Spatial temporal analysis of vehicle routing problem from online car-hailing trajectories

Abstract A range of vehicle routing problems, from routing planning that vehicles will apply to the actual route that drivers selected in their environment, depend on many factors including travel length, traffic condition, or personalized experience, etc., raising a fundamental question: To what degree is planned route align with the actual route. Here we explore the spatial temporal differences between the planned route and actual route by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car hailing trajectories. By matching the raw trajectories based on an improved HMM map-matching algorithm, we obtain the OD (origin-destination) matrix and their corresponding actual route that vehicles traveled, and planned route generated by A* routing algorithm. We used the Jaccard index to quantify the similarity between the actual route and the planned route of the same OD pair. The PRAD is detected and further analyzed from the aspects of traffic condition. By using car-hailing trajectories provided by DiDi company, we analyzed drivers' routing behavior in workday and weekend in Wuhan. The relation of PRAD with traffic condition in workday and weekend is discussed and results shown that about 65% PRAD are occurred with a serious traffic jam especially in workday.