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Graph-Based Analysis and Optimization of Public Transport Headways

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Public transport reliability is strongly influenced by the regularity of vehicle headways, defined as the time intervals between consecutive vehicles serving the same route. Irregular headways increase passenger waiting times, cause vehicle bunching, and reduce overall system efficiency. This paper presents a graph-based approach to the analysis and optimization of public transport headways, using the city of Bishkek, Kyrgyzstan, as a case study. The public transport network is modeled as a weighted graph, where stops are represented as vertices and route segments as edges. Headways are incorporated as temporal attributes associated with routes and vehicle movements. An optimization objective is formulated to minimize headway variability across selected routes. Using simulated operational data, the proposed approach demonstrates that graph-based modeling provides a flexible and effective framework for analyzing headway irregularities and evaluating optimization strategies. The results highlight the potential of graph-based methods to support planning and operational decision-making in urban public transport systems.
Title: Graph-Based Analysis and Optimization of Public Transport Headways
Description:
Public transport reliability is strongly influenced by the regularity of vehicle headways, defined as the time intervals between consecutive vehicles serving the same route.
Irregular headways increase passenger waiting times, cause vehicle bunching, and reduce overall system efficiency.
This paper presents a graph-based approach to the analysis and optimization of public transport headways, using the city of Bishkek, Kyrgyzstan, as a case study.
The public transport network is modeled as a weighted graph, where stops are represented as vertices and route segments as edges.
Headways are incorporated as temporal attributes associated with routes and vehicle movements.
An optimization objective is formulated to minimize headway variability across selected routes.
Using simulated operational data, the proposed approach demonstrates that graph-based modeling provides a flexible and effective framework for analyzing headway irregularities and evaluating optimization strategies.
The results highlight the potential of graph-based methods to support planning and operational decision-making in urban public transport systems.

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