travel demand and traffic forecasting
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Travel Demand and Traffic Forecasting. Dr. Attaullah Shah. Travel Demand & Traffic Forecasting. Necessary understand the where to invest in new facilities and what type of facilities to invest Two interrelated elements need to be considered Overall regional traffic growth/decline - PowerPoint PPT PresentationTRANSCRIPT
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
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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
!)(
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Ti
i T
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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?