Urban Intersection Recognition and Construction Based on Big Trace Data

doi:10.11947/j.AGCS.2017.20160614

Abstract

Abstract: Intersection is an important part of the generation and renewal of urban traffic network. In this paper, a new method was proposed to detect urban intersections automatically from the spatiotemporal big trace data. Firstly, the turning point pairs were based on tracking the trace data collected by vehicles. Secondly, different types of turning point pairs were clustered by using spatial growing clustering method based on angle and distance differences, and the clustering methods of local connectivity was used to recognize the intersection. Finally, the intersection structure of multi-level road network was constructed with the range of the intersection and turning point pairs. Taking the taxi trajectory data in Wuhan city as an example, the experimental results showed that the method proposed in this paper can automatically detect and recognize the road intersection and its structure.

Key words: urban traffic network, automatic intersection recognition, intersection structure, similarity clustering, big trace data

CLC Number:

P208

TANG Luliang, NIU Le, YANG Xue, ZHANG Xia, LI Qingquan, XIAO Shilun. Urban Intersection Recognition and Construction Based on Big Trace Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 770-779.

References

[1] HILLEL A B, LERNER R, LEVI D, et al. Recent Progress in Road and Lane Detection: A Survey[J]. Machine Vision and Applications, 2014, 25(3): 727-745.
[2] 曹云刚, 王志盼, 慎利, 等. 像元与对象特征融合的高分辨率遥感影像道路中心线提取[J]. 测绘学报, 2016, 45(10): 1231-1240, 1249. DOI: 10.11947/j.AGCS.2016.20160158. CAO Yungang, WANG Zhipan, SHEN Li, et al. Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(10): 1231-1240, 1249. DOI: 10.11947/j.AGCS.2016.20160158.
[3] 郑年波, 陆锋, 李清泉. 面向导航的动态多尺度路网数据模型[J]. 测绘学报, 2010, 39(4): 428-434. ZHENG Nianbo, LU Feng, LI Qingquan. Dynamic Multi-scale Road Network Data Model for Navigation[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4): 428-434.
[4] 黄敏, 饶明雷, 李敏. 面向仿真的车道级基础路网模型及其应用[J]. 系统仿真学报, 2014, 26(3): 657-661, 681. HUANG Min, RAO Minglei, LI Min. Research of Lane-level Basic Road Network Model for Simulation and its Application[J]. Journal of System Simulation, 2014, 26(3): 657-661, 681.
[5] 朱庆, 李渊. 面向实际车道的3维道路网络模型[J]. 测绘学报, 2007, 36(4): 414-420. DOI: 10.3321/j.issn:1001-1595.2007.04.010. ZHU Qing, LI Yuan. Lane-oriented 3D Road Network Model[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(4): 414-420. DOI: 10.3321/j.issn:1001-1595.2007.04.010.
[6] UDUWARAGODA E R I A C, PERERA A S, DIAS S A D. Generating Lane Level Road Data from Vehicle Trajectories Using Kernel Density Estimation[C]//Proceedings of 2013 the 16th International IEEE Conference on Intelligent Transportation Systems. The Hague, Netherlands: IEEE, 2013: 384-391.
[7] 李晓峰, 张树清, 韩富伟, 等. 基于多重信息融合的高分辨率遥感影像道路信息提取[J]. 测绘学报, 2008, 37(2): 178-184. DOI: 10.3321/j.issn:1001-1595.2008.02.009. LI Xiaofeng, ZHANG Shuqing, HAN Fuwei, et al. Road Extraction from High Resolution Remote Sensing Images Based on Multiple Information Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(2): 178-184. DOI: 10.3321/j.issn:1001-1595.2008.02.009.
[8] 蔡红玥, 姚国清. 高分辨率遥感图像道路交叉口自动提取[J]. 国土资源遥感, 2016, 28(1): 63-71. CAI Hongyue, YAO Guoqing. Auto-extraction of Road Intersection from High Resolution Remote Sensing Image[J]. Remote Sensing for Land & Resources, 2016, 28(1): 63-71.
