Analysis of taxi driving behavior and driving risk based on trajectory data

Understanding human driving style and classifying driver's risk pattern is the basis of traffic risk management. The recent rapid increase of the availability of taxi trajectory data, combined with the popular analysis techniques for big data, gives the chance of thorough analysis of taxi drivers' driving style and risk pattern. In this paper, the driving characteristics of 10674 taxies (at Qiangsheng Taxi Corporation) in a month are extracted from trajectory data. The trajectory data includes time, position, motion, as well as operating status. The method adopted in this paper is entropy weight-analytic hierarchy process (Entropy-AHP) with speed, over speed behavior, driving stability, mileage and time, and fatigue driving as first-grade indexes. The weights of indexes and risk value are calculated, then all taxi drivers are grouped into five risk grades. The risk pattern recognized from the data could be particularly helpful for insurance companies to formulate differentiated pricing strategy.