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

In urban areas, fluctuating road speeds due to traffic congestion and accidents significantly impact bus operations and stop connectivity. Current approaches cannot maintain public transport (PT) network stability during adaptation to changing road conditions, undermining both operations and passenger experience. 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. 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. The case studies utilising Mandl’s network illustrate that our methodology can propose effective strategies for time-varying roads with any coefficient of variation. 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.