A Tour-Based Mode Choice Model Incorporating Inter-Personal Interactions Within the Household

1. A Tour-Based Mode Choice Model Incorporating Inter-Personal Interactions Within the Household Matthew J. Roorda Eric J. Miller UNIVERSITY OF TORONTO PROCESSUS Second International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation Models: Frameworks, Models andApplications, Toronto June 12 – 15, 2005

2. Introduction The mode choice decision involves availability constraints: Trip- level constraints - it must be possible for that person to travel by the mode for that particular trip, e.g. - for transit: transit route must run from O to D - for walk: walk distance must be reasonable (<4km) - for drive: household must have a vehicle available and the driver should have a license

3. Tour- level constraints - If a household member takes a car out of the driveway on a tour, the car typically must be returned home by that person at the end of the tour - If the car is not taken on the first trip in the tour, then the car is typically not available to that person for other trips on the tour

4. Household interactions that influence mode availability - Car allocation among household members - Joint activity participation and travel - Ridesharing among household members

5. In the literature… • Mode choice models don’t account for all of the household interactions explicitly Why? Because they are complicated, Model formulations don’t lend well to analytical solutions.

6. Design Concepts • Tour-based mode choice • Non-home-based sub-tours are also handled • Vehicle Allocation - conflicts over household vehicle usage are modelled endogenously at the household level • Ride sharing - decision of one household member to drive another household member to his/her activity location is explicitly represented • Joint Activities - joint choice of mode for joint tours • Microsimulation Framework - explicit mode choices are generated, rather than analytical probabilities

7. Individual trip-maker tour-based mode choice Vehicle allocation (when conflicting tours) Opportunities for ride share within the household Model Sequence

8. Shop Home Shop Work Home Work Individual Trip-maker Tour Mode Choice Simple Tour Tour with Subtour • Tour-based modes (e.g. car, bicycle) • Trip-based modes (e.g. transit, walk, taxi) Anchor Point

9. Individual Trip-maker Tour Mode Choice

10. Random Utility Formulation • Utility of a trip t by mode m: Umt = Vmt + mtm  feasible modes • Utility of a trip chain c with by a set of modes M: UMc = t Vmt + tmtM  feasible mode combinations • Standard RUM Assumption: UM*c UMc M,M* feasible mode sets; M*  M

11. Microsimulation framework Then: • Directly compare Utilities for individual tours • Explicitly choose the maximum utility set of modes • Replicate many times to result in probability that a set of modes is chosen for that trip chain Umt = Vmt + mt For each trip, we simulate the random error term directly for each mode

12. Vehicle Allocation 3 Conflicting With-Car Chains Work Shop Person 1 Shopping Person 2 School Person 3 3 Possible Vehicle Allocations Person 1 Person 2 Person 3 Allocation 1 Choose allocationwith highest total household utility Allocation 2 Allocation 3

13. Partial Joint Tour Pure Serve Passenger Tour En route Serve Passenger Tour Ridesharing Pure Joint Tour

14. Ridesharing We have rules to determine whether rideshare would be considered by a household: • e.g. A person can get a ride to school only if there is a driver and a vehicle available at the right time. • Driver would only sacrifice 30 min of an out-of-home activity to give a ride to another household member Then the decision to share a ride is utility based. • Rideshare only done if: passenger’s increase in utility > driver’s reduction in utility

15. Data • Random subsample drawn from the 1996 Transportation Tomorrow Survey – Toronto • Final estimation sample • 4,049 households • 7,154 trip-making persons • 8,603 tours • 19,335 individual trips

16. Likelihood estimation for one candidate parameter set: • Based on the simulated probability that the observed mode is chosen • For each replication: • Random error terms are drawn • A specific mode is chosen for each trip • Probability calculated by accumulating correct predictions

17. Distributed Computing Master . . Each slave evaluates likelihood of one parameter set in the “population” . . . Controls the geneticalgorithm, sends jobs toslaves . slaves Finding the maximum likelihood parameter set • Genetic Algorithm • Start with a random “population” of parameter sets • Selection: choose the highest likelihood sets • combine to form the next “generation” • After many generations we obtain the maximum likelihood set

18. Estimation Results

19. Conclusions (1) • Parameters are statistically significant and have the correct sign • Very few socioeconomic variables have significant parameters • Believe this is an indication that many socio-economic variables may be a proxy for household interactions • E.g. household size & number of vehicles are household attributes that influence vehicle allocation directly, therefore not in the utility function • Age & gender effects: may be better captured through competing needs for vehicles and ridesharing than by including in the utility function.

20. Conclusions (2) • Microsimulation framework is critical: We do not need an analytical expression of the choice probability Therefore: • We can therefore represent very complex personal and inter-personal decisions • Additional modes, levels of decision making can be added with little additional model complexity • A very flexible error can be used However: • Computationally burdensome for parameter estimation