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Travel Demand and Traffic Forecasting

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

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Travel Demand and Traffic Forecasting

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  1. Travel Demand and Traffic Forecasting Dr. Attaullah Shah

  2. 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

  3. 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?)

  4. 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

  5. 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.

  6. Typical Trip Generation Model

  7. 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 $1,000s) – 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.

  8. 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

  9. Poisson Regression Model

  10. Estimating Poisson Parameter

  11. Example 8.4 Given: 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?

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