基于出租车轨迹的可导航路网构建

doi:10.11947/j.AGCS.2021.20200470

测绘学报 ›› 2021, Vol. 50 ›› Issue (12): 1650-1662.doi: 10.11947/j.AGCS.2021.20200470

• 位置服务与地理空间信息处理 • 上一篇下一篇

基于出租车轨迹的可导航路网构建

张彩丽^1,2, 向隆刚¹, 李雅丽¹, 王文龙³

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 邵阳学院城乡建设学院, 湖南邵阳 422000;
3. 国网思极神往位置服务(北京)有限公司, 北京 100000

收稿日期:2020-09-21 修回日期:2021-05-08 发布日期:2022-01-08
通讯作者: 向隆刚 E-mail:geoxlg@whu.edu.cn
作者简介:张彩丽(1989—),女,博士,研究方向为轨迹数据挖掘、可导航路网构建等。
基金资助:
国家自然科学基金（41771474；42071432）；国家电网有限公司总部科技项目（5700-201919244A-0-0-00）

Construction of navigable road network based on taxi trajectories

ZHANG Caili^1,2, XIANG Longgang¹, LI Yali¹, WANG Wenlong³

1. State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Urban and Rural Construction College, Shaoyang University, Shaoyang 422000, China;
3. State Grid Siji Shenwang Location Service(Beijing) Co., Ltd., Beijing 100000, China

Received:2020-09-21 Revised:2021-05-08 Published:2022-01-08
Supported by:
The National Natural Science Foundation of China (Nos. 41771474;42071432);The State Grid Corporation Headquarters Technology Project(No. 5700-201919244A-0-0-00)

摘要/Abstract

摘要： 出租车轨迹不仅反映城市路网的静态几何与拓扑结构，而且蕴含转向等动态导航信息。考虑到交叉口的位置、拓扑连接及转向规则是构建可导航路网的基础与关键，本文利用出租车轨迹的空间分布与动态连接信息，采用“交叉口位置—路段几何—导航属性”的研究思路，提出一种交叉口引导的可导航路网构建方法。设计了一种顾及多模特征的交叉口集成识别技术，并引入随机森林算法，探索待定交叉口的零标注监督式去伪；在此基础上提出一种基于Delaunay三角网的多阶段路段生成方法；最后围绕交叉口，挖掘路段的转向及单双向信息，从而构建出可导航路网。与其他方法相比，本文方法在交叉口、路段及转向等可导航路网信息提取方面具有更高的完整性与准确性。

关键词: 出租车轨迹, 可导航路网构建, 交叉口识别

Abstract: Taxi trajectories not only reflect the static geometrical and topological structures of urban road network, but also carry dynamic navigation information such as turning relationship. Considering that the location, topological connection and turning rules of road intersection are the basis and key to the construction of navigable road network, this paper made full use of the spatial distribution and dynamic connection information of taxi trajectories and proposed a navigable road network generation method guided by intersection information, which adopts the research idea of “intersection location—road segment geometry—navigation attribute”. Firstly, an integrated intersection recognition technology considering multi-mode features was designed, and then the random forest algorithm was introduced to explore a zero-label supervised anti-counterfeiting strategy for eliminating false intersections; on this basis, a multi-stage road segment generation method based on Delaunay triangulation network was proposed; finally, the turning relationship and one/two-way information of road segment, which are closely related to intersections, were further captured so as to derive a navigable road network. Compared with other methods, the proposed method achieves higher completeness and accuracy for the extraction of navigable road network information such as road intersection, road segment and turning relationship.

Key words: taxi trajectory, construction of navigable road network, road intersection recognition

中图分类号:

P208

张彩丽, 向隆刚, 李雅丽, 王文龙. 基于出租车轨迹的可导航路网构建[J]. 测绘学报, 2021, 50(12): 1650-1662.

ZHANG Caili, XIANG Longgang, LI Yali, WANG Wenlong. Construction of navigable road network based on taxi trajectories[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12): 1650-1662.

