轨迹分割与图层融合的车辆轨迹线构建道路地图方法

doi:10.11947/j.AGCS.2018.20170182

测绘学报 ›› 2018, Vol. 47 ›› Issue (12): 1650-1659.doi: 10.11947/j.AGCS.2018.20170182

轨迹分割与图层融合的车辆轨迹线构建道路地图方法

杨伟, 艾廷华

武汉大学资源与环境科学学院, 湖北武汉 430079

收稿日期:2017-04-11 修回日期:2018-07-26 出版日期:2018-12-20 发布日期:2018-12-24
通讯作者: 艾廷华 E-mail:tinghua_ai@tom.com
作者简介:杨伟(1987-),男,博士生,研究方向为时空轨迹数据挖掘与行为建模。E-mail:ywgismap@whu.edu.cn
基金资助:
国家自然科学基金重点项目（41531180）；国家重点研发计划（2017YFB0503500）

A Method for Road Map Construction Based on Trajectory Segmentation and Layer Fusion Using Vehicle Track Line

YANG Wei, AI Tinghua

School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

Received:2017-04-11 Revised:2018-07-26 Online:2018-12-20 Published:2018-12-24
Supported by:
The National Natural Science Foundation of China (No. 41531180);The National Key Research and Development Program of China (No. 2017YFB0503500)

摘要/Abstract

摘要： 传统道路地图构建方法将轨迹点（线）同等对待提取道路数据，忽略车辆轨迹的空间差异性，制约其结果精度与应用范围。为此，本文根据轨迹速度将轨迹线集分割滤选为3个轨迹线子集，将轨迹方向与Delaunay三角网模型集成探测路网拓扑结构；顾及轨迹线子集的特征差异选取不同参数值和约束条件并分层提取道路几何、交通语义数据，分别构建3个道路图层；运用缓冲区方法并根据道路几何、语义特征将多个道路图层融合为单个完整道路地图。运用出租车轨迹数据进行试验分析，结果表明：该方法顾及轨迹分布差异性，能将道路几何、交通语义信息融合提取，更适于处理复杂道路结构下的轨迹线。

关键词: 众源轨迹, 轨迹分割, 数据融合, 道路地图, Delaunay三角网

Abstract: Traditional methods treat track points (lines) equally to extract road data, which ignores the spatial distribution disparity and restricts its application. Therefore, this paper proposes a new approach for map construction based on trajectory segmentation and layer fusion from vehicle tracks. First, track line subset is selected through the segmentation filtering method based on speed profile. Second, three road map layers are constructed by the Delaunay triangulation through adding different constraints according to the feature of track line subset. Third, buffer method is used to integrate multiple road layers into a single road map. An experiment using taxi GPS traces in Beijing is verified the novel method. The experimental results show that our method can extract road geometry and traffic semantic data considering the heterogeneity of trajectory, and the accuracy of result is improved compared with the two existing methods.

Key words: GPS trajectory, trajectory segmentation, data fusion, road map, Delaunay triangulation

中图分类号:

P208

杨伟, 艾廷华. 轨迹分割与图层融合的车辆轨迹线构建道路地图方法[J]. 测绘学报, 2018, 47(12): 1650-1659.

YANG Wei, AI Tinghua. A Method for Road Map Construction Based on Trajectory Segmentation and Layer Fusion Using Vehicle Track Line[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12): 1650-1659.

参考文献

[1] 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.
[2] LI Jun, QIN Qiming, XIE Chao, et al. Integrated Use of Spatial and Semantic Relationships for Extracting Road Networks from Floating Car Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2012(19):238-247.
[3] YANG Wei, AI Tinghua, LU Wei. A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories[J]. Sensors, 2018, 18(4):1261.
[4] 杨伟, 艾廷华. 运用约束Delaunay三角网从众源轨迹线提取道路边界[J]. 测绘学报, 2017, 46(2):237-245. DOI:10.11947/j.AGCS.2017.20160233. YANG Wei, AI Tinghua. The Extraction of Road Boundary from Crowdsourcing Trajectory Using Constrained Delaunay Triangulation[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(2):237-245. DOI:10.11947/j.AGCS.2017.20160233.
[5] AHMED M, KARAGIORGOU S, PFOSER D, et al. A Comparison and Evaluation of Map Construction Algorithms Using Vehicle Tracking Data[J]. GeoInformatica, 2015, 19(3):601-632.
[6] QIU Jia, WANG Ruisheng. Road Map Inference:A Segmentation and Grouping Framework[J]. ISPRS International Journal of Geo-Information, 2016, 5(8):130.
[7] 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, California:ACM, 2012:89-98.
[8] LIU Xuemei, BIAGIONI J, ERIKSSON J, et al. Mining Large-scale, Sparse GPS Traces for Map Inference:Comparison of Approaches[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China:ACM, 2012:669-677.
[9] KUNTZSCH C, SESTER M, BRENNER C. Generative Models for Road Network Reconstruction[J]. International Journal of Geographical Information Science, 2016, 30(5):1012-1039.
[10] 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, Washington:ACM, 2009:3-12.
[11] 唐炉亮, 刘章, 杨雪, 等. 符合认知规律的时空轨迹融合与路网生成方法[J]. 测绘学报, 2015, 44(11):1271-1276. DOI:10.11947/j.AGCS.2015.20140591. TANG Luliang, LIU Zhang, YANG Xue, et al. A Method of Spatio-temporal Trajectory Fusion and Road Network Generation Based on Cognitive Law[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11):1271-1276. DOI:10.11947/j.AGCS.2015.20140591.
[12] XIE Xingzhe, BING-YUNGWONG K, AGHAJAN H, et al. Inferring Directed Road Networks from GPS Traces by Track Alignment[J]. ISPRS International Journal of Geo-Information, 2015, 4(4):2446-2471.
[13] WANG Jing, RUI Xiaoping, SONG Xianfeng, et al. A Novel Approach for Generating Routable Road Maps from Vehicle GPS Traces[J]. International Journal of Geographical Information Science, 2015, 29(1):69-91.
[14] 杨伟, 艾廷华. 基于众源轨迹数据的道路中心线提取[J]. 地理与地理信息科学, 2016, 32(3):1-7. YANG Wei, AI Tinghua. Road Centerline Extraction from Crowdsourcing Trajectory Data[J]. Geography and Geo-Information Science, 2016, 32(3):1-7.
[15] DAVIES J J, BERESFORD A R, Hopper A. Scalable, Distributed, Real-time Map Generation[J]. IEEE Pervasive Computing, 2006, 5(4):47-54.
[16] WANG Suyi, WANG Yusu, LI Yanjie. Efficient Map Reconstruction and Augmentation via Topological Methods[C]//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington:ACM, 2015:25.
[17] BIAGIONI J, ERIKSSON J. Map Inference in the Face of Noise and Disparity[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems. Redondo Beach, California:ACM, 2012:79-88.
[18] LIU Xuemei, ZHU Yanmin, WANG Yin, et al. Road Recognition Using Coarse-grained Vehicular Traces[R]. Beijing:HP Labs, 2012
[19] 中华人民共和国住房和城乡建设部. CJJ 37-2012城市道路工程设计规范(2016年版)[S]. 北京:中国建筑工业出版社, 2012. Ministry of Housing and Urban-rural Development of the People's Republic of China. Code for Design of Urban Road Engineering[S]. Beijing:China Architecture and Building Press, 2012.
[20] KARAGIORGOU S, PFOSER D, SKOUTAS D. Segmentation-based Road Network Construction[C]//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Orlando, Florida:ACM, 2013:460-463.
[21] PALMA A T, BOGORNY V, KUIJPERS B, et al. A Clustering-based Approach for Discovering Interesting Places in Trajectories[C]//Proceedings of the 2008 ACM Symposium on Applied Computing. Fortaleza, Ceara, Brazil:ACM, 2008:863-868.
[22] JONES C B, BUNDY G L, WARE M J. Map Generalization with a Triangulated Data Structure[J]. Cartography and Geographic Information Systems, 1995, 22(4):317-331.
[23] LIU Qiliang, TANG Jianbo, DENG Min, et al. An Iterative Detection and Removal Method for Detecting Spatial Clusters of Different Densities[J]. Transactions in GIS, 2015, 19(1):82-106.
[24] ZHENG Yu, LI Quannan, CHEN Yukun, et al. Understanding Mobility Based on GPS Data[C]//Proceedings of the 10th International Conference on Ubiquitous Computing. New York:ACM, 2008:312-321.
[25] LYU Haiyang, SHENG Yehua, GUO Ningning, et al. Geometric Quality Assessment of Trajectory-generated VGI Road Networks Based on the Symmetric Arc Similarity[J]. Transactions in GIS, 2017, 21(5):984-1009.

轨迹分割与图层融合的车辆轨迹线构建道路地图方法

A Method for Road Map Construction Based on Trajectory Segmentation and Layer Fusion Using Vehicle Track Line

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘纪平, 王勇, 胡燕祝, 罗安, 车向红, 李朋朋, 曹元晖. 互联网泛在地理信息感知融合技术综述[J]. 测绘学报, 2022, 51(7): 1618-1628.
[2]	姜三, 江万寿. Delaunay三角网约束下的影像稳健匹配方法[J]. 测绘学报, 2020, 49(3): 322-333.
[3]	颜金彪, 吴波, 彭馨. 顾及Gestalt邻近与简化原则的平面点集形状重建[J]. 测绘学报, 2020, 49(11): 1485-1496.
[4]	刘洋, 吴自银, 赵荻能, 周洁琼, 尚继宏, 王明伟, 朱超, 鲁号号. MF多源测深数据融合方法及大洋水深模型构建[J]. 测绘学报, 2019, 48(9): 1171-1181.
[5]	赵诣, 蒋弥. 极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类[J]. 测绘学报, 2019, 48(5): 609-617.
[6]	李成名, 殷勇, 武鹏达, 刘晓丽. 一种聚集性面群中毗邻区自动识别与处理方法[J]. 测绘学报, 2019, 48(3): 363-373.
[7]	刘文凯, 刘启亮, 蔡建南. 自然邻域支持下的空间同位模式挖掘方法[J]. 测绘学报, 2019, 48(1): 95-105.
[8]	柯宝贵, 张利明, 章传银, 党亚民. 卫星测高与船载重力测量数据融合的点质量拟合法[J]. 测绘学报, 2018, 47(7): 924-929.
[9]	梁福逊, 杨必胜, 黄荣刚, 董震, 李健平. 基于多源点云的建筑物立面太阳能潜力估计[J]. 测绘学报, 2018, 47(2): 225-233.
[10]	杨伟, 艾廷华. 运用约束Delaunay三角网从众源轨迹线提取道路边界[J]. 测绘学报, 2017, 46(2): 237-245.
[11]	朱杰, 孙毅中. 多约束的平面点集形状重构方法[J]. 测绘学报, 2017, 46(2): 253-264.
[12]	王家耀. 时空大数据时代的地图学[J]. 测绘学报, 2017, 46(10): 1226-1237.
[13]	杨元喜, 李晓燕. 微PNT与综合PNT[J]. 测绘学报, 2017, 46(10): 1249-1254.
[14]	孙群. 多源矢量空间数据融合处理技术研究进展[J]. 测绘学报, 2017, 46(10): 1627-1636.
[15]	谷守周, 施闯, 党亚民, 秘金钟, 薛树强, 张龙平. 顾及轨道误差BDS/GPS实时钟差融合估计的观测权函数设计[J]. 测绘学报, 2016, 45(S2): 39-45.