东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (4): 472-475.DOI: 10.12068/j.issn.1005-3026.2017.04.004

• 信息与控制 • 上一篇    下一篇

基于隐马尔可夫模型和遗传算法的地图匹配算法

吴刚, 邱煜晶, 王国仁   

  1. (东北大学 信息科学与工程学院, 辽宁 沈阳110819)
  • 收稿日期:2015-12-07 修回日期:2015-12-07 出版日期:2017-04-15 发布日期:2017-04-11
  • 通讯作者: 吴刚
  • 作者简介:吴刚(1978-),男,辽宁沈阳人,东北大学副教授; 王国仁(1966-),男,辽宁沈阳人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61332006, 61370154); 中央高校基本科研业务费专项资金资助项目(N140404009).

Map Matching Algorithm Based On Hidden Markov Model and Genetic Algorithm

WU Gang, QIU Yu-jing, WANG Guo-ren   

  1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2015-12-07 Revised:2015-12-07 Online:2017-04-15 Published:2017-04-11
  • Contact: QIU Yu-jing
  • About author:-
  • Supported by:
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摘要: 综合采用隐马尔可夫模型(HMM)和遗传算法,提出了一种新的地图匹配算法.首先初始化HMM概率矩阵,然后使用前向后向算法进行参数学习,用Viterbi算法预测一组路段序列,最后将路段序列作为种群,通过遗传算法得到最优的路段序列.采用北京市2012年出租车GPS定位数据分别对传统的基于隐马尔可夫模型的算法和新算法进行测试,实验结果表明,传统的基于隐马尔可夫模型的算法的匹配精确度低于90%,新算法的匹配精确度高达90%以上.

关键词: 地图匹配, 隐马尔可夫模型, 遗传算法, 匹配精确度, 路网数据

Abstract: A new map matching algorithm was proposed on the basis of the hidden Markov model and the genetic algorithm. Firstly, the HMM probability matrix was initialized. Then, the parameters were learned by using the forward-backward algorithm, and a set of road sections was predicted by using the Viterbi algorithm. Finally, taking section sequence as population, the optimal section sequence was obtained by using the genetic algorithm. By using the taxi GPS data from Beijing in 2012 to test the traditional algorithm based on hidden Markov model and the proposed algorithm, the results showed that the traditional algorithm based on hidden Markov model has a matching accuracy below 90% and the proposed algorithm has a matching accuracy above 90%.

Key words: map matching, hidden Markov model, genetic algorithm, matching accuracy, road network data

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