Chapter 7 Traffic Forecasting

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Traffic Forecasting


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Introduction

The modification of a highway network either by newroad construction or operating improvements onexisting roads(e.g., use or retiming of trafficsignalization) must be predicated upon some

expectation or forecast of roadway traffic volumes.For new road construction, forecast of traffic areneeded to determine an appropriate pavement aswell as a appropriate geometric design that will

provide an acceptable roadway level of service .Foroperational improvements, traffic forecast areneeded to estimate the effectiveness of alternateimprovement option.


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In forecasting vehicular traffic, two interrelated elementsmust be considered:

Overall regional traffic growth/decline.

-In the design of these projects, the engineer mustseek to provide a sufficient roadway level of service and an

acceptable pavement ride quality for future traffic volumes.One would expect that factors affecting long-termregional traffic growth/decline trends are primarilyeconomic and, to an historically lesser extent, social innature. For example, some regional populations may have

social characteristics that make them more likely, thanother regional populations to make fewer trips, to carpool,to vanpool or to take public transportation ,all of whichsignifically impact the actual amount of roadway traffic.


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Traffic Diversion

-in addition to overall regional traffic growth/decline,there is the more microscopic , short-termphenomenon of traffic diversion. As new roads areconstructed, as operational improvements are made,and/or as roads gradually become more congested,traffic will divert as drivers change routes or trip-departure time in an effort to avoid congestions andimprove the level of service that they experience.


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Traffic Decisions- Forecast of highway traffic should, at least in theory be

predicated upon some understanding of traveller decisions,since the various decisions that travellers make regarding tripswill ultimately determine the quantity, spatial distribution, andtemporal distribution of vehicles on a highway network. Withinthis context, traveller can be viewed as making four distinct butinterrelated decisions regarding trips:

Temporal decisions The temporal decision includes the decision to travel and,

more important, when to travel. destination decisions

The destination decision is concerned with the selection ofa specific destination.

modal decisions Modal decisions relates to how trip is to be made

spatial or route decisions spatial decisions focus on which route is to be taken from

the traveller's origin initial location.


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SCOPE OF THE TRAFFIC FORECASTING

PROBLEM

since traffic forecasting is predicated upon theaccurate forecasting of traveller decisions, two factorsmust be addressed in the development of an effective

traffic forecasting methodology:-The complexity of the travellerdecision-making process.

-System equilibration.


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Figure 7.1


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If we look more directly at the decision to travelmode/destination choice, and highway route choice, fig7.1 indicates that both long-term and short-termfactors affect these decisions. For the decision to

travel, as well as mode/destination choice, the longterm factor of modal availability, residential andcommercial distribution, and modal infrastructure playa significant role. These factors are considered long-term because they change relatively slowly over time.A short-term factor, such a modal traffic, is one thatcan vary in short period of time.


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The outcome of the combination of these traveller decisions is of course,

highway traffic, the prediction of which is objective traffic forecasting. Asidefrom the complexities involved in the traveller decision-making process, theissue of system equilibration(mentioned in the beginning of the section mustalso considered. Note that fig 7.1 not only indicates long term and short termfactors affect traveller decision and choices, but also that these decisions andchoice in turn affect the long and short-term factors. Such a simultaneousrelationship is most apparent when considering the relationship between

traveller choice and short-term factors).Travellers decisions to take specificroutes ultimately determine the route traffic upon which their route decisionsare based. This interdependence between traveller decisions and modal trafficis schematically presented in Fig 7.2 In addition to these shirt-term effects,persistently high traffic volumes may result in a change in the highwayinfrastructures. Again resulting in an interdependence. This interdependencecreates the problem of equilibrium that is common to many modellingapplication. Perhaps the most recognizable equilibration problem is the

determination of price in a classic model of economic supply and demand for aproduct. This evolution of traffic forecasting methodology has led to the popularapproach of viewing traveller decisions as a sequence of three distinctdecisions, as shown in Fig 7.3 the result of which is forecasted traffic flow


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Clearly, the sequential structures of traveller decisionsis a considerable simplification of the actual decision-making process in which all trip-related decisions areconsidered simultaneously by the traveller. However,

this sequential simplification permits the developmentof a sequence of mathematical models of travelbehaviour that can be applied to forecast traffic flow.


