Dr. Attaullah Shah

Travel Demand & Traffic ForecastingNecessary understand the where to invest

in new facilities and what type of facilities to invest

Two interrelated elements need to be consideredOverall regional traffic growth/declinePotential traffic diversions

Traveler DecisionsFour key traveler decisions need to be

studied and modeled:Temporal decisions – the decision to travel and

when to travelDestination decisions – where to travel

(shopping centers, medical centers, etc.)Modal decisions – how to travel (auto, transit,

walking, biking, etc)Route decisions – which route to travel (I-66 or

Rt 50?)

Trip GenerationObjective of this step is to develop a model

which can predict when a trip will be madeTypical input information

Aggregate decision making units – we study households not individual travelers typically

Segment trips by type – three types 1) work trips 2) shopping trips and 3) social/recreational trips

Aggregate temporal decisions – trips per hour or per day

Trip Generation ModelTypically assume linear formTypical variables which influence number of

trips are Household incomeHousehold sizeNumber of non-working household membersEmployment rates in the neighborhoodEtc.

Typical Trip Generation Model

i household of members) household ofnumber

od,neighborhoin employment (income,k sticcharacteriz

k sticcharacteri toingcorrespond and data

survey traveler from estimatedt coefficienb

i householdby made period timespecified

somein given type a of tripsbased- vehofnumber T

:where

...

ki

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kikiioi zbzbzbbT

Trip Generation Model Example ProblemNumber of peak hour vehicle-based

shopping trips per household = 0.12 + 0.09 (household size) + 0.011(annual

household income in $1,000s) – 0.15 (employment in the household’s neighborhood in 100s)

A household with 6 members; annual income of $50k; current neighborhood has 450 retail employees; new neighborhood has 150 retail employees.

Trip Generation with Count Data ModelsLinear regression models can produce

fractions of trips which are not realisticPoisson regression can be used to estimate

trip generation for a given trip type to address this problem

Poisson Regression Model

]E[T period, timespecified somein trips

based- vehofnumber expected si' household to

equal is which i, householdfor parameter Poisson

2.817)(e logarithm natural of basee

integer) negativenon is Ti (where tripsTexactly

making i household ofy probabilit)P(T

i householdby period timespecifiedin made

given type of tripsbased- vehof No.T

:Where

!)(

i

i

i

i

i

i

Ti

i T

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ii

Estimating Poisson Parameter

previously explained as sother term

generation tripgdeterminin

sticscharacteri household ofvector Z

tscoefficien eestimatabl ofvector B

:where

i

iBZi e

Example 8.4Given:BZi= -0.35 + 0.03 (household size) + (0.004) annual household income in 1,000s –0.10 (employment in household’s

neighborhood in 100s)Household has 6 members; income of $50k;

lives in neighborhood with 150 retail employment; what is expected no of peak hour shopping trips? What is prob household will not make peak hour shopping trip?