Road crowd-sensing with high spatio-temporal resolution in big data era

doi:10.11947/j.AGCS.2022.20220155

Abstract

Abstract: As one of the most important, complex, and typical carriers in the evolution of human development, the road scene is a complex of road infrastructure and activity behavior, linking and constructing the "man-land relationship". Road scene perception has developed from two-dimensional abstraction to three-dimensional refinement, from static past tense to dynamic present tense. It has become the key technical support for smart cities, intelligent transportation, and autonomous driving, and is the core technical guarantee for China's new urbanization strategy and strong transportation strategy. Based on spatio-temporal traffic data, this paper proposes a new method for road crowd-sensing with high spatio-temporal resolution based on static infrastructure "form" and dynamic activity behavior "flow". From the perspective of static road network "form", the method takes "point-line-surface-body" elements as the research context, and constructs a theoretical system of high-precision road map crowd-sensing. In terms of activity behavior "flow", we break through the limitations of traditional point pattern analysis and develop a spatio-temporal modeling and multi-scale analysis method for spatial activity flow. In this paper, we reveal the evolution pattern of road scenes under the interaction of static infrastructure "form" structure and dynamic activity behavior "flow" pattern. Furthermore, we develop a road crowd-sensing theoretical system in which "form" controls "flow", "flow" determines "form", and "form" overlaps "flow", to provide core technology and data support for the development of smart cities and intelligent transportation.

Key words: road scene, big data, crowd-sensing, static infrastructure "form", dynamic activity behavior "flow", spatio-temporal modeling, interaction analysis

CLC Number:

P208

TANG Luliang, ZHAO Zilong, YANG Xue, KAN Zihan, REN Chang, GAO Jie, LI Chaokui, ZHANG Xia, LI Qingquan. Road crowd-sensing with high spatio-temporal resolution in big data era[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 1070-1090.

References

[1] 王家耀, 成毅. 论地图学的属性和地图的价值[J]. 测绘学报, 2015, 44(3):237-241. DOI:10.11947/j.AGCS.2015.20140406. WANG Jiayao, CHENG Yi. Discussions on the attributes of cartography and the value of map[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(3):237-241. DOI:10.11947/j.AGCS.2015.20140406.
[2] 闾国年, 俞肇元, 袁林旺, 等. 地图学的未来是场景学吗?[J]. 地球信息科学学报, 2018, 20(1):1-6. LV Guonian, YU Zhaoyuan, YUAN Linwang, et al. Is the future of cartography the scenario science?[J]. Journal of Geo-information Science, 2018, 20(1):1-6.
[3] 特里·N· 克拉克, 李鹭. 场景理论的概念与分析:多国研究对中国的启示[J]. 东岳论丛, 2017, 38(1):16-24. CLARK T N, LI Lu. Conceptualization and analysis of scenario theory:implications for China from a multinational study[J]. Dongyue Tribune, 2017, 38(1):16-24.
[4] 罗伯特·斯考伯, 谢尔·伊斯雷尔. 即将到来的场景时代[M]. 赵乾坤, 周宝曜, 译. 北京:北京联合出版公司, 2014. SCOBLE R, ISRAEL S. Age of context[M]. ZHAO Qiankun, ZHOU Baoyao, trans. Beijing:Beijing United Publishing Company, 2014.
[5] 罗桑扎西, 甄峰, 张姗琪. 复杂网络视角下的城市人流空间概念模型与研究框架[J]. 地理研究, 2021, 40(4):1195-1208. LUO Sangzhaxi, ZHEN Feng, ZHANG Shanqi. A conceptual model and methodological framework for examining urban people flow space based on complex network perspective[J]. Geographical Research, 2021, 40(4):1195-1208.
[6] 李德仁. 论时空大数据的智能处理与服务[J]. 地球信息科学学报, 2019, 21(12):1825-1831. LI Deren. The intelligent processing and service of spatiotemporal big data[J]. Journal of Geo-information Science, 2019, 21(12):1825-1831.
[7] 刘经南, 方媛, 郭迟, 等. 位置大数据的分析处理研究进展[J]. 武汉大学学报(信息科学版), 2014, 39(4):379-385. LIU Jingnan, FANG Yuan, GUO Chi, et al. Research progress in location big data analysis and processing[J]. Geomatics and Information Science of Wuhan University, 2014, 39(4):379-385.
