Integrated mode choice and dynamic traveler assignment in multimodal transit networks: Mathematical formulation, solution procedure, and large-scale application

This paper introduces an integrated mode choice-multimodal transit assignment model and solution procedure intended for large-scale urban applications. The cross-nested logit mode choice model assigns travelers to car, transit, or park-And-ride. The dynamic multimodal transit assignment-simulation model determines minimum hyperpaths and assigns and simulates transit and park-And-ride travelers iteratively until the network approaches a state of equilibrium. After a given number of iterations, the updated transit network travel times are fed into the mode choice model and the model reassigns travelers to transit, car, or park-And-ride. The outer feedback loop between the mode choice model and the transit assignment model continues until the mode probabilities for each traveler do not change between iterations. A unique contribution of the method presented in this paper is that it reaches mode choice convergence with the use of disaggregate agents (travelers) instead of aggregate modal flows at the origin-destination level. The integrated model is successfully implemented on the Chicago Transit Agency's bus and train network in Illinois. Different procedures for reaching convergence are tested; the results suggest that a gap-based formulation is more efficient than the method of successive averages.