TY - JOUR
T1 - Context-aware, preference-based vehicle routing
AU - Guo, Chenjuan
AU - Yang, Bin
AU - Hu, Jilin
AU - Jensen, Christian S.
AU - Chen, Lu
PY - 2020
Y1 - 2020
N2 - Vehicle routing is an important service that is used by both private individuals and commercial enterprises. Drivers may have different contexts that are characterized by different routing preferences. For example, during different times of day or weather conditions, drivers may make different routing decisions such as preferring or avoiding highways. The increasing availability of vehicle trajectory data yields an increasingly rich data foundation for context-aware, preference-based vehicle routing. We aim to improve routing quality by providing new, efficient routing techniques that identify and take contexts and their preferences into account. In particular, we first provide means of learning contexts and their preferences, and we apply these to enhance routing quality while ensuring efficiency. Our solution encompasses an off-line phase that exploits a contextual preference tensor to learn the relationships between contexts and routing preferences. Given a particular context for which trajectories exist, we learn a routing preference. Then, we transfer learned preferences from contexts with trajectories to similar contexts without trajectories. In the on-line phase, given a context, we identify the corresponding routing preference and use it for routing. To achieve efficiency, we propose preference-based contraction hierarchies that are capable of speeding up both off-line learning and on-line routing. Empirical studies with vehicle trajectory data offer insight into the properties of proposed solution, indicating that it is capable of improving quality and is efficient.
AB - Vehicle routing is an important service that is used by both private individuals and commercial enterprises. Drivers may have different contexts that are characterized by different routing preferences. For example, during different times of day or weather conditions, drivers may make different routing decisions such as preferring or avoiding highways. The increasing availability of vehicle trajectory data yields an increasingly rich data foundation for context-aware, preference-based vehicle routing. We aim to improve routing quality by providing new, efficient routing techniques that identify and take contexts and their preferences into account. In particular, we first provide means of learning contexts and their preferences, and we apply these to enhance routing quality while ensuring efficiency. Our solution encompasses an off-line phase that exploits a contextual preference tensor to learn the relationships between contexts and routing preferences. Given a particular context for which trajectories exist, we learn a routing preference. Then, we transfer learned preferences from contexts with trajectories to similar contexts without trajectories. In the on-line phase, given a context, we identify the corresponding routing preference and use it for routing. To achieve efficiency, we propose preference-based contraction hierarchies that are capable of speeding up both off-line learning and on-line routing. Empirical studies with vehicle trajectory data offer insight into the properties of proposed solution, indicating that it is capable of improving quality and is efficient.
U2 - 10.1007/s00778-020-00608-7
DO - 10.1007/s00778-020-00608-7
M3 - Journal article
SN - 1066-8888
VL - 29
SP - 1149
EP - 1170
JO - V L D B Journal
JF - V L D B Journal
ER -