[8] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057. LI Deren. Towards geo-spatial information science in big data era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057.
[9] 李清泉, 李德仁. 大数据GIS[J]. 武汉大学学报(信息科学版), 2014, 39(6):641-644, 666. LI Qingquan, LI Deren. Big data GIS[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):641-644, 666.
[10] 吴华意, 黄蕊, 游兰, 等. 出租车轨迹数据挖掘进展[J]. 测绘学报, 2019, 48(11):1341-1356. DOI:10.11947/j.AGCS.2019.20190210. WU Huayi, HUANG Rui, YOU Lan, et al. Recent progress in taxi trajectory data mining[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11):1341-1356. DOI:10.11947/j.AGCS.2019.20190210.
[11] 陆化普, 孙智源, 屈闻聪. 大数据及其在城市智能交通系统中的应用综述[J]. 交通运输系统工程与信息, 2015, 15(5):45-52. LU Huapu, SUN Zhiyuan, QU Wencong. Big data and its applications in urban intelligent transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5):45-52.
[12] 牟乃夏, 张恒才, 陈洁, 等. 轨迹数据挖掘城市应用研究综述[J]. 地球信息科学学报, 2015, 17(10):1135-1142. MOU Naixia, ZHANG Hengcai, CHEN Jie, et al. A review on the application research of trajectory data mining in urban cities[J]. Journal of Geo-Information Science, 2015, 17(10):1135-1142.
[13] 刘瑜, 康朝贵, 王法辉. 大数据驱动的人类移动模式和模型研究[J]. 武汉大学学报(信息科学版), 2014, 39(6):660-666. LIU Yu, KANG Chaogui, WANG Fahui. Towards big data-driven human mobility patterns and models[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):660-666.
[14] 杨延杰, 尹丹, 刘紫玟, 等. 基于大数据的流空间研究进展[J]. 地理科学进展, 2020, 39(8):1397-1411. YANG Yanjie, YIN Dan, LIU Ziwen, et al. Research progress on the space of flow using big data[J]. Progress in Geography, 2020, 39(8):1397-1411.
[15] 黄建华, 孟伟强, 吴飞霞. 基于信令数据的人流时空分布和移动模式研究[J]. 计算机工程与应用, 2019, 55(23):53-63. HUANG Jianhua, MENG Weiqiang, WU Feixia. Research of human flow spatial-temporal distribution and mobility pattern based on signaling data[J]. Computer Engineering and Applications, 2019, 55(23):53-63.
[16] 薛冰, 李京忠, 肖骁, 等. 基于兴趣点(POI)大数据的人地关系研究综述:理论、方法与应用[J]. 地理与地理信息科学, 2019, 35(6):51-60. XUE Bing, LI Jingzhong, XIAO Xiao, et al. Overview of man-land relationship research based on POI data:theory, method and application[J]. Geography and Geo-information Science, 2019, 35(6):51-60.
[17] 唐炉亮, 杨雪, 牛乐, 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, NIU 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.
[18] HAN Songlai, WANG Jinling. Integrated GPS/INS navigation system with dual-rate Kalman filter[J]. GPS Solutions, 2012, 16(3):389-404.
[19] LEE W C, KRUMM J. Trajectory preprocessing[M]//ZHENG Yu, ZHOU Xiaofang. Computing with Spatial Trajectories. New York:Springer, 2011:3-33.
[20] 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.
[21] TANG Luliang, YANG Xue, KAN Zihan, et al. Lane-level road information mining from vehicle GPS trajectories based on naive bayesian classification[J]. ISPRS International Journal of Geo-Information, 2015, 4(4):2660-2680.
[22] YANG Xue, TANG Luliang, ZHANG Xia, et al. A data cleaning method for big trace data using movement consistency[J]. Sensors, 2018, 18(3):824.
[23] 李霖, 杨蕾. 公众参与的兴趣点数据有效性效验方法[J]. 测绘科学, 2015, 40(7):98-103. LI Lin, YANG Lei. A verifying method on validity of POI data involving public participation[J]. Science of Surveying and Mapping, 2015, 40(7):98-103.
