Trip Generation and Mode Choice Saigon CEE 320 Anne Goodchild

Outline Trip Generation Mode Choice Survey

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

Motivations for Making Trips Lifestyle Residential choice Work choice Recreational choice Kids, marriage Money Life stage Technology

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

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.

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)

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

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?

Example Calculations Exactly 2: Less than 2: More than 2: Less than 2 P(0) = e-(2.8)(1) = 0.0608 P(1) = 0.1703 P(0) + P(1) = 0.0608 + 0.1703 = 0.2311 More than 2 P(n>2) = 1 – (0.2311 + 0.2384) = 0.5305 More than 2:

Example Graph

Example Graph

Example: Time Between Trips

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

ln(λ) = 0.49

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

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

Dilemma Qualitative Dependent Variable Explanatory Variables

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

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

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

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)

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)

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)

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.

My Results Uginger = – 1.1075 Umary anne – 0.2636 Uno preference – 0.3265

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.