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Trip Generation

-The first traveller decision to be modelled in thesequential approach to traffic forecasting is tripgeneration. The objective of trip generation modellingis to develop an expression that predicts exactly when

a trip is to be made. This is an inherently difficult taskdue to the wide variety of trip types.


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Traveller in a sample day, as is schematically shown in fig 7.4 to address the complexity

of the trip generation decision, the following approach is typically taken: Aggregation of decision-making units predicting the trip generation behaviour is

simplified by considering the trip generation behaviour of a household as opposed tothe behaviour of individual travellers. Such an aggregation of travellers decisions is

justified on the basis of the comparatively homogeneous nature of householdmembers and their often intertwined trip generating activities.

Segmentation of trips by type Different types of trips have different characteristicsthat make them more or less likely to be taken at various times of the day. For

example, work trips are more to be taken in the morning hours than are shoppingtrips, which are more likely to be taken during the evening hours. Also, it is more likelythat the traveller will have two or three shopping trips during the course of a day thantwo or three work trips. To account for this, three distinct trip types are used:

-work trips, including trips to and from work

-shopping trips

-social/recreational trips

Temporal aggregation although research has been undertaken to developmathematical expression that predict when traveller is likely to make a trip more often. Trip generation focuses on the number of trips made over some period of time. Thustrips are aggregated temporally, and trip generation models seek to predict thenumber of trips per hour on per day.


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Trip Generation Models


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Example Problem (7.1)

A simple linear regression model is estimated from shopping trip generation during a shopping-trip peak hour(e.g.,Saturday afternoon). The model is

number of peak-hour vehicle-based shopping trips per household

=0.12+0.09(household size)

+0.011(annual household income (in thousand of dollars ))

-0.15 (employment in the households neighbourhood (in hundreds))

A particular household has six members, and an annual income of $50,000.they live in a neighbourhood with 450 retailemployees. Calculate the predicted number of peak-hour shopping trips the household makes before and after themove.

Solution

Note that the signs of the model coefficients ( bs, +0.09, and +0.011) indicate that as household size and incomeincrease, the number of shopping trips also increase. This is reasonable since wealthier, larger household can beexpected to make more vehicle-based shopping trips. The negative sign of the employment coefficient (-0.15)indicates that as retail employment in a household neighbourhood increase, fewer vehicle-based shopping trips will begenerated. This reflects the fact that a larger neighbourhood retail employment implies more shopping opportunitiesnearer to the household, thereby increasing the possibility that a shopping trip can be conducted without the use of avehicle

turning to the problem solution, before the household moves, we find that

number of trips=.12+.09(6)+.011(50)-.15(4.5)=.535 vehicle trips

after the household moves, we obtain

number of trips=.12+.09(6)+.011(50)-.15(1.5)=.985 vehicles trips

Thus the model predicts that the move will result in .45 additional peak-hour vehicle-based shopping tips due to thedecline in neighbourhood shopping opportunities as reflected by the decline in neighbourhood retail employment


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Mode And Destination Choice

-After the number of trips generated per unit time isknown, the next step in the sequential approach totraffic forecasting is the determination of traveller modeand destination. As was the case with trip generation,trips are classified as work, shopping, andsocial/recreational. For both shopping andsocial/recreational trips, the traveller will have theopinion to choose a mode of travel as well as adestination. In contrast, the work trip offers only themode option, since the choice of work location isusually a long-term decision that is beyond the timerange of most traffic forecast.


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Theoretical Approach


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Highway Route Choice

To summarize, the trip generation andmode/destination choice model give total highwaytraffic demand between a specified origin anddestination, in terms of vehicle per some time period.With this information in hand, the final step insequential approach to traffic forecasting, route choice,can be addressed. The result of the route choicedecision will be traffic flow on specific highway routes,which is the desired output from the traffic forecastingprocess.


