A Green Self-adaptive Approach For Online Map Matching

In recent years,with the improvement of human's travel quality requirements and the advancement of technology,the field of intelligent transportation has developed rapidly.The essence of the development of intelligent transportation is the application of GPS trajectory data.Since the GPS trajectory data is not always perfect with measurement error,sampling error and battery energy consumption,these trajectory points must be processed by the online map matching algorithm before utilizing.In this paper,based on the Hidden Markov Model,this paper proposes a green adaptive online map matching algorithm is proposed to improve the performances in these perspectives at the same time: 1)the probabilistic method integrating the geometric information and topological information is developed to improve the accuracy;2)the adaptive sampling frequency method is proposed to reduce the energy consumption;3)the adaptive sliding window method is presented to reduce the output delay.The experiments demonstrate that our approaches can not only improve the matching precision,but also reduce the latency and energy consumption simultaneously.In summary,aiming at various problems in GPS trajectory data acquisition process,a green adaptive online map matching method based on hidden Markov model is proposed and implemented in this paper.The proposed algorithm solves the problems of matching accuracy,output delay and power consumption simultaneously.At the same time,the implementation of this method is of great significance to the development of various trajectory-based online applications.