Enhancing Underutilized Bus Routes with Advance Reservations and Semiflexible Routing

Md Hishamur Rahman (), Ye Chen (), Shijie Chen (), Yanshuo Sun (), Muhammad Imran Younus Siddiqui (), Matthew Mohebbi () and Nikola Marković ()
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Md Hishamur Rahman: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112
Ye Chen: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
Shijie Chen: Department of Industrial and Manufacturing Engineering, Florida A&M University–Florida State University College of Engineering, Florida State University, Tallahassee, Florida 32310
Yanshuo Sun: Department of Industrial and Manufacturing Engineering, Florida A&M University–Florida State University College of Engineering, Florida State University, Tallahassee, Florida 32310
Muhammad Imran Younus Siddiqui: IT Curves, Gaithersburg, Maryland 20879
Matthew Mohebbi: IT Curves, Gaithersburg, Maryland 20879
Nikola Marković: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112

Transportation Science, 2025, vol. 59, issue 5, 909-931

Abstract: This paper seeks to improve an underutilized conventional bus route by converting it into a semiflexible transit system where passengers provide advance notice of their intended stops, allowing buses to skip downstream stops without demand by taking shortcuts. This approach increases stop density, reduces walking distances to and from bus stops, and maintains operational efficiency. To design this system, we develop optimization models that maximize the number of stops while adhering to tour duration and arrival time constraints. A case study in Allegany County, Maryland, demonstrates significant enhancements for routes that were both underutilized (where the probability of a stop lacking demand exceeded 45%) and had layouts conducive to substantial shortcuts. In these instances, the number of stops can be increased by up to 160%, with the actual improvement depending on route configuration, passenger demand, and advance notice requirements.

Keywords: transit; flexible routing; probabilistic analysis; online learning; approximate Bayesian inference (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:5:p:909-931

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