1. CEE 320 Winter 2006 Trip Generation and Mode Choice CEE 320 Steve Muench

2. CEE 320 Winter 2006 Outline 1.Trip Generation 2.Mode Choice a.Survey

3. CEE 320 Winter 2006 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

4. CEE 320 Winter 2006 Motivations for Making Trips Lifestyle –Residential choice –Work choice –Recreational choice –Kids, marriage –Money Life stage Technology

5. CEE 320 Winter 2006 Reporting of Trips - Issues Under-reporting trivial trips Trip chaining Other reasons (passenger in a car for example)

6. CEE 320 Winter 2006 Trip Generation Models Linear (simple) Poisson (a bit better)

7. CEE 320 Winter 2006 Poisson Distribution Count distribution –Uses discrete values –Different than a continuous distribution P(n)=probability of exactly n trips being generated over time t n=number of trips generated over time t λ=average number of trips over time, t t=duration of time over which trips are counted (1 day is typical)

8. CEE 320 Winter 2006 Poisson Ideas Probability of exactly 4 trips being generated –P(n=4) Probability of less than 4 trips generated –P(n<4) = P(0) + P(1) + P(2) + P(3) Probability of 4 or more trips generated –P(n≥4) = 1 – P(n<4) = 1 – (P(0) + P(1) + P(2) + P(3)) Amount of time between successive trips

9. CEE 320 Winter 2006 Poisson Distribution Example Trip generation from my house is assumed Poisson distributed with an average trip generation per day of 2.8 trips. What is the probability of the following: 1.Exactly 2 trips in a day? 2.Less than 2 trips in a day? 3.More than 2 trips in a day?

10. CEE 320 Winter 2006 Example Calculations Exactly 2: Less than 2: More than 2:

11. CEE 320 Winter 2006 Example Graph

12. CEE 320 Winter 2006 Example Graph

13. CEE 320 Winter 2006 Example: Time Between Trips

14. CEE 320 Winter 2006 Example Recreational or pleasure trips measured by λ i (Poisson model):

16. CEE 320 Winter 2006 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

17. CEE 320 Winter 2006 Mode Choice Purpose –Predict the mode of travel for each trip Approach –Categorized modes (SOV, HOV, bus, bike, etc.) –Generate Model

18. CEE 320 Winter 2006 Dilemma Explanatory Variables Qualitative Dependent Variable

19. CEE 320 Winter 2006 Dilemma Home to School Distance (miles) Walk to School (yes/no variable) 0 1 0 10 1 = no, 0 = yes = observation

20. CEE 320 Winter 2006 A Mode Choice Model Logit Model Final form Specifiable partUnspecifiable part s = all available alternatives m = alternative being considered n = traveler characteristic k = traveler

21. CEE 320 Winter 2006 Discrete Choice Example Regarding the TV sitcom Gilligan’s Island, whom do you prefer?

22. CEE 320 Winter 2006 Ginger Model U Ginger = 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)

23. CEE 320 Winter 2006 Mary Anne Model U Mary 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)

24. CEE 320 Winter 2006 No Preference Model U no 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)

25. CEE 320 Winter 2006 Results 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.

26. CEE 320 Winter 2006 My Results U ginger =– 1.1075 U mary anne =– 0.2636 U no preference =– 0.3265

27. CEE 320 Winter 2006 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.