A Green Self-Adaptive Approach for Online Map Matching

Abstract

GPS trajectory data plays a critical role for the intelligent transportation. Due to many factors like measure error, sampling error and battery power-saving requirement, the directly obtained trajectories will inaccurately align to the digital map. This requires an online map matching algorithm with high precision, low latency, and energy consumption saving. However, the existing approaches need to make some trade off among these criterions. Hence, based on the hidden Markov model, this paper proposes an adaptive online map matching algorithm 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; and 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.