Data-Driven Transit Network Design at Scale
Name
2020-yan-data-driven-transit-network-design.pdf
Description
Accepted version
Size
1.07 MB
Format
Adobe PDF
Checksum (MD5)
4700453412fd8f1d2f5ac27eed349939
Author(s)
Bertsimas, Dimitris
Ng, Yee Sian
Yan, Julia
Date Issued
2021
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Bertsimas, Dimitris, Ng, Yee Sian and Yan, Julia. 2021. "Data-Driven Transit Network Design at Scale." Operations Research, 69 (4).
Version
Author's final manuscript
Abstract
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.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/OPRE.2020.2057