[24] MACEACHREN A M, GAHEGAN M, PIKE W, et al. Geovisualization for knowledge construction and decision support[J]. IEEE Computer Graphics and Applications, 2004, 24(1):13-17.
[25] 禹文豪, 艾廷华, 周启. 设施POI的局部空间同位模式挖掘及范围界定[J]. 地理与地理信息科学, 2015, 31(4):6-11. YU Wenhao, AI Tinghua, ZHOU Qi. Regional co-location pattern mining and scoping from urban facility POI[J]. Geography and Geo-Information Science, 2015, 31(4):6-11.
[26] 陈世莉, 陶海燕, 李旭亮, 等. 基于潜在语义信息的城市功能区识别——广州市浮动车GPS时空数据挖掘[J]. 地理学报, 2016, 71(3):471-483. CHEN Shili, TAO Haiyan, LI Xuliang, et al. Discovering urban functional regions using latent semantic information:Spatiotemporal data mining of floating cars GPS data of Guangzhou[J]. Acta Geographica Sinica, 2016, 71(3):471-483.
[27] 赵彦云, 张波, 周芳. 基于POI的北京市"15分钟社区生活圈"空间测度研究[J]. 调研世界, 2018(5):17-24. ZHAO Yanyun, ZHANG Bo, ZHOU Fang. Research on the spatial measurement of "15-minute community life circle" in Beijing based on POI[J]. The World of Survey and Research, 2018(5):17-24.
[28] 廖心治, 王华, 赵万民. 融合地图数据的山地城市医疗设施服务覆盖评估方法研究[J]. 地球信息科学学报, 2021, 23(4):604-616. LIAO Xinzhi, WANG Hua, ZHAO Wanmin. Evaluation method of medical facilities service coverage in mountainous cities based on map data[J]. Journal of Geo-information Science, 2021, 23(4):604-616.
[29] JAHROMI K K, ZIGNANI M, GAITO S, et al. Simulating human mobility patterns in urban areas[J]. Simulation Modelling Practice and Theory, 2016, 62:137-156.
[30] NOULAS A, SCELLATO S, LATHIA N, et al. A random walk around the city:new venue recommendation in location-based social networks[C]//Proceedings of 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. Amsterdam:IEEE, 2012:144-153.
[31] 曲畅, 任玉环, 刘亚岚, 等. POI辅助下的高分辨率遥感影像城市建筑物功能分类研究[J]. 地球信息科学学报, 2017, 19(6):831-837. QU Chang, REN Yuhuan, LIU Yalan, et al. Functional classification of urban buildings in high resolution remote sensing images through POI-assisted analysis[J]. Journal of Geo-information Science, 2017, 19(6):831-837.
[32] 陈瑞. 基于多源POI数据的匹配融合方法研究[D]. 兰州:兰州交通大学, 2014. CHEN Rui. Study on the method of matching and fusion based on the multi-source POI data[D]. Lanzhou:Lanzhou Jiaotong University, 2014.
[33] 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.
[34] 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.
[35] UDUWARAGODA E R I A C M, PERERA A S, DIAS S A D. Generating lane level road data from vehicle trajectories using kernel density estimation[C]//Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013). Hague:IEEE, 2013:384-391.
[36] 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.
[37] 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, 2010:81-88.
[38] 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.
[39] 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.
[40] YANG Xue, TANG Luliang, NIU Le, et al. Generating lane-based intersection maps from crowdsourcing big trace data[J]. Transportation Research Part C:Emerging Technologies, 2018, 89:168-187.
[41] 尹彤, 黄鹤, 郭迟, 等. 面向自动驾驶的高精地图生产技术及数据模型标准化探讨[J]. 中国标准化, 2021(4):33-37. YIN Tong, HUANG He, GUO Chi, et al. Discussion on high-definition map production technology and data model standardization for autonomous driving[J]. China Standardization, 2021(4):33-37.
[42] 侯翘楚, 李必军, 蔡毅. 高分辨率遥感影像的车道级高精地图要素提取[J]. 测绘通报, 2021(3):38-43. DOI:10.13474/j.cnki.11-2246.2021.0075. HOU Qiaochu, LI Bijun, CAI Yi. High-precision lane-level map elements extracting based on high-resolution remote sensing image[J]. Bulletin of Surveying and Mapping, 2021(3):38-43. DOI:10.13474/j.cnki.11-2246.2021.0075.
