Dynamic Assignment-Simulation Methodology for Multimodal Urban Transit Networks

This paper presents an integrated transit assignment-simulation tool. Finding least cost hyperpaths in a large-scale network and assigning travelers onto these paths are computationally challenging problems. Moreover, modeling the spatial and temporal complexities in a transit network that result from the discontinuities in transit events, such as missing a connection and not receiving a seat, exacerbates the issue of capturing realism. These challenges are overcome by (a) using a least cost hyperpath algorithm that captures the multimodal, multipattern, time-, and approach-dependent features of a transit network to provide realistic optimal strategies; (b) using a gap-based assignment approach to reach fast convergence; and (c) developing a multiagent particle simulation platform that is able to capture the heterogeneities and the discontinuities in travel. The platform was tested on the Chicago Transit Authority network of 14,000 nodes and 64,000 links; 1.25 million travelers were assigned and simulated, along with 21,000 transit vehicles. The assignment-simulation framework can be used as a network evaluation tool to assist decision making at the strategic and operational levels.