基于低频出租车轨迹的城市路网交叉口提取研究

doi:10.12082/dqxxkx.2019.190187

摘要/Abstract

摘要：

出租车GPS轨迹数据获取成本低、周期短,且覆盖面广,具有实时性及大规模性,同时其包含大量的行车记录信息,对提取数字道路信息具有巨大贡献,适用于大范围城市交通路网信息的获取和快速更新。基于GPS轨迹数据进行交叉口提取是目前的研究热点,但现有研究方法大多适用于高频GPS数据,不能很好地提取稀疏区域的交叉口,难以适应出租车轨迹点采样频率低、定位精度低、噪声点多、数据分布不均匀的特性。因此,本文聚焦于城市路网的交叉口识别,为尽可能准确、全面地提取道路交叉口信息,兼顾密集与稀疏区域,提出了一种集成识别策略,分别在矢量空间和栅格空间,采用密度峰值聚类和数学形态学处理方法提取交叉口,并设计了一种融合机制探测交叉口,最后结合主成分分析法判断交叉口的真伪性,识别真实交叉口,并去除伪交叉口,从而得到基于低频出租车轨迹的城市道路交叉口。与已有的研究方法相比,本方法提取了更多的交叉口,并与遥感图像显示一致。本文提取结果准确率为92.23%、召回率为77.26%、F值为84.08%,很好地保证了交叉口的完整性和准确性,在智能交通系统中具有一定的应用价值。

关键词: 轨迹数据, 道路交叉口, 密度峰值聚类, 低频率, 数据融合, 数学形态学

Abstract:

Taxi GPS trajectory data are of low acquisition cost, short cycle, large coverage, large-scale, and real-time. Moreover, taxi trajectory data contain a large amount of driving record information for extracting digital road information. Thus, taxi GPS trajectory data are suitable for obtaining and rapidly updating the information of large-scale urban traffic road networks. The extraction of urban road network intersections based on GPS trajectory data is currently a research hotspot. However, most of existing methods, which are applicable to the high-frequency GPS data, are difficult to adapt to taxi trajectories with low sampling frequency, low positioning accuracy, many noise points, and uneven data distribution. Therefore, existing methods are not readily applied to extract the intersections of suburb areas where taxi trajectory data are sparse or low-frequency. To extract road intersection information as accurately and comprehensively as possible, this paper proposed an integrated methodology to identify the intersections of urban road networks based on dense and sparse trajectory data. In this paper, the density peak clustering method was adopted in the vector space. Meanwhile, the mathematical morphology processing method was adopted in the grid space, where multiple resolution images were generated in the trajectory data rasterization stage. The extraction results were finally fused to achieve the purpose of extracting the road intersections of suburb areas with low traffic (i.e., sparse sampled data). Further, a fusion mechanism was designed to detect these intersections by fusing multiple results in both spaces. Finally, this paper used principal component analysis to determine the authenticity of the intersections, which was used to identify real intersections and remove pseudo intersections that were incorrectly extracted. In so doing, we obtained the urban road intersections based on the low-frequency taxi trajectory. Compared with existing methods, this method extracted more intersections and showed considerable consistency with remote sensing imagery. Besides, the accuracy evaluation shows that the extraction accuracy was 92.23%, the recall rate was 77.26% and the F-value was 84.08%. Our findings suggest that the proposed methodology can ensure the integrity and accuracy of urban road network intersections and be applied in intelligent transportation systems.

Key words: trajectory data, road intersections, density peak clustering, low-frequency, data fusion, mathematical morphology

李思宇, 向隆刚, 张彩丽, 龚健雅. 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.DOI:10.12082/dqxxkx.2019.190187

LI Siyu, XIANG Longgang, ZHANG Caili, Gong Jianya. Extraction of Urban Road Network Intersections based on Low-Frequency Taxi Trajectory Data[J]. Journal of Geo-information Science, 2019, 21(12): 1845-1854.DOI:10.12082/dqxxkx.2019.190187