[43] 钟棉卿. 基于移动激光雷达数据的路面状况检测方法研究[D]. 西安:长安大学, 2020. ZHONG Mianqing. Research on methods of pavement condition survey using mobile laser scanning data[D]. Xi'an:Chang'an University, 2020.
[44] ZHENG Ling, LI Bijun, YANG Bo, et al. Lane-level road network generation techniques for lane-level maps of autonomous vehicles:a survey[J]. Sustainability, 2019, 11(16):4511.
[45] 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.
[46] 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.
[47] YANG Xue, TANG Luliang, STEWART K, et al. Automatic change detection in lane-level road networks using GPS trajectories[J]. International Journal of Geographical Information Science, 2018, 32(3):601-621.
[48] TANG Jianbo, DENG Min, HUANG Jincai, et al. An automatic method for detection and update of additive changes in road network with GPS trajectory data[J]. ISPRS International Journal of Geo-Information, 2019, 8(9):411.
[49] CROOKS A, PFOSER D, JENKINS A, et al. Crowdsourcing urban form and function[J]. International Journal of Geographical Information Science, 2015, 29(5):720-741.
[50] 崔海福. 基于浮动车数据的多维度城市功能区划分研究[D]. 武汉:中国地质大学, 2021. CUI Haifu. The study of multidimensional urban functional district division based on the floating car data[D]. Wuhan:China University of Geosciences, 2021.
[51] YUAN Jing, ZHENG Yu, XIE Xing. Discovering regions of different functions in a city using human mobility and POIs[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. Beijing:Association for Computing Machinery, 2012:186-194.
[52] 王胜利. 深度学习在城市功能区域划分中的应用研究[D]. 成都:电子科技大学, 2018. WANG Shengli. Urban functional regions discovering based on deep learning[D]. Chengdu:University of Electronic Science and Technology of China, 2018.
[53] 凌鹏, 诸彤宇, 周轶, 等. 基于人群出行行为轨迹的城市功能区识别[J/OL]. 计算机工程:1-8.[2022-04-07]. https://kns.cnki.net/kcms/detail/31.1289.TP.20210728.1849.022.html. LING Peng, ZHU Tongyu, ZHOU Yi, et al. Urban functional area recognition based on crowd travel behavior trajectory[J/OL]. Computer Engineering:1-8.[2022-04-07]. https://kns.cnki.net/kcms/detail/31.1289.TP.20210728.1849.022.html.
[54] ZHI Ye, LI Haifeng, WANG Dashan, et al. Latent spatio-temporal activity structures:a new approach to inferring intra-urban functional regions via social media check-in data[J]. Geo-spatial Information Science, 2016, 19(2):94-105.
[55] LIU Xiaoping, HE Jialv, YAO Yao, et al. Classifying urban land use by integrating remote sensing and social media data[J]. International Journal of Geographical Information Science, 2017, 31(8):1675-1696.
[56] TANG Luliang, GAO Jie, REN Chang, et al. Detecting and evaluating urban clusters with spatiotemporal big data[J]. Sensors, 2019, 19(3):461.
[57] 刘耀丹. 立体交通三维道路网快速构建算法研究[D]. 郑州:郑州大学, 2017. LIU Yaodan. Fast construction algorithm of the three-dimensional road network[D]. Zhengzhou:Zhengzhou University, 2017.
[58] 朱庆. 三维GIS及其在智慧城市中的应用[J]. 地球信息科学学报, 2014, 16(2):151-157. ZHU Qing. Full three-dimensional GIS and its key roles in smart city[J]. Journal of Geo-information Science, 2014, 16(2):151-157.
[59] 杜建丽, 陈动, 张振鑫, 等. 建筑点云几何模型重建方法研究进展[J]. 遥感学报, 2019, 23(3):374-391. DU Jianli, CHEN Dong, ZHANG Zhenxin, et al. Research progress of building reconstruction via airborne point clouds[J]. Journal of Remote Sensing, 2019, 23(3):374-391.
[60] 苗志成, 杨永崇, 于庆和, 等. 贴近摄影测量在单体建筑物精细化建模中的应用[J]. 遥感信息, 2021, 36(5):107-113. MIAO Zhicheng, YANG Yongchong, YU Qinghe, et al. Application of nap-of-the-object photogrammetry in fine modeling of single building[J]. Remote Sensing Information, 2021, 36(5):107-113.
