Building a socially-aware solution to the urban transit routing problem

Abstract

Public transit ridership in the United States has been decreasing, which is especially true for bus ridership. Some of the factors that drive this decrease include fuel prices, housing density, and employment levels, but these are factors well beyond a transit agency’s control. A common reason riders cite for not taking public transit is a concern for personal well-being because the physical separation provided by a vehicle is not available in public transit, and with perceptions of safety contributing to declining ridership, agencies have limited resources available to address such concerns. So, where can public transit agencies make impacts? One potential solution is to optimize a public transit network to increase rider comfort. In this work, two new socially-aware objectives, the Familiar Stranger (FS) metric and the encounter network clustering (ENC) metric, are introduced for use in an urban transit routing problem (UTRP). The UTRP is solved using an evolutionary heuristic optimization. Empirically sourced origin-destination pairs from a university campus were used to represent potential transit users. The improved routes showed a 242% and a 119% increase for the FS and ENC metrics, respectively, compared to the existing transit network during the busiest week of the university campus’s term. The results of this work are a starting point to introduce additional socially-aware objectives in formulations used to solve the UTRP. Such objectives could serve to improve service without the funding demands of other service improvement efforts.

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