Customizable Routing with Learnings from Past Recommendations

Kuo-Han Hung, Chiqun Zhang, Dragomir Yankov

Abstract: Finding routes in road networks is a fundamental task for routing services, but most existing methods only consider the network topology and properties and cannot handle semantic queries that express user preferences or constraints. We present a novel method that leverages historical route recommendations to prune irrelevant paths and speed up the search process. Moreover, we introduce a probabilistic modeling for path finding that can incorporate query semantics, such as "route from Seattle to Redmond with less traffic lights", and find optimal routes that satisfy them. We conduct experiments and evaluations on real-world datasets and show that our method outperforms the state-of-the-art methods in terms of runtime efficiency and route quality and can effectively answer semantic queries.

External IDs:dblp:conf/gis/HungZY24