[61] 符钟壬. 基于倾斜摄影测量与激光雷达点云匹配的建筑物三维模型研究[D]. 昆明:云南大学, 2020. FU Zhongren. Research on building three-dimensional model based on oblique photogrammetry and LiDAR point cloud data registration[D]. Kunming:Yunnan University, 2020.
[62] CAO Rujun, ZHANG Yongjun, LIU Xinyi, et al. 3D building roof reconstruction from airborne LiDAR point clouds:a framework based on a spatial database[J]. International Journal of Geographical Information Science, 2017, 31(7):1359-1380.
[63] 唐炉亮, 于智伟, 任畅, 等. 基于车载GPS轨迹的立体交叉口空间结构信息获取方法[J]. 交通运输工程学报, 2019, 19(5):170-179. TANG Luliang, YU Zhiwei, REN Chang, et al. Information acquisition method of three-dimensional intersection spatial structure based on vehicle GPS trajectory[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5):170-179.
[64] YEH A G O, ZHONG Teng, YUE Yang. Angle difference method for vehicle navigation in multilevel road networks with a three-dimensional transport GIS database[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(1):140-152.
[65] REN Chang, TANG Luliang, YANG Xue, et al. Mapping grade-separated junctions in detail using crowdsourced trajectory data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. DOI:10.1109/TITS.2021.3054910.
[66] CASTELLS M. The informational city:Information technology, economic restructuring, and the urban-regional process[M]. Oxford:B. Blackwell, 1989.
[67] 沈丽珍, 顾朝林. 区域流动空间整合与全球城市网络构建[J]. 地理科学, 2009, 29(6):787-793. SHEN Lizhen, GU Chaolin. Integration of regional space of flows and construction of global urban network[J]. Scientia Geographica Sinica, 2009, 29(6):787-793.
[68] BORRUSO G. Network density estimation:analysis of point patterns over a network[C]//International Conference on Computational Science and Its Applications. Singapore:Springer, 2005:126-132.
[69] BORRUSO G. Network density estimation:a GIS approach for analysing point patterns in a network space[J]. Transactions in GIS, 2008, 12(3):377-402.
[70] 萧世伦, 方志祥. 从时空GIS视野来定量分析人类行为的思考[J]. 武汉大学学报(信息科学版), 2014, 39(6):667-670. SHAW Shihlung, FANG Zhixiang. Rethinking human behavior research from the perspective of space-time GIS[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):667-670.
[71] YU Hongbo, SHAW S L. Exploring potential human activities in physical and virtual spaces:a spatio-temporal GIS approach[J]. International Journal of Geographical Information Science, 2008, 22(4):409-430.
[72] 陈洁, 萧世伦, 陆锋. 面向人类行为研究的时空GIS[J]. 地球信息科学学报, 2016, 18(12):1583-1587. CHEN Jie, SHAW ShihLung, LU Feng. A space-time GIS approach for human behavior studies[J]. Journal of Geo-information Science, 2016, 18(12):1583-1587.
[73] GVTING R H, DE ALMEIDA V T, DING Zhiming. Modeling and querying moving objects in networks[J]. The VLDB Journal, 2006, 15(2):165-190.
[74] LONG J A, NELSON T A. A review of quantitative methods for movement data[J]. International Journal of Geographical Information Science, 2013, 27(2):292-318.
[75] YANG Lin, KWAN M P, PAN Xiaofang, et al. Scalable space-time trajectory cube for path-finding:a study using big taxi trajectory data[J]. Transportation Research Part B:Methodological, 2017, 101:1-27.
[76] TANG Jinjun, LIU Fang, WANG Yinhai, et al. Uncovering urban human mobility from large scale taxi GPS data[J]. Physica A:Statistical Mechanics and Its Applications, 2015, 438:140-153.
[77] PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3):1065-1076.
[78] BAILEY T C, GATRELL A C. Interactive spatial data analysis[M]. Harlow Essex:Longman Scientific & Technical, 1995.
[79] SILVERMAN B W. Density estimation for statistics and data analysis[M]. London:Routledge, 2018.
[80] FLAHAUT B, MOUCHART M, SAN MARTIN E, et al. The local spatial autocorrelation and the kernel method for identifying black zones:a comparative approach[J]. Accident Analysis & Prevention, 2003, 35(6):991-1004.
[81] TIMOTHOE P, NICOLAS L B, EMANUELE S, et al. A network based kernel density estimator applied to barcelona economic activities[C]//Proceedings of 2010 International Conference on Computational Science and its Applications. Fukuoka:ACM, 2010:32-45.
[82] YING Lingxiao, SHEN Zehao, CHEN Jiding, et al. Spatiotemporal patterns of road network and road development priority in three parallel rivers region in Yunnan, China:an evaluation based on modified kernel distance estimate[J]. Chinese Geographical Science, 2014, 24(1):39-49.
[83] CAI Xuejiao, WU Zhifeng, CHENG Jiong. Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation[J]. International Journal of Geographical Information Science, 2013, 27(2):222-230.
[84] ERDOGAN S, YILMAZ I, BAYBURA T, et al. Geographical information systems aided traffic accident analysis system case study:city of Afyonkarahisar[J]. Accident Analysis & Prevention, 2008, 40(1):174-181.
[85] HA H H, THILL J C. Analysis of traffic hazard intensity:a spatial epidemiology case study of urban pedestrians[J]. Computers, Environment and Urban Systems, 2011, 35(3):230-240.
[86] LAHR H. An improved test for earnings management using kernel density estimation[J]. European Accounting Review, 2014, 23(4):559-591.
[87] OKABE A, SATOH T, SUGIHARA K. A kernel density estimation method for networks, its computational method and a GIS-based tool[J]. International Journal of Geographical Information Science, 2009, 23(1):7-32.
[88] LEE D, HAHN M. A study on density map based crash analysis[C]//Proceedings of 2014 International Conference on Information Science & Applications (ICISA). Seoul:IEEE, 2014:1-3.
[89] TANG Luliang, KAN Zihan, ZHANG Xia, et al. Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data[J]. Cartography and Geographic Information Science, 2016, 43(5):417-426.
[90] TANG Luliang, KAN Zihan, ZHANG Xia, et al. A network kernel density estimation for linear features in space-time analysis of big trace data[J]. International Journal of Geographical Information Science, 2016, 30(9):1717-1737.
[91] 唐炉亮, 阚子涵, 刘汇慧, 等. 网络空间中线要素的核密度估计方法[J]. 测绘学报, 2017, 46(1):107-113. DOI:10.11947/j.AGCS.2017.20150158. TANG Luliang, KAN Zihan, LIU Huihui, et al. A kernel density estimation method for linear features in network space[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1):107-113. DOI:10.11947/j.AGCS.2017.20150158.
[92] RIPLEY B D. The second-order analysis of stationary point processes[J]. Journal of Applied Probability, 1976, 13(2):255-266.
[93] GETIS A, FRANKLIN J. Second-order neighborhood analysis of mapped point patterns[M]//ANSELIN L, REY S. Perspectives on Spatial Data Analysis. Berlin:Springer, 2010:93-100.
[94] OKABE A, YAMADA I. The K-function method on a network and its computational implementation[J]. Geographical Analysis, 2001, 33(3):271-290.
[95] DAY P L, PEARCE J. Obesity-promoting food environments and the spatial clustering of food outlets around schools[J]. American Journal of Preventive Medicine, 2011, 40(2):113-121.
[96] YAMADA I, THILL J C. Comparison of planar and network K-functions in traffic accident analysis[J]. Journal of Transport Geography, 2004, 12(2):149-158.
[97] 王结臣, 卢敏, 苑振宇, 等. 基于Ripley's K函数的南京市ATM网点空间分布模式研究[J]. 地理科学, 2016, 36(12):1843-1849. WANG Jiechen, LU Min, YUAN Zhenyu, et al. Point pattern analysis of ATMs distribution based on ripley's K-function method in Nanjing city[J]. Scientia Geographica Sinica, 2016, 36(12):1843-1849.
[98] TAO R, THILL J C. Spatial cluster detection in spatial flow data[J]. Geographical Analysis, 2016, 48(4):355-372.
[99] KAN Zihan, KWAN M P, TANG Luliang. Ripley's K-function for network-constