MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks isolation. To fully exploit the transportation infrastructure, multimodal are sought shortest paths combining various modes of transportation. Nonetheless, current still lack performance efficiently handle interactive under realistic network conditions where traffic jams, cancelations, delays often occur. We present a separators–based algorithm (MUSE), new based graph separators travel time paths. It partitions into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common all independent so that preprocessing only executed once. MUSE relies state automaton describes sequence constrain path during online phase. support sequences mobility requires cliques, independently for each partition. also augment our with heuristics query phase achieve further speedups minimal effect correctness. provide experimental results France’s containing pedestrian, road, bicycle,