Data-Driven Transit Network Design at Scale

• 成果类型: Article
• 署名作者: Bertsimas, Dimitris; Ng, Yee Sian; Yan, Julia
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.2057
• 发表日期: 2021
• 页码: 1118-1133
• 关键词: mass transit; transportation; optimization

摘要

Mass transit remains the most efficient way to service a densely packed commuter population. However, reliability issues and increasing competition in the transportation space have led to declining ridership across the United States, and transit agencies must also operate under tight budget constraints. Recent attempts at using bus network redesign to improve ridership have attracted attention from various transit authorities. However, the analysis seems to rely on ad hoc methods, for example, considering each line in isolation and using manual incremental adjustments with backtracking. We provide a holistic approach to designing a transit network using column generation. Our approach scales to hundreds of stops, and we demonstrate its usefulness on a case study with real data from Boston.