基于改进AOE网络的低频浮动车数据地图匹配算法

由于低频浮动车数据时间间隔较长,现有地图匹配方法难以满足低频浮动车数据地图匹配的要求.综合考虑浮动车数据轨迹点之间的整体特性,在局部和全局地图匹配算法的基础上,提出了一种基于改进AOE网络的低频浮动车数据地图匹配方法.首先,采用相交分析判断GPS点缓冲区和候选路段的关系,以获取候选路段和候选匹配点;其次,基于四叉树空间索引和Dijkstra算法,获取候选匹配点之间的最短路径;第三,设计了一种改进AOE网络,提出了基于改进AOE网络的最短可达路径算法,以获取最终的地图匹配点;最后,对改进AOE网络的地图匹配算法进行评价,并通过实验分析了算法的时间效率和正确率.实验结果表明:基于改进AOE网络的地图匹配算法正确率为95.3%,程序执行总时间为96.8 s. 其正确率分别比点到线的局部地图匹配方法和基于弱Frchet距离的全局地图匹配方法的正确率高13.6%和2.8%. 

A Map-Matching Algorithm Based on Improved AOE Network for Low Frequency Floating Car Data

Due to the long time interval characteristic, the existing map-matching algorithms are not suitable for the low-frequency FCD (floating car data). By analyzing local map-matching algorithms and global map-matching algorithms, and overall considering the FCD trace, a map-matching algorithm for low-frequency FCD based on improved AOE (activity on edge) network was proposed. Firstly, intersection analysis between a buffer around a GPS point and road segments was carried out to acquire the candidate road segments and candidate map-matching points. Secondly, quadtree spatial index and Dijkstra algorithm were introduced to obtain the shortest path between the adjacent candidate map-matching points. Thirdly, the improved AOE network was built to search the FCD shortest path and the map-matching points were acquired. Lastly, the proposed algorithm was evaluated in terms of time efficiency and accuracy. Results show that the accuracy of the proposed algorithm is 95.3%, and the total program execution time is 96.8 s. The accuracy is respectively 13.6% and 2.8% higher than that of the local map-matching algorithm and global map-matching algorithm.