Cluster-Based Optimization of Urban Transit Hub Locations

Abstract

Choosing proper locations for urban transit hubs has always been a critical concern facing urban transportation planning agencies in China. This study proposes a mixed integer optimal location model for urban transit hubs, with the objective to minimize the demand-weighted total travel time, when explicitly taking into account traffic analysis zones as demand origins or destinations in a target urban area. An integer nonlinear programming (INLP) reformulation was developed to reduce the number of variables significantly. Bilinear constraints in the proposed INLP formulation were then remodeled into linear functions to ensure that global optimal solutions were obtained. The model was successfully applied to optimize the hub locations in Suzhou Industrial Park, China, with the result of significantly improved system performance. The effects of several critical factors, such as the number of hubs and the travel time discount coefficient on the system performance, were also investigated.