Forecasting and Mitigating Disruptions in Public Bus Transit Services: A Data-Driven Approach

The authors address the challenge of disruptions in public bus transit services by introducing data-driven statistical models for forecasting disruptions and an effective randomized local-search algorithm for selecting optimal locations to station substitute vehicles. Their approach aims to enhance operational efficiency and passenger experience.