Time-dependent stochastic shortest path (s) algorithms for a scheduled transportation network (original) (raw)

This research addresses the challenges of finding optimal paths in time-dependent stochastic transportation networks, especially for scheduled public transportation like buses and trains. It begins with a review of existing shortest path algorithms that focus on deterministic edge weights and progresses to explore methods for representing public transportation networks more effectively under stochastic conditions. Key contributions include an exploration of K-shortest path (KSP) algorithms integrated with probabilistic bus arrival time distributions, setting the stage for future research on implementing these algorithms in real-world transportation scenarios.