基于历史轨迹数据的地图匹配集成算法

2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN) Pub Date : 2019-10-01 DOI:10.1109/ISPCE-CN48734.2019.8958632

Xin Lai, Jianhua Chen, Jingjing Cao, Fei Xia

{"title":"基于历史轨迹数据的地图匹配集成算法","authors":"Xin Lai, Jianhua Chen, Jingjing Cao, Fei Xia","doi":"10.1109/ISPCE-CN48734.2019.8958632","DOIUrl":null,"url":null,"abstract":"The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.","PeriodicalId":221535,"journal":{"name":"2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Map Matching Integration Algorithm Based on Historical Trajectory Data\",\"authors\":\"Xin Lai, Jianhua Chen, Jingjing Cao, Fei Xia\",\"doi\":\"10.1109/ISPCE-CN48734.2019.8958632\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.\",\"PeriodicalId\":221535,\"journal\":{\"name\":\"2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPCE-CN48734.2019.8958632\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPCE-CN48734.2019.8958632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

GPS技术精度不足，导致采样轨迹数据偏离实际道路。为了提高GPS轨迹数据与地图匹配的精度，提出了一种基于历史轨迹数据的地图匹配积分算法。首先分别使用投影距离和隐马尔可夫模型对匹配结果进行比较。然后找到不同路段，利用DBSCAN算法对历史轨迹数据进行聚类，调整路段;我们的实验使用卡车轨迹数据来测试算法。结果表明，地图匹配积分算法有效提高了地图匹配的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Map Matching Integration Algorithm Based on Historical Trajectory Data

The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)

自引率

0.00%

发文量