© 2023 Elsevier LtdUnanticipated commuter railway disruptions inevitably tend to result in passenger bunching and frustration with considerable delays. Bus bridging strategy is a timely response approach to operationally handle and ease these scheduling disturbance issues. Conventional strategies, in this case, are apt to adding new/temporary bus routes, or, alternatively, sharply amending existing routes. Nonetheless, previous approaches have mainly been dedicated to reducing the delay of stranded passengers. This was done without integrating and considering the ordinary demand with the stranded demand through optimally handling time-dependent total passenger demand based on available service capacity. Consequently, this study creates a method on how to fine-tune bus-routing changes to handle these railway disruptions. This method allows for adjusting partial bus routes to optimally solve the combined problem of the railway passengers with bus bridging and to service all the network-based bus stops. We propose a mixed integer nonlinear programming model with multitype vehicles using a rolling horizon method to minimize total passenger delay from integrated bus bridging and existing bus services. Considering the extensive problem of the bus network with variable headways, we use a customized branch-and-bound algorithm incorporating an elitist genetic algorithm and the Monte Carlo simulation method to solve the model. A case study of the Shanghai Jinshan Railway shows that the result of total passenger delay of the proposed bus bridging modeling with rolling horizon is less by 37.23% than without rolling horizon for representing the advance of knowledge, and less by 60.7% than the do-nothing situation for representing the advance over practice.
https://doi.org/10.1016/j.eswa.2023.122825Cite as:
@article{Feng_2024, title={Bus routing fine-tuning for integrated network-based demand and bus bridging for a disrupted railway system}, volume={242}, ISSN={0957-4174}, url={http://dx.doi.org/10.1016/j.eswa.2023.122825}, DOI={10.1016/j.eswa.2023.122825}, journal={Expert Systems with Applications}, publisher={Elsevier BV}, author={Feng, Yinfei and Ceder, Avishai (Avi) and Zhang, Silin and Cao, Zhichao}, year={2024}, month=may, pages={122825} }