Detection and extraction of urban road intersections using GPS trajectories of floating vehicles

doi:10.13474/j.cnki.11-2246.2021.0274

Abstract

Abstract: Road intersection plays an important role in transport hub and road network, it's also the basic data of geographic information system. GPS trajectory data of floating vehicles is easy availability and low cost, but the data contains certain amounts of noises. To alleviate the impact of noise and improve compute efficiency of GPS data, this paper proposes a method to detect and extract urban road intersections. Construct data index based on the k-nearest neighbors (KNN) algorithm. Estimate the angles between vectors and coarsely detect road intersections. And then apply three clustering algorithms (K-means, DBSCAN and hierarchical clustering algorithms) to fine extracted road intersections. We test the proposed method on GPS dataset from Chengdu city to evaluate its performance. This research can be effectively used in practical intelligent transportation scenarios.

Key words: road intersections, GPS trajectories, cluster analysis, vector angle, spatial index

CLC Number:

P208

MENG Qiuyu, SONG Ziang, WANG Jin, GE Zhijin. Detection and extraction of urban road intersections using GPS trajectories of floating vehicles[J]. Bulletin of Surveying and Mapping, 2021, 0(9): 59-63.

References

[1] 李德仁.论21世纪遥感与GIS的发展[J].武汉大学学报(信息科学版),2003,28(2):127-131.
[2] 章永来,周耀鉴.聚类算法综述[J].计算机应用,2019,39(7):1869-1882.
[3] 曾添,隋立芬,阮仁桂,等.三频非组合模型的GPS/BDS/Galileo精密定轨[J].测绘学报,2021,50(2):169-180.
[4] DAVIES J J, BERESFORD A R, HOPPER A. Scalable, distributed, real-time map generation[J]. IEEE Pervasive Computing, 2006, 5(4):47-54.
[5] LIU X, 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:ACM, 2012:669-677.
[6] 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.
[7] 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.
[8] 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.
[9] 王龙飞,刘智,金飞,等.道路交叉口自动检测算法的研究[J].测绘科学,2020,45(5):126-131.
[10] 王浩,陈冬.城市交叉口群的聚类分析分类算法[J].公路交通科技,2019,36(7):121-126.
[11] 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.
[12] 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.
[13] 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.
[14] 张朝,郭秀娟,张坤鹏.K-means算法聚类中心选取[J].吉林大学学报(信息科学版),2019, 37(4):437-441.
[15] 徐新华,谢永红.增量聚类综述及增量DBSCAN聚类算法研究[J].华北航天工业学院学报,2006,16(2):15-17.
[16] 靳延安,刘行军.一种改进的层次聚类算法[J].武汉理工大学学报(信息与管理工程版),2011, 33(6):883-886.