1. Trip Generation and Mode Choice
Saigon
CEE 320 Anne Goodchild

2. Outline
Trip Generation
Mode Choice
Survey

3. Trip Generation Purpose Approach 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. Motivations for Making Trips
Lifestyle
Residential choice
Work choice
Recreational choice
Kids, marriage
Money
Life stage
Technology

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

6. Trip Generation Models
Linear (simple)
Poisson (a bit better)
Poisson gives the average number of daily trips but can also calculate the probability of making X number of trips in a day.

7. 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. 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. 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:
Exactly 2 trips in a day?
Less than 2 trips in a day?
More than 2 trips in a day?

10. Example Calculations Exactly 2: Less than 2: More than 2: Less than 2
P(0) = e-(2.8)(1) =
P(1) =
P(0) + P(1) = =
More than 2
P(n>2) = 1 – ( ) =
More than 2:

11. Example Graph

12. Example Graph

13. Example: Time Between Trips

14. Example
Recreational or pleasure trips measured by λi (Poisson model):

15. ln(λ) = 0.49

16. 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. Mode Choice Purpose Approach Predict the mode of travel for each trip
Categorized modes (SOV, HOV, bus, bike, etc.)
Generate Model

18. Dilemma
Qualitative Dependent Variable
Explanatory Variables

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

20. A Mode Choice Model Logit Model Final form Specifiable part
Unspecifiable part
Betas are estimated using maximum likelihood
s = all available alternatives
m = alternative being considered
n = traveler characteristic
k = traveler

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

22. Ginger Model
UGinger = – (carg) (mang) (pierceg) – (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. Mary Anne Model
UMary Anne = – (privatem) – (agem) – (sinm) – (housem) (seanm) (collegem) – (llm) (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. No Preference Model
Uno preference = – x10-6(incn) – (gunsn) (nojames)
(cafn) (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. 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. My Results Uginger = – 1.1075 Umary anne – 0.2636 Uno preference–

27. 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 National Research Council, Washington, D.C.