参考文献

[1] HE Hao, WANG Shuyang, WANG Shicheng, et al. A road extraction method for remote sensing image based on encoder-decoder network[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(2):16-25.
[2] 李思宇,向隆刚,张彩丽,等.基于低频出租车轨迹的城市路网交叉口提取研究[J].地球信息科学学报, 2019, 21(12):1845-1854. LI Siyu, XIANG Longgang, ZHANG Caili, et al. Extraction of urban road network intersections based on low-frequency taxi trajectory data[J]. Journal of Geo-Information Science, 2019, 21(12):1845-1854.
[3] CAO Lili, KRUMM J. From GPS traces to a routable road map[C]//Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. Seattle, WA:Association for Computing Machinery, 2009:3-12.
[4] TANG Luliang, REN Chang, LIU Zhang, et al. A road map refinement method using Delaunay triangulation for big trace data[J]. ISPRS International Journal of Geo-Information, 2017, 6(2):45.
[5] 刘纪平,张用川,徐胜华,等.一种顾及道路复杂度的增量路网构建方法[J].测绘学报, 2019, 48(4):480-488. DOI:10.11947/j.AGCS.2019.20180419. LIU Jiping, ZHANG Yongchuan, XU Shenghua, et al. An incremental construction method of road network considering road complexity[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):480-488. DOI:10.11947/j.AGCS.2019.20180419.
[6] AGAMENNONI G, NIETO J I, NEBOT E M. Robust inference of principal road paths for intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1):298-308.
[7] EDELKAMP S, SCHRÖDL S. Route planning and map inference with global positioning traces[M]//Computer Science in Perspective. Berlin:Springer, 2003:128-151.
[8] LI Hengfeng, KULIK L, RAMAMOHANARAO K. Automatic generation and validation of road maps from GPS trajectory data sets[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management. Indianapolis:ACM, 2016:1523-1532.
[9] QIU Jia, WANG Ruisheng. Automatic extraction of road networks from GPS traces[J]. Photogrammetric Engineering&Remote Sensing, 2016, 82(8):593-604.
[10] 廖律超,蒋新华,邹复民,等.浮动车轨迹数据聚类的有向密度方法[J].地球信息科学学报, 2015, 17(10):1152-1161. LIAO Lüchao, JIANG Xinhua, ZOU Fumin, et al. A fast method of FCD trajectory data clustering based on the directed density[J]. Journal of Geo-Information Science, 2015, 17(10):1152-1161.
[11] DAVIES J J, BERESFORD A R, HOPPER A. Scalable, distributed, real-time map generation[J]. IEEE Pervasive Computing, 2006, 5(4):47-54.
[12] AHMED M, FASY B T, GIBSON M, et al. Choosing thresholds for density-based map construction algorithms[C]//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, WA:Association for Computing Machinery, 2015:1-10.
[13] ZHENG Renjie, LIU Qin, RAO Weixiong, et al. Topic model-based road network inference from massive trajectories[C]//Proceedings of the 18th IEEE International Conference on Mobile Data Management (MDM). Daejeon, Korea (South):IEEE, 2017:246-255.
[14] ZHANG Caili, XIANG Longgang, LI Siyu, et al. An intersection-first approach for road network generation from crowd-sourced vehicle trajectories[J]. ISPRS International Journal of Geo-Information, 2019, 8(11):473.
[15] ZOURLIDOU S, SESTER M. Intersection detection based on qualitative spatial reasoning on stopping point clusters[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B2:269-276.
[16] WANG Jing, WANG Chaoliang, SONG Xianfeng, et al. Automatic intersection and traffic rule detection by mining motor-vehicle GPS trajectories[J]. Computers, Environment and Urban Systems, 2017, 64:19-29.
[17] HUANG Jincai, DENG Min, ZHANG Yunfei, et al. Complex road intersection modelling based on low-frequency GPS track data[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, XLII-2/W7:23-28.
[18] FATHI A, KRUMM J. Detecting road intersections from GPS traces[C]//Proceedings of the 6th International Conference on Geographic Information Science. Berlin:Springer, 2010:56-69.
[19] MARIESCU-ISTODOR R, FRÄNTI P. CellNet:inferring road networks from GPS trajectories[J]. ACM Transactions on Spatial Algorithms and Systems, 2018, 4(3):8.
[20] 王德浩.基于低频出租车GPS轨迹数据的路网信息提取[D].武汉:武汉大学, 2017. WANG Dehao. Generation of road network information from massive less frequent GPS vehicle trajectories[D]. Wuhan:Wuhan University, 2017.
[21] KARAGIORGOU S, PFOSER D. On vehicle tracking data-based road network generation[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems. Redondo Beach, CA:Association for Computing Machinery, 2012:89-98.
[22] YANG Xue, TANG Luliang, NIU Le, et al. Generating lane-based intersection maps from crowdsourcing big trace data[J]. Transportation Research Part C:Emerging Technologies, 2018, 89:168-187.
[23] 庄立坚,何兆成,杨文臣,等.基于大规模浮动车数据的交叉口转向规则自动提取算法[J].武汉理工大学学报(交通科学与工程版), 2013, 37(5):1084-1088. ZHUANG Lijian, HE Zhaocheng, YANG Wenchen, et al. A large-scale floating car data-based algorithm of turning rule extraction at intersections[J]. Journal of Wuhan University of Technology (Transportation Science&Engineering), 2013, 37(5):1084-1088.
[24] 唐炉亮,于智伟,任畅,等.基于车载GPS轨迹的立体交叉口空间结构信息获取方法[J].交通运输工程学报, 2019, 19(5):170-179. TANG Luliang, YU Zhiwei, REN Chang, et al. Information acquisition method of three-dimensional intersection spatial structure based on vehicle GPS trajectory[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5):170-179.
[25] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
[26] WU Junwei, ZHU Yunlong, KU Tao, et al. Detecting road intersections from coarse-gained GPS traces based on clustering[J]. Journal of Computers, 2013, 8(11):2959-2965.
[27] ZHAO Lisheng, MAO Jiali, PU Min, et al. Automatic calibration of road intersection topology using trajectories[C]//Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE). Dallas, TX:IEEE, 2020:1633-1644.
[28] 唐炉亮,杨雪,阚子涵,等.一种基于朴素贝叶斯分类的车道数量探测[J].中国公路学报, 2016, 29(3):116-123. TANG Luliang, YANG Xue, KAN Zihan, et al. Traffic lane numbers detection based on the naive Bayesian classification[J]. China Journal of Highway and Transport, 2016, 29(3):116-123.

基于出租车轨迹的可导航路网构建

Construction of navigable road network based on taxi trajectories

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