinion survey is to identify the reasons

for not preferring the MRTS and desirable factors for switch over. To enable

the process, a questionnaire was designed with multiple choice questions to

collect the people opinions. Formats of questionnaire are shown in Appendix

2 and 3. In this work direct interview method was adopted with survey

questionnaire to obtain the opinion. The persons to be interviewed were

selected by random sampling method.

3.5.2 Estimation of Travel Time between Two Modes

The travel time between two nodes includes the in vehicle time,

dwell time and access time. The in-vehicle travel time is measured by using

47

the GPS. Conventionally the travel time was measured manually with stop

watch. The major disadvantage of this method was the manual assumption

involved and that may lead to mis-prediction and prone to error. Hence, GPS

was used in this study.

The probe vehicle and the route were selected first and the GPS

was fitted to the probe vehicle. In this work the probe vehicles were MRTS

and bus. The GPS will automatically record the data like altitude, speed,

acceleration, latitude and longitude. Finally when the destination was reached

the GPS recording was stopped. GPS Visualizer, Data Processing software

were used to extract the data and the processed data is then used to compute

the travel time precisely.

3.5.2.1 Dwell Time Survey

The dwell time is the time that bus spends at stopping to serve the

passengers at a specific stop. Levinson (1983) and Rajbhadri (2003) state that

the dwell time can account for up to 26 percent of the total travel time. Hence

a detailed study is carried out in this work. The survey location is selected

along the MRTS corridor. Three persons were involved in this study, one

person recorded the arrival time and departure time of buses, second person

recorded the number of alighting persons and standing inside the bus and the

third person recorded the number of boarding commuters in the bus. The

collected data was processed and dwell time model was developed.

The bus dwell time consists of the time needed for the following

events to occur; 1) passenger boarding, 2) passenger alighting. If the number

of passengers alighting and boarding are independent of each other, the dwell

time was expressed by the sum of two random variables as shown in

Equation 3.1. The marginal passenger alighting and boarding time per

48

passengers was assumed constant. The proposed model for estimating dwell

time is given in Equation 3.1.

αd = µb + σa + ρs (3.1)

where,

αd = Bus dwell time in Seconds. µb = Number of passengers boarding

σa = Number of Passengers alighting

ρs = Number of passengers standing

inside the bus

3.5.3 Accessibility Survey

The accessibility survey is the direct appraisal survey done by a

group of experts. The members assessed the status of accessibility level in

terms of feeder service, approach road conditions, parking facility, bus stop

locations, etc. The accessibility readings are analyzed and weightage is

assigned for each category of options as discussed in section 2.4.2.

Accessibility plays an important role in any system. If a system is provided with

excellent facilities and if there is no accessibility then it will discourage the

passengers to use the system. Table 3.8 shows the various accessibility

indices for the MRTS and bus system.

Table 3.8 Accessibility Indices

Attributes Nature of Attributes Weightage Assigned

Feeder Service availability at MRTS Stations

Yes 1 No 0

Location of Bus Stop from MRTS Stations

Bus stop < 200m 2 Bus stop with in 200-500m 1 Bus stop with in >500m 0

Approach Road condition Good 1 Poor 0

Parking Facility availability at MRTS Stations.

Available 1 Not Available 0

49

3.6 PROPOSED LOGIT MODEL FOR MODE CHOICE

In this section the proposed Logit model for mode choice

estimation is presented. The mathematical models derived from Random

Utility Theory is the richest and tried extensively for the simulation of

transport related choices and choices among discrete alternatives. Random

Utility Theory is based on the hypothesis that every individual is a rational

decision maker, maximizing utility relative to his/her choices. The underlying

important assumptions are:

An individual is faced with a finite set of choices from which

only one can be chosen.

Individuals belong to a homogenous population, act rationally,

and possess perfect information and always select the option

that maximizes their net personal utility.

If C is defined as the universal choice set of discrete

alternatives, and “ j ” the number of elements in C, then each

member of the population has some subset of C as his or her

choice set. Most decision-makers, however, have some subset

Cn, that is considerably smaller than C. It should be

recognized that defining a subset Cn, that is the feasible choice

set for an individual is not a trivial task; however, it is

assumed that it can be determined.

