Integrated Mode Choice and Dynamic Traveler Assignment in Multimodal Transit Networks: Mathematical Formulation, Solution Procedure, and Large-Scale Application

Abstract

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.

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

“…However, the above mentioned emerging "intelligent" transport technologies are generally difficult to cast in conventional framework of macroscopic models. Nevertheless, there are valid attempts to integrate microscopic effects of new services in aggregate, macroscopic model using certain idealizing or extreme assumptions: for example, in [6] a multi-modal traffic assignment is modeled; in [7] the link flows of autonomous vehicles (AVs) are modeled by increasing the link capacities; in [8] the empty and occupied vehicle flows of SAVs are determined under system optimum flow constraints by solving a linear programming problem and in [9] the stability of the UE with AVs is examined by means of Lijapunov functions.…”

A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function

“…Computational results conducted by LeBlanc and Farhangian [20] revealed Evan's partial linearization method performed better than Frank-Wolfe's complete linearization method, while Ryu et al [21] recently demonstrated the superiority of the gradient projection (GP) algorithm over Evan's algorithm. Note that very few studies have focused on developing solution algorithms for solving large-scale problems with multiple modes in a multimodal transportation network [24][25][26].…”

A Practical Traffic Assignment Model for Multimodal Transport System Considering Low-Mobility Groups

“…However, these models considered mode choice equilibrium rather than route choice equilibrium, i.e., assigning travel demands of different trip modes into the multimodal traffic network [13]. To deal with this shortcoming, combined models that incorporated equilibriums of both modal and route choices together were proposed in numerous studies [14][15][16][21][22][23][24][25][26][27][28]. These combined models could be regarded as convex optimization problems where the travel cost structures were either separable or symmetric [31,32].…”

“…(a) Describing the equilibrium of route choice for each trip mode using the logit function based on the existing studies [22][23][24][25][26][27][28][29][30] and research requirements. This is formulated as the variational inequality problem considering low-mobility groups.…”