Data Sampling-driven Adaptive Modification of Bus Routes Under Time-Varying Road Conditions

Zihao GUO, Andrea Araldo, Mounîm El-Yacoubi

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Tracks: Main Track

Keywords: Adaptive Network Optimization, Time-Varying Road Conditions, Data Sampling-driven

Abstract:

In urban areas, fluctuating road speeds due to traffic congestion and accidents significantly impact bus operations and stop connectivity.
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Current approaches cannot maintain public transport (PT) network stability during adaptation to changing road conditions, undermining both operations and passenger experience.
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This paper proposes a data sampling-based adjustment strategy to adapt the time-varying road conditions. The innovation lies in utilising limited network modifications to enhance the existing static PT network instead of considering reconstruction from scratch or minor adjustments (such as stop-skipping), aiming to minimise both passenger travel time degradation and the operational duration of each transit line.
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Our proposed multi-objective optimization model leverages historical traffic data samples and integrates route variation quantification with penalty mechanisms to enable real-time adaptive routing decisions.
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The case studies utilising Mandl's network illustrate that our methodology can propose effective strategies for time-varying roads with any coefficient of variation.
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Experimental findings with high-variance samples indicate that our methodology decreases passenger travel time in roughly 80\% of various scenarios compared to conventional static routes, providing a more efficient solution for public transport systems.

Submission Number: 78