Decision-makers are endowed with a subset of attributes xn

X, all measured attributes relevant in the decision making

process.

In the case of only two alternatives A and B in a choice set (like

Bus and MRTS) and if the alternatives have systematic utilities of say UBUS,

50

UMRTS, the measure of utilities UBus, UMRTS is the function of travel time,

travel cost and accessibility which may be expressed as in Equation 3.2

U Bus / MRTS = a0 + al x1 + a2.x2 + a3.x2 …….an.xn (3.2)

where

UC BUS : Utility measure of Bus transport

UC MRTS : Utility measure of MRTS

a1, a2, a3, an : Utility coefficients of Bus/ MRTS

x1, x2, x3, xn : Utility Parameters

Based on the above utility equations, the proportion of travel

demand is estimated using the multinomial Logit model as given in

Equations 3.3 and 3.4. In this model e(i)’s of choice utility function are all

assumed to be independent and are identically distributed exponential

function.

Bus

Bus M RT S

( U )

( U ( U) )P(BUS)

(3.3)

MRTS

Bus MRTS

(U )

(U (U) )P(MRTS)

(3.4)

where,

P(BUS) : Proportion of travel demand for Bus

P(MRTS) : Proportion of travel demand for MRTS

3.7 PROPOSED ANN MODEL FOR MODE CHOICE

In this study an ANN architecture which was never adopted before

in literature is used to address some of the main drawbacks of existing ANN

models, namely the unclear interpretation of parameters and the absence of an

explicit utility function, which do not allow for elasticity analysis and, more

51

generally, make the generalization of the resulting models arguable. The

proposed architecture separately specifies the function, assumed linear,

between attributes and mode systematic utility. The function is modeled

through a hidden layer between mode systematic utility and choice

probabilities. This way, an explicit utility function can be specified, as

commonly for Random Utility Models, the calibration of such structure

allows an interpretation of input variables, while the relative weights allow to

carry out an elasticity analysis.

The proposed architecture contains four layers as shown in

Figure 3.9; 0. Attribute (Input) layer, regarding relevant attributes, with one

processing unit for each attributes 1. Utility layer, regarding systematic utility,

with one processing unit for each transportation mode, 2. Hidden layer,

regarding effect of utility on choices, with a number of processing units to be

defined, 3. Probability (Output) layer, regarding choice probabilities, with one

processing unit for each transportation mode. The whole model can be

described by equations between the processing units of each pair of

successive layers, as described below.

Layer 0. Given the values of j-th attribute for each mode k: xkj

calibration; the transfer function being the identity.

Layer 1. For each processing unit k (one per mode) of the utility

layer Vk as in Equation 3.5

vk = ∑j β kj xkj + ASAk (3.5)

where the weights kj, and the biases ASAk are to be estimated.

Layer 2. For a processing unit yn (their number being a design

option) of the hidden layer, Equation 3.6

52

yn = φ (∑k wnk vk + bn) (3.6)

where the weights wnk, and the biases bn are to be estimated during the

calibration; the transfer function φ( ●) being a design option.

Figure 3.9 Proposed Architecture of ANN Model

Layer 3. For a processing unit Pm (one per mode) of the

probability layer, Equation 3.7

Рm = Ψ (∑n znk yn + cm) (3.7)

where the weights znk, and the biases cn are to be estimated during the

calibration; the transfer function φ(●) being a design option. Eventually, to

compensate numerical errors, the output (probability) values Pm (non-negative

in any case) are normalized so that their sum is equal to one.

53

3.8 SUMMARY

In this chapter, a brief description of the methodology of the work

carried out in this thesis is explained. Model development and data collection

process were given more importance. The land use disposition along the

corridor was analyzed and it was found that 35 percent are residential, 25

percent are institutional and about 15 percent are open and vacant land, 12

percent are industrial and 13 percent are commercial. The various travel

characteristics of MRTS system was studied and it was found that Beach,

Velachery and Thirumailai stations are the well-patronized stations. The

travel pattern of the people was analyzed and it was found that 184572

passengers are moving along the MRTS corridor by bus and they are the

potential passengers likely to get shifted to MRTS.