Traffic Forecasting (Transportation Engineering)
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This document provides an overview of a traffic forecasting course (CEE320) taught in winter 2006. It discusses the need for traffic forecasting to plan transportation infrastructure and services. It covers topics like traveler decisions, trip generation, and mode choice modeling. Trip generation involves predicting the number and timing of trips, while considering factors like life stage, technology, and land use. Mode choice modeling uses techniques like logit models to predict transportation mode. The document provides examples of models for recreational trip generation and sitcom character preference to illustrate these traffic forecasting techniques.
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Traffic Forecasting (Transportation Engineering)
- 2. CEE320 Winter2006 Outline 1. Need for Traffic Forecasting 2. Traveler Decisions 3. Trip Generation 4. Mode Choice a. Survey
- 3. CEE320 Winter2006 Need for Traffic Forecasting • Impacts of facilities or modes of travel – Lines on existing roads – Roads – Light rail – Bus service • Geometric design • Pavement design
- 4. CEE320 Winter2006 Traveler Decisions • Types of decisions – Time (when do you go?) – Destination (where do you go?) – Mode (how do you get there?) – Route choice (what route do you choose?) • Influences – Economic – Social
- 5. CEE320 Winter2006 Predicting Travel Decisions • Collect data on travel behavior – Observation (number of buses, cars, bikes, etc.) – Surveys • Collect data on what travelers have done • Collect data on their values and choices (utility) • Inexact nature of prediction – Incomplete data – Reporting problems
- 6. CEE320 Winter2006 Trip Generation & Mode Choice
- 7. CEE320 Winter2006 Trip Generation • Purpose – Predict how many trips will be made – Predict exactly when a trip will be made • Approach – Aggregate decision-making units – Categorized trip types – Aggregate trip times (e.g., AM, PM, rush hour) – Generate Model
- 8. CEE320 Winter2006 Motivations for Making Trips • Lifestyle – Residential choice – Work choice – Recreational choice – Kids, marriage – Money • Life stage • Technology
- 9. CEE320 Winter2006 Reporting of Trips - Issues • Under-reporting trivial trips • Trip chaining • Other reasons (passenger in a car for example)
- 10. CEE320 Winter2006 Trip Generation Models • Linear (simple) • Poisson (a bit better) nn xxxT ββββ ...22110 +++= nni xxx ββββλ ...ln 22110 +++= ( ) ( ) = − ! tripsofnumber x x exP λλ
- 11. CEE320 Winter2006 Example kidsmarriedinternetagegender autosbus bicyclesedanvansportssuv incomeeducationi * 8 * 7 * 6 *5*4 *#37*36 *35*34*33*32*31 *2*10ln βββββ ββ βββββ βββλ +++++ ++ +++++ ++= Recreational or pleasure trips measured by λi (Poisson model):
- 12. Variable Coefficient Value Product Constant 0 1 0 Education (undergraduate degree or higher) 0.15 1 0.15 Income 0.00002 45,000 0.9 Whether or not individual owns an SUV 0.1 1 0.1 Whether or not individual owns a sports car 0.05 0 0 Whether or not individual owns a van 0.1 1 0.1 Whether or not individual owns a sedan 0.08 0 0 Whether or not individual uses a bicycle to work 0.02 0 0 Whether or not individual uses the bus to work all the time -0.12 0 0 Number of autos owned in the last ten years 0.06 6 0.36 Gender (female) -0.15 0 0 Age -0.025 40 -1 Internet connection at home -0.06 1 -0.06 Married -0.12 1 -0.12 Number of kids 0.03 2 0.06 Sum = 0.49 λi = 1.632 trips/day
- 13. CEE320 Winter2006 Example • Probability of exactly “n” trips using the Poisson model: • Cumulative probability – Probability of one trip or less: P(0) + P(1) = 0.52 – Probability of at least two trips: 1 – (P(0) + P(1)) = 0.48 • Confidence level – We are 52% confident that no more than one recreational or pleasure trip will be made by the average individual in a day ( ) ( ) 20.0 !0 0632.1 0 632.1 = = − eP ( ) ( ) 32.0 !1 1632.1 1 632.1 = = − eP
- 14. CEE320 Winter2006 Mode Choice • Purpose – Predict the mode of travel for each trip • Approach – Categorized modes (SOV, HOV, bus, bike, etc.) – Generate Model
- 15. CEE320 Winter2006 A Mode Choice Model • Logit Model • Final form mk n kmnmnmk zV εβ += ∑ ∑ = s U U mk sk mk e e P Specifiable part Unspecifiable part ∑= n kmnmnmk zU β s = all available alternatives m = alternative being considered n = traveler characteristic k = traveler
- 16. CEE320 Winter2006 Discrete Choice Example Regarding the TV sitcom Gilligan’s Island, whom do you prefer?
