1. CEE 320 Fall 2008 Trip Generation and Mode Choice CEE 320 Anne Goodchild

2. CEE 320 Fall 2008 Trip Generation Purpose –Predict how many trips will be made –Predict exactly when a trip will be made Approach –Aggregate decision-making units (households) –Categorized trip types –Aggregate trip times (e.g., AM, PM, rush hour) –Generate Model

3. CEE 320 Fall 2008 Motivations for Making Trips Lifestyle –Residential choice –Work choice –Recreational choice –Kids, marriage –Money Life stage Technology

4. CEE 320 Fall 2008 Reporting of Trips - Issues Under-reporting trivial trips Trip chaining Other reasons (passenger in a car for example) Time consuming and expensive

5. CEE 320 Fall 2008 Trip Generation Models Linear (simple) –Number of trips is a function of user characteristics Poisson (a bit better)

6. CEE 320 Fall 2008 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)

7. CEE 320 Fall 2008 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

8. CEE 320 Fall 2008 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?

9. CEE 320 Fall 2008 Example Calculations Exactly 2: Less than 2: More than 2:

10. CEE 320 Fall 2008 Example Graph

11. CEE 320 Fall 2008 Example Graph

12. CEE 320 Fall 2008 Example: Time Between Trips

13. CEE 320 Fall 2008 Example Recreational or pleasure trips measured by λ i (Poisson model):

15. CEE 320 Fall 2008 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

16. CEE 320 Fall 2008 Mode Choice Purpose –Predict the mode of travel for each trip Approach –Categorized modes (SOV, HOV, bus, bike, etc.) –Generate Model

17. CEE 320 Fall 2008 Dilemma Explanatory Variables Qualitative Dependent Variable

18. CEE 320 Fall 2008 Dilemma Home to School Distance (miles) Walk to School (yes/no variable) 0 1 0 10 1 = no, 0 = yes = observation

19. CEE 320 Fall 2008 A Mode Choice Model Logit Model Final form Specifiable partUnspecifiable part s = all available alternatives m = alternative being considered n = traveler characteristic k = traveler

20. CEE 320 Fall 2008 Discrete Choice Example Buying a golf ball –Price –Driving distance –Life expectancy

21. CEE 320 Fall 2008 Typical ranges Consumer’s ideal Average Driving Distance: 225-275 yards Average Ball Life: 18-54 holes Price: $1.25-$1.75 Producer’s ideal Average Driving Distance: 225-275 yards Average Ball Life: 18-54 holes Price: $1.25-$1.75

22. CEE 320 Fall 2008 Would you rather have: 250 yards 54 holes $1.75 225 yards 36 holes $1.25 If we ask enough of these questions, and respondent has some underlying rational value system, we can identify the values for each attribute that would cause them to answer the way that they did.

23. CEE 320 Fall 2008 Utilities What is the respondent willing to pay for one additional yard of driving distance? What is the respondent willing to pay for one additional hole of ball life? Assume no value for the bottom of the range Calculate their utility for any ball offering, then the probability they will choose each offering.

24. CEE 320 Fall 2008 Concerns All attributes must be independent Must capture everything that is important (or use constant) Can present a set of choices and predict population’s response –Offer complete set of choices –Red bus/blue bus

25. CEE 320 Fall 2008 My Results U bus =– 1.1075 U car =– 0.2636 U walk =– 0.3265