Travel Demand Traffic Forecasting Dr Attaullah Shah Travel

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Travel Demand Traffic Forecasting Dr. Attaullah Shah

Travel Demand Traffic Forecasting Dr. Attaullah Shah

Travel Demand & Traffic Forecasting �Necessary understand the where to invest in new facilities

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 �Potential traffic diversions

Traveler Decisions �Four key traveler decisions need to be studied and modeled: �Temporal decisions

Traveler Decisions �Four key traveler decisions need to be studied and modeled: �Temporal decisions – the decision to travel and when to travel �Destination 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 Generation �Objective of this step is to develop a model which can predict

Trip Generation �Objective of this step is to develop a model which can predict when a trip will be made �Typical 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 Model �Typically assume linear form �Typical variables which influence number of trips

Trip Generation Model �Typically assume linear form �Typical variables which influence number of trips are �Household income �Household size �Number of non-working household members �Employment rates in the neighborhood �Etc.

Typical Trip Generation Model

Typical Trip Generation Model

Trip Generation Model Example Problem Number of peak hour vehicle-based shopping trips per household

Trip Generation Model Example Problem Number of peak hour vehicle-based shopping trips per household = 0. 12 + 0. 09 (household size) + 0. 011(annual household income in $1, 000 s) – 0. 15 (employment in the household’s neighborhood in 100 s) A household with 6 members; annual income of $50 k; current neighborhood has 450 retail employees; new neighborhood has 150 retail employees.

Trip Generation with Count Data Models �Linear regression models can produce fractions of trips

Trip Generation with Count Data Models �Linear regression models can produce fractions of trips which are not realistic �Poisson regression can be used to estimate trip generation for a given trip type to address this problem

Poisson Regression Model

Poisson Regression Model

Estimating Poisson Parameter

Estimating Poisson Parameter

Example 8. 4 Given: BZi= -0. 35 + 0. 03 (household size) + (0.

Example 8. 4 Given: BZi= -0. 35 + 0. 03 (household size) + (0. 004) annual household income in 1, 000 s – 0. 10 (employment in household’s neighborhood in 100 s) Household has 6 members; income of $50 k; 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?

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