Dynamic time-dependent route planning in road networks with user preferences

Abstract

Algorithms for computing driving directions on road networks often presume constant costs on each arc. In practice, the current traffic situation significantly influences the travel time. One can distinguish traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e. g., accidents. We study the dynamic and time-dependent route planning problem, which takes both live traffic and long-term prediction into account. We propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric faster than previous approaches and allows queries in the order of milliseconds.