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.

中文翻译：

---

上下文感知，基于偏好的车辆路线

车辆路线选择是个人和商业企业都使用的一项重要服务。驱动程序可能具有以不同的路由首选项为特征的不同上下文。例如，在一天中的不同时间或天气情况下，驾驶员可能会做出不同的路线决定，例如偏爱或避开高速公路。车辆轨迹数据的可用性不断提高，为上下文感知，基于偏好的数据基础提供了越来越丰富的基础车辆路线。我们旨在通过提供新的高效路由技术来提高路由质量，这些技术可以识别并考虑上下文及其偏好。特别是，我们首先提供学习上下文及其偏好的方法，然后将其应用于提高路由质量的同时确保效率。我们的解决方案包括一个离线阶段，该阶段利用上下文偏好张量来学习上下文和路由偏好之间的关系。给定特定的轨道存在上下文，我们将学习路由偏好。然后，我们将学习到的偏好从具有轨迹的上下文转移到没有轨迹的相似上下文。在网上阶段，给定上下文，我们确定相应的路由首选项并将其用于路由。为了实现效率，我们提出了基于首选项的收缩层次结构，该层次结构能够加快离线学习和在线路由的速度。利用车辆轨迹数据进行的实证研究提供了对拟议解决方案属性的洞察力，表明该解决方案能够提高质量并且高效。