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Highway Performance Function

Route choice presents itself as a classic equilibriumproblem, since travellers route choice decisions areprimarily a function of route travel times that aredetermined by traffic flow, which is itself a product of routechoice decisions. This interrelationship between routechoice decisions and traffic flow forms the basis of routechoice theory and model development.

To begin modeling traveller route choice, amathematical relationship between route travel time androute traffic flow is needed. Such a relationship is

commonly referred to as a highway performance function.The most simplistic approach to formalizing thisrelationship is to assume a linear highway performancefunction in which travel time increase linearly with speed.

An example of such a function is illustrated in fig. 7.5 in this

figure, the free-flow travel time


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Fig(7.5)


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Fig(7.6)


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Refers to the travel time that a traveller would experience if

no other vehicle were present to impede travel speed. Thisfree-flow speed is generally computer assuming that avehicle travels at the speed limit of the route.

although the linear highway performance functionhas the appeal of simplicity, it is not a particularly realistic

representation of the travel time/traffic flow relationship.This parabolic speed/flow relationship suggest a nonlinearhighway performance function, such as that illustrated in fig7.6. this figure shows route travel time increasing morequickly as traffic flow approaches capacity, which isconsistent with parabolic relationship presented in Chapter5.

Both linear and nonlinear highway performancefunction will be demonstrated, through example, using twotheories of travel route choice; user equilibrium and systemoptimization.


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Theory Of EquilibriumIn developing theories of traveller route choice. Two important assumptions are usuallymade. First, it is assumed that travellers will select routes between. Origins anddestinations on the basis of route travel time only. This assumption is not terriblyrestrictive, since travel time obviously plays the dominant role in route choice, but othermore subtle factors that may influence route choice(e.g., scenery), are not accounted for.The second assumption is that travellers know the travel times that would beencountered on all available routes between their origin and destinations. This is,potentially , a strong assumption, since the traveller may not have actually travelled on allavailable routes between an origin and destination and may repeatedly(day after day)choose one route based only on the perception that the travel times on alternate routesare higher. However, in support of this assumption, studies have shown that travellersperception of alternate route travel times are reasonably close to actual observed traveltimes

with these assumptions, the theory of user equilibrium route choice can beoperationalized. The rule of choice underlying user equilibrium is that travellers will selecta route so as to minimize their personal travel time between their origin and destination.User equilibrium is said to exist when individual travellers cannot improve their traveltimes by unilaterally changing routes. Stated differently user equilibrium can be definedas

the travel t ime between a speci f ied o r ig in and d est inat ion o n al l usedroutes is equal , and less than or equal to the travel t ime that wou ld beexper ienced by a travel ler on any unu sed route.


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Example (7.9)

Two routes connect a city and a suburb. During thepeak-hour morning commute, a total of 4500 vehicletravel from the suburb to the city. Route 1 has a 60-

mph speed limit and is 6 miles in length route 2 is 3miles in length with a 45-mph speed limit. Studiesshow that the total travel time on route 1 increasestwo minutes for every additional 500 vehicles added.

Minutes of travel time on route 2 increase with squareof the number of vehicle expressed in thousands ofvehicle per hour. Determine user equilibrium traveltimes.


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Fig(7.7)


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h i l i h


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Mathematical Programming Approach to

User Equilibrium


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Theory of System Optimal Route Choice


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PRACTICAL ISSUE IN TRAFFIC


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PRACTICAL ISSUE IN TRAFFIC

FORECASTNG

As mentioned earlier, the objective of this chapter was to

convey the basic principles underlying traffic forecasting.One can readily imagine that the actual application of thevarious method presented in this chapter to a real-worldtraffic forecasting problem requires that a number ofimportant practical issue be addressed. Such issue include:

The geographical definition or origin and destination areas,

The definition of a household as a decision-making unit,

The possibility of trip chaining,

The large number of alternate routes available between mostorigin and destination,

Specifying appropriate route performance function,

The equilibrium problem in nonroute choice models

The long-term effect of travel decision on residential andcommercial location

METHOD OF LEAST SQUARES


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METHOD OF LEAST SQUARES

REGRESSION


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Chapter 7 Traffic Forecasting

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Transcript of Chapter 7 Traffic Forecasting