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
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Dr. Attaullah Shah
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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
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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?)
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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
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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.
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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
k
i
2211
kikiioi zbzbzbbT
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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.
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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
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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
eTP
ii
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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
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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?