Abstract: Focusing on the issue that current real-time map matching algorithms are difficult to keep high efficiency and high accuracy simultaneously, an improved dynamic weighted real-time map matching algorithm was proposed. Firstly, considering the temporal, speed, heading and direction constraints of Global Positioning System (GPS) points and the topological structures of road network, a weighted model was constructed in the algorithm based on spatio-temporal analysis, which consisted of proximity weight, heading weight, direction weight and connectivity weight. Then according to the properties of GPS points, a dynamic weighted coefficient model was created. Lastly, the best matching road segment was selected according to the confidence level of current GPS point. The experiments were conducted on three city bus trajectories with length of 36 km in Chongqing. The average matching accuracy of the algorithm was 97.31% and the average matching delay of each GPS point was 17.9 ms. The experimental results show that compared with the contrast algorithms, the proposed algorithm has higher accuracy and efficiency, and has better performance in matching Y-junctions and parallel roads.

Key words: map matching, dynamic weight, Global Positioning System (GPS) trajectory, spatio-temporal property, Intelligent Transportation System (ITS)

摘要： 针对当前实时地图匹配算法难以同时保证匹配高准确性和高实时性的问题，提出一种基于动态权重的实时地图匹配改进算法。首先，算法考虑了相邻全球定位系统（GPS）轨迹点在时间、速度和方向上的约束关系，以及道路网拓扑结构，并基于时空特性分析，建立了距离权重、方位权重、方向权重和连通性权重组成的权重模型；然后，根据GPS轨迹点自身属性信息，建立了动态权重系数模型；最后，根据置信度水平选择最佳匹配路段。用三条总长36 km的重庆城市公交车行驶轨迹进行测试，结果显示：所提算法平均匹配正确率达到97.31%，单个轨迹点匹配平均延迟为17.9 ms。新算法匹配正确率和实时性较高，在Y形路口和平行路段的匹配效果上优于对比算法。

关键词: 地图匹配, 动态权重, 全球定位系统轨迹, 时空特性, 智能交通系统