[9] 李怡静, 胡翔云, 张剑清, 等. 影像与LiDAR数据信息融合复杂场景下的道路自动提取[J]. 测绘学报, 2012, 41(6): 870-876. LI Yijing, HU Xiangyun, ZHANG Jianqing, et al. Automatic Road Extraction in Complex Scenes Based on Information Fusion from LiDAR and Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6): 870-876.
[10] ZHU Quanwen, CHEN Long, LI Qingquan, et al. 3D LIDAR Point Cloud based Intersection Recognition for Autonomous Driving[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Alcala de Henares, Spain: IEEE, 2012: 456-461.
[11] 贺勇, 路昊, 王春香, 等. 基于多传感器的车道级高精细地图制作方法[J]. 长安大学学报(自然科学版), 2015, 35(S1): 274-278. HE Yong, LU Hao, WANG Chunxiang, et al. Generation of Precise Lane-level Maps Based on Multi-sensors[J]. Journal of Chang'an University (Natural Science Edition), 2015, 35(S1): 274-278.
[12] AYCARD O, BAIG Q, BOTA S, et al. Intersection Safety Using Lidar and Stereo Vision Sensors[C]//Proceedings of 2011 IEEE Intelligent Vehicles Symposium. Baden: IEEE, 2011: 863-869.
[13] TANG Luliang, YANG Xue, KAN Zihan, et al. Lane-level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification[J]. ISPRS International Journal of Geo-Information, 2015, 4(4): 2660-2680.
[14] 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.
[15] CHEN Yihua, KRUMM J. Probabilistic Modeling of Traffic Lanes from GPS Traces[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose: ACM, 2010.
[16] 王振华, 胡翔云, 单杰. 众源GPS浮动车数据中城市道路中心线分级提取的栅格化方法[J]. 测绘通报, 2015(8): 22-24, 34. DOI: 10.13474/j.cnki.11-2246.2015.0236. WANG Zhenhua, HU Xiangyun, SHAN Jie. A Rasterization-based Hierarchical Approach for Urban Road Centerline Extraction from crowdsourcing GPS Floating Car Data[J]. Bulletin of Surveying and Mapping, 2015(8): 22-24, 34. DOI: 10.13474/j.cnki.11-2246.2015.0236.
[17] BETAILLE D, TOLEDO-MOREO R. Creating Enhanced Maps for Lane-level Vehicle Navigation[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 786-798.
[18] 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.
[19] 谭祥爽, 王晶, 宋现锋, 等. 基于浮动车数据的路口探测方法[J]. 地理与地理信息科学, 2015, 31(5): 34-38, 封3. TAN Xiangshuang, WANG Jing, SONG Xianfeng, et al. Detection of Road Intersections Using Floating Car Data[J]. Geography and Geo-Information Science, 2015, 31(5): 34-38, 封3.
[20] CHIANG Y Y, KNOBLOCK C A. Automatic Extraction of Road Intersection Position, Connectivity, and Orientations from Raster Maps[C]//Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Irvine: ACM, 2008: 1-10.
[21] FATHI A, KRUMM J. Detecting Road Intersections from GPS Traces[M]//FABRIKANT S I, REICHENBACHER T, VAN KREVELD M, et al. Geographic Information Science. GIScience 2010. Lecture Notes in Computer Science. Heidelberg: Springer, 2010, 6292: 56-69.
[22] LIU Jiang, CAI Baigen, WANG Yunpeng, et al. Generating Enhanced Intersection Maps for Lane Level Vehicle Positioning based Applications[J]. Procedia-social and Behavioral Sciences, 2013, 96: 2395-2403.
[23] 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.
[24] LI Pengfei, SOULEYRETTE R R. A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID-matching Technologies[J]. Computer-Aided Civil and Infrastructure Engineering, 2016, 31(5): 351-365.
[25] TANG Luliang, YANG Xue, DONG Zhen, et al. CLRIC: Collecting Lane-based Road Information via Crowdsourcing[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(9): 2552-2562.
[26] 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.