- 17. CEE320 Winter2006 Ginger Model UGinger = 0.0699728 – 0.82331(carg) + 0.90671(mang) + 0.64341(pierceg) – 1.08095(genxg) carg = Number of working vehicles in household mang = Male indicator (1 if male, 0 if female) pierceg = Pierce Brosnan indicator for question #11 (1 if Brosnan chosen, 0 if not) genxg = generation X indicator (1 if respondent is part of generation X, 0 if not)
- 18. CEE320 Winter2006 Mary Anne Model UMary Anne = 1.83275 – 0.11039(privatem) – 0.0483453(agem) – 0.85400(sinm) – 0.16781(housem) + 0.67812(seanm) + 0.64508(collegem) – 0.71374(llm) + 0.65457(boomm) privatem = number of years spent in a private school (K – 12) agem = age in years sinm = single marital status indicator (1 if single, 0 if not) housem = number of people in household seanm = Sean Connery indicator for question #11 (1 if Connery chosen, 0 if not) collegem = college education indicator (1 if college degree, 0 if not) llm = long & luxurious hair indicator for question #7 (1 if long, 0 if not) boomm = baby boom indicator (1 if respondent is a baby boomer, 0 if not)
- 19. CEE320 Winter2006 No Preference Model Uno preference = – 9.02430x10-6(incn) – 0.53362(gunsn) + 1.13655(nojames) + 0.66619(cafn) + 0.96145(ohairn) incn = household income gunsn = gun ownership indicator (1 if any guns owned, 0 if no guns owned) nojames = No preference indicator for question #11 (1 if no preference, 0 if preference for a particular Bond) cafn = Caffeinated drink indicator for question #5 (1 if tea/coffee/soft drink, 0 if any other) ohairn = Other hair style indicator for question #7 (1 if other style indicated, 0 if any style indicated)
- 20. CEE320 Winter2006 Results 10. Regarding the TV sitcom “Gilligan’s Island” whom do your prefer? 29 90 85 30 88 89 7 112 87 0 20 40 60 80 100 120 Ginger Mary Ann No Preference #ofRespondants Survey average Model Average probabilities of selection for each choice are shown in yellow. These average percentages were converted to a hypothetical number of respondents out of a total of 207.
- 21. CEE320 Winter2006 My Results ∑ = s U U mk sk mk e e P 8201.13265.02636.01075.1 =++= −−− ∑ eeee s Usk 1815.0 8201.1 1075.1 === − ∑ e e e P s U U ginger sk mk 4221.0 8201.1 2636.0 === − ∑ e e e P s U U annemary sk mk 3964.0 8201.1 3265.0 === − ∑ e e e P s U U preferenceno sk mk Uginger = – 1.1075 Umary anne = – 0.2636 Uno preference = – 0.3265
- 22. CEE320 Winter2006 Primary References • Mannering, F.L.; Kilareski, W.P. and Washburn, S.S. (2005). Principles of Highway Engineering and Traffic Analysis, Third Edition. Chapter 8 • Transportation Research Board. (2000). Highway Capacity Manual 2000. National Research Council, Washington, D.C.
Editor's Notes
- Saigon
- Poisson gives the average number of daily trips but can also calculate the probability of making X number of trips in a day.
- ln(λ) = 0.49
- Betas are estimated using maximum likelihood