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参考文献 22

[1]	Chiang Y Y, Knoblock C A. Automatic extraction of road intersection position, connectivity, and orientations from raster maps[C]. Proceedings of 16^th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2008.
[2]	傅罡 . 多源遥感数据的道路提取方法研究[D]. 北京:清华大学, 2014.
	[ Fu W . Road extraction method using multi-source remote sensing data[D]. Beijing: Tsinghua University, 2014. ]
[3]	Hu X, Li Y, Shan J , et al. Road centerline extraction in complex urban scenes from LiDAR data based on multiple features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(11):7448-7456. doi: 10.1109/TGRS.2014.2312793
[4]	郭风成, 李参海, 李宗春 , 等. 高分辨率SAR影像道路交叉口自动提取方法[J]. 测绘科学技术学报, 2017,34(2):199-203.
	[ Guo F C, Li S H, Li Z C , et al. A new method for automatic extracting road junctions from high resolution SAR images[J]. Journal of Geomatics Science and Technology, 2017,34(2):199-203. ]
[5]	Ekpenyong F, Palmerbrown D, Brimicombe A . Extracting road information from recorded GPS data using snap-drift neural network[J]. Neurocomputing, 2009,73(1):24-36. doi: 10.1016/j.neucom.2008.11.032
[6]	李润生, 司毅博, 马德宝 , 等. 基于语义规则的高分辨率影像道路交叉口提取方法[J]. 测绘科学技术学报, 2017,34(2):168-174.
	[ Li R S, Si Y B, Ma D B , et al. Road intersections extracting from remote sensing imagery based on semantic rules[J]. Journal of Geomatics Science and Technology, 2017,34(2):168-174. ]
[7]	Cao L, Krumm J. From GPS traces to a routable road map[C]. Proceedings of 17^th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2009: 3-12.
[8]	Ahmed M, Wenk C. Constructing street networks from GPS trajectories[C]// European Symposium on Algorithms, Springer Berlin Heidelberg, 2012.
[9]	Schroedl S, Wagstaff K, Rogers S , et al. Mining GPS traces for map refinement[J]. Data Mining and Knowledge Discovery, 2004,9(1):59-87. doi: 10.1023/B:DAMI.0000026904.74892.89
[10]	Davies J J, Beresford A R, Hopper A . Scalable, distributed, real-time map generation[J]. IEEE Pervasive Computing, 2006,5(4):47-54.
[11]	胡瀚, 向隆刚, 王德浩 . 出租车轨迹数据的道路提取[J]. 测绘通报, 2018(7):53-57.
	[ Hu H, Xiang L G, Wang D H . Road extraction based on taxi trajectory data[J]. Bulletin of Surveying and Mapping, 2018(7):53-57. ]
[12]	Fathi A, Krumm J. Detecting road intersections from GPS Traces[C]// Geographic Information Science, 6th International Conference, GIScience 2010, Zurich, Switzerland, September 14-17, 2010. Proceedings. Springer-Verlag, 2010.
[13]	Karagiorgou S, Pfoser D, Skoutas D. Segmentation-based road network construction[C]. Proceedings of the 21^st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , 2013: 450-453.
[14]	Wang J, Rui X, Song X , 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. doi: 10.1080/13658816.2014.944527
[15]	Wang J, Wang C, Song X , et al. Automatic intersection and trafﬁc rule detection by mining motor-vehicle GPS trajectories[J]. Computers, Environment and Urban Systems , 2017,64:19-29. doi: 10.1016/j.compenvurbsys.2016.12.006
[16]	唐炉亮, 牛乐, 杨雪 , 等. 利用轨迹大数据进行城市道路交叉口识别及结构提取[J]. 测绘学报, 2017,46(6):770-779.
	[ Tang L L, Niu L, Yang X , et al. Urban intersections recognition and construction based on big trace data[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(6):770-779. ]
[17]	谭祥爽, 王静, 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):34-38,127.
	[ Tan X S, Wang J, Song X F , et al. Intersection detection method based on data of floating car[J]. Geography and Geo-Information Science, 2015,31(5):34-38,127. ]
[18]	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 D H . Generation of road network information from massive less frequent GPS vehicle trajectories[D]. Wuhan: Wuhan University, 2017. ]
[19]	荣秋生, 颜君彪, 郭国强 . 基于DBSCAN聚类算法的研究与实现[J]. 计算机应用, 2004(4):45-46,61.
	[ Rong Q S, Yan J B, Guo G Q . Research and implementation of clustering algorithm based on DBSCAN[J]. Computer Applications, 2004(4):45-46,61. ]
[20]	冯少荣, 肖文俊 . DBSCAN聚类算法的研究与改进[J]. 中国矿业大学学报, 2008,37(1):105-111.
	[ Feng S R, Xiao W J . An improved DBSCAN clustering algorithm[J]. Journal of China University of Mining & Technology, 2008,37(1):105-111. ]
[21]	Rodriguez A, Laio A . Clustering by fast search and find of density peaks[J]. Science, 2014,344(6191):1492-1496. doi: 10.1126/science.1242072 pmid: 24970081
[22]	唐炉亮, 郑文斌, 王志强 , 等. 城市出租车上下客的GPS轨迹时空分布探测方法[J]. 地球信息科学学报, 2015,17(10):1179-1186. doi: 10.3724/SP.J.1047.2015.01179
	[ Tang L L, Zheng W B, Wang Z Q , et al. Space time analysis on the pick-up and drop-off of taxi passengers based on GPS big data[J]. Journal of Geo-information Science, 2015,17(10):1179-1186 ] doi: 10.3724/SP.J.1047.2015.01179

2.5 m提取结果	5 m提取结果	密度峰值聚类提取结果	判断结果
√	√	√	真
√	√		真
√		√	真
	√	√	真
√			待定
	√		待定
		√	待定

方法	准确率	召回率	F-值
本文方法	92.23	77.26	84.08
Ahmed方法	74.00	30.08	42.77
Davies方法	85.19	56.10	67.65