Vehicle trajectory reconstruction using a tensor-based individual travel time matching method

With the development of intelligent transportation system, more and more traffic detectors have been deployed in urban road networks. However, the failure rate of equipment such as automatic license plate reader (ALPR) detector is high after long-term use. Besides, bad weather, license plate occlusion and other factors will affect the detection accuracy. These above reasons will lead to the discontinuity of vehicle trajectories, which will lead to huge errors in the calculation of traffic parameters such as traffic flow. Moreover, trajectory missing is not conducive to the supervision of traffic management departments. Therefore, this paper proposes a tensor based individual travel time matching method to recover missing vehicle trajectories. An initialized tensor will be used to estimate the travel time of every particular vehicle whose trajectory missed, and then the most likely trajectory will be calculated to recover the missing parts. Finally, the proposed method performs well in the ALPR dataset measured in Ruian, Zhejiang Province, China.