一种顾及道路复杂度的增量路网构建方法
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摘要
针对传统路网采集和更新需要昂贵的实地测量以及大量的后续内业处理问题,提出了一种从大规模粗糙轨迹数据中自动生成路网的方法。该方法包含轨迹滤选和路网增量构建两步:第1步通过构建空间、时间、逻辑约束的规则模型,在消除数据中的噪音和冗余的同时,将原始轨迹进行合理分割,滤选形成规范轨迹集合;第2步基于信息熵计算轨迹点周围道路的复杂度,据此自动调节道路分割参数,不断将新产生的路段加入到路网,同时计算道路平均交通流量和速度等路况信息,遍历各规范轨迹的定位点重复以上处理过程,最终得到完整路网。通过昆明市200辆出租车采集的约6851万条轨迹数据进行路网构建试验,并与OpenStreetMap数据比较,证明了本文方法的有效性。与已有典型方法比较,本文方法能用更少节点提取更高质量的路网。
Aiming at the problem of needing expensive field survey and a large number of subsequent indoor processing for traditional road network acquisition and update, a method for automatically generating road network from large-scale raw trajectory data is proposed. The road network is constructed in two steps:trajectory selection and road network incremental construction. The trajectory selection process divides the raw trajectory records and filters them by constructing a spatial, temporal and rule constraint model to eliminate noise and redundancy in the data, and forms a set of canonical trajectories; the road network incrementally construction process calculates the road complexity around the processing point based on the information entropy to automatically adjust the road segmentation parameters, and continuously adds the newly generated road segments to the road network, and simultaneously calculates road traffic information such as average traffic volume and speed, and traverses the points of each trajectory, repeats the above process, and finally gets the complete road network. The road network construction experiment was carried out through about 68.51 million trajectory data collected by 200 taxis in Kunming. The results were compared with the OpenStreetMap data, which proved the effectiveness of the proposed method. When compared with the existing methods, our method can extract higher quality road network with fewer nodes.
Aiming at the problem of needing expensive field survey and a large number of subsequent indoor processing for traditional road network acquisition and update, a method for automatically generating road network from large-scale raw trajectory data is proposed. The road network is constructed in two steps:trajectory selection and road network incremental construction. The trajectory selection process divides the raw trajectory records and filters them by constructing a spatial, temporal and rule constraint model to eliminate noise and redundancy in the data, and forms a set of canonical trajectories; the road network incrementally construction process calculates the road complexity around the processing point based on the information entropy to automatically adjust the road segmentation parameters, and continuously adds the newly generated road segments to the road network, and simultaneously calculates road traffic information such as average traffic volume and speed, and traverses the points of each trajectory, repeats the above process, and finally gets the complete road network. The road network construction experiment was carried out through about 68.51 million trajectory data collected by 200 taxis in Kunming. The results were compared with the OpenStreetMap data, which proved the effectiveness of the proposed method. When compared with the existing methods, our method can extract higher quality road network with fewer nodes.
引文
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[24] DAVIES J J,BERESFORD A R,HOPPER A.Scalable,distributed,real-time map generation[J].IEEE Pervasive Computing,2006,5(4):47-54.
[25] 唐炉亮,杨雪,刘乐,等.一种众源车载GPS轨迹大数据自适应滤选方法[J].测绘学报,2016,45(12):1455-1463.DOI:10.11947/j.AGCS.2016.20160117.TANG Luliang,YANG Xue,LIU Le,et al.An adaptive filtering method based on crowdsourced big trace data[J].Acta Geodaetica et Cartographica Sinica,2016,45(12):1455-1463.DOI:10.11947/j.AGCS.2016.20160117.
[2] 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.
[3] 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.
[4] 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.
[5] AHMED M,KARAGIORGOU S,PFOSER D,et al.Map construction algorithms[M].Cham,Switzerland:Springer,2015:1-14.
[6] ZHOU Yuanchun,ZHANG Yang,GE Yong,et al.An efficient data processing framework for mining the massive trajectory of moving objects[J].Computers,Environment and Urban Systems,2017(61):129-140.
[7] 蒋益娟,李响,李小杰,等.利用车辆轨迹数据提取道路网络的几何特征与精度分析[J].地球信息科学学报,2012,14(2):165-170.JIANG Yijuan,LI Xiang,Li Xiaojie,et al.Geometrical characteristics extraction and accuracy analysis of road network based on vehicle trajectory data[J].Journal of Geo-Information Science,2012,14(2):165-170.
[8] LIU Yaqiong,SEAH H S.Points of interest recommendation from GPS trajectories[J].International Journal of Geographical Information Science,2015,29(6):953-979.
[9] 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):1271-1276,1284.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):1271-1276,1284.DOI:10.11947/j.AGCS.2015.20140591.
[10] EDELKAMP S,SCHR?DL S.Route planning and map inference with global positioning traces[M]//KLEIN R,SIX H W,WEGNER L.Computer Science in Perspective.Berlin:Springer,2003(2598):128-151.
[11] 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.
[12] 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.DOI:10.11947/j.AGCS.2017.20160614.TANG Luliang,NIU Le,YANG Xue,et al.Urban intersection recognition and construction based on big trace data[J].Acta Geodaetica et Cartographica Sinica,2017,46(6):770-779.DOI:10.11947/j.AGCS.2017.20160614.
[13] TANG Luliang,YANG Xue,KAN Zihan,et al.Lane-level road information mining from vehicle GPS trajectories based on na?ve Bayesian classification[J].ISPRS International Journal of Geo-Information,2015,4(4):2660-2680.
[14] ZHOU Yang,FANG Zhixiang,THILL J C,et al.Functionally critical locations in an urban transportation network:identification and space-time analysis using taxi trajectories[J].Computers,Environment and Urban Systems,2015(52):34-47.
[15] 杨伟,艾廷华.众源车辆轨迹加油停留行为探测与加油站点提取[J].测绘学报,2017,46(7):918-927.DOI:10.11947/j.AGCS.2017.20160657.YANG Wei,AI Tinghua.Refueling stop activity detection and gas station extraction using crowdsourcing vehicle trajectory data[J].Acta Geodaetica et Cartographica Sinica,2017,46(7):918-927.DOI:10.11947/j.AGCS.2017.20160657.
[16] BIAGIONI J,ERIKSSON J.Inferring road maps from global positioning system traces:survey and comparative evaluation[J].Transportation Research Record:Journal of the Transportation Research Board,2012,2291(1):61-71.
[17] 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.
[18] 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.
[19] AHMED M,WENK C.Constructing street networks from GPS trajectories[M]//EPSTEIN L,FERRAGINA P.Algorithms-ESA 2012.Berlin:Springer,2012:60-71.
[20] ROGERS S,LANGLEY P,WILSON C.Mining GPS data to augment road models[C]//Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego,California:ACM,1999:104-113.
[21] ZHANG Lijuan,THIEMANN F,SESTER M.Integration of GPS traces with road map[C]//Proceedings of the 3th International Workshop on Computational Transportation Science.San Diego,California:ACM,2010:17-22.
[22] 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.
[23] FATHI A,KRUMM J.Detecting road intersections from GPS traces[M]//FABRIKANT S I,REICHENBACHER T,VAN KREVELD M,et al.Geographic Information Science.Berlin:Springer,2010:56-69.
[24] DAVIES J J,BERESFORD A R,HOPPER A.Scalable,distributed,real-time map generation[J].IEEE Pervasive Computing,2006,5(4):47-54.
[25] 唐炉亮,杨雪,刘乐,等.一种众源车载GPS轨迹大数据自适应滤选方法[J].测绘学报,2016,45(12):1455-1463.DOI:10.11947/j.AGCS.2016.20160117.TANG Luliang,YANG Xue,LIU Le,et al.An adaptive filtering method based on crowdsourced big trace data[J].Acta Geodaetica et Cartographica Sinica,2016,45(12):1455-1463.DOI:10.11947/j.AGCS.2016.20160117.