Game Theory Based Approach for Massive Route Planning in Dynamic Road Networks

With the widely use of mobile consumer electronics devices, location-based services becomes more and more popular in our lives, e.g., mapping services and ride-hailing services. Most of location-based services rely on the support of efficient and accurate route planning. However, existing route planning algorithms mainly aim to plan for a single query in dynamic road networks, while ignoring the internal flows caused by massive planned route themselves, i.e., many vehicles may take the same road segments and thus cause traffic congestion and increase the global travel time. Therefore, in this paper, we focus on massive route planning in dynamic road networks to avoid such traffic congestion caused by the internal traffic flows. We first formally define the massive route planning with minimizing the global travel time (MRP-GTT) problem. Then, we prove that the MRP-GTT problem is NP-hard. To effectively solve it, we first design a novel game theory based algorithm (GTA) to reduce the global travel time for massive route queries. Because of the low efficiency of the global gaming for all queries, we then devise a game theory with query clustering algorithm (GTA-QC) in the paper, which first clusters queries based on the source and destination locations of queries, so that only queries in the same cluster can participate in a game to improve gaming efficiency. Extensive experiments on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithms.