https://doi.org/10.1140/epjds/s13688-025-00559-5
Research
Designing transit routes based on vehicle routing behavior determined through location-based services data
1
Department of Civil and Environmental Engineering, University of California at Berkeley, 94720, Berkeley, CA, USA
2
Department of City and Regional Planning, University of California at Berkeley, 94720, Berkeley, CA, USA
3
Department of Urban Studies and Planning, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA
4
Department of Civil and Environmental Engineering, Princeton University, 08540, Princeton, NJ, USA
Received:
23
January
2025
Accepted:
23
May
2025
Published online:
11
June
2025
The disparity between transit agency travel predictions and the unpredictable nature of real-world travel behavior contributes to inefficiencies within the transit system. To address this challenge, we propose a bottom-up transit planning approach that leverages extensive Location-Based Services (LBS) data and General Transit Feed Specification (GTFS) data for Dallas, Texas. The LBS dataset used in this study is comprised of approximately 12.43 billion records from 6.5 million users. This rich dataset is combined with GTFS data to analyze vehicle routing behavior and identify transit supply gaps. Hidden Markov Model (HMM)-based map matching aligns the LBS trajectories with a road network extracted from OpenStreetMap, allowing us to compare user demand against bus service frequency based on GTFS. To design transit improvements, we first apply k-means clustering based on Euclidean distances to group underserved road segments, and then refine these groups using a shortest-path-based clustering algorithm. This second step explicitly incorporates the actual connectivity of the road network, ensuring that proposed transit routes follow realistic travel paths. Our evaluation indicates that the proposed transit routes, whether via route extensions or new bus lines, can substantially serve the underserved areas and have the potential to significantly reduce Vehicle Miles Traveled (VMT).
Key words: Location-based service data / Public transit / Transit planning
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
