Dynamic Intercity Ride‐Sharing Optimisation Based on Two‐Stage Information Feedback

ABSTRACT Current approaches to intercity dynamic ride‐sharing mainly adopt single‐stage scheduling, where new orders are periodically batched and processed. Although effective, this strategy often causes heavy computation and delayed passenger feedback, limiting real‐time applicability. To address these issues, we propose a novel two‐stage information feedback framework combining coarse and fine scheduling. In the coarse stage, online scheduling (nearest insertion) promptly matches new orders with departed vehicles, while offline scheduling (best insertion) processes non‐departed vehicles, thus providing passengers with staged and timely feedback. In the fine stage, assignments are further optimised through large neighbourhood search, with the triggering decision modelled as a Markov decision process and learned by deep Q‐learning. This design reduces redundant computation while dynamically balancing feedback timeliness and scheduling efficiency. Unlike traditional methods, our framework is novel in integrating staged passenger feedback, hybrid heuristic optimisation and reinforcement learning‐based control. Experiments on two real‐world intercity carpooling datasets show that the method significantly reduces runtime and feedback delays while maintaining strong scheduling performance, demonstrating its potential as a practical solution for large‐scale dynamic ride‐sharing platforms.