摘要
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Fréchet距离的全局地图匹配方法的正确率高13.6%和2.8%.
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 mapmatching 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.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2015年第3期497-503,共7页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(41301417
41201467)
重庆市基础与前沿计划资助项目(cstc2014jcyj A20017)
四川省应急测绘与防灾减灾工程技术研究中心开放基金资助项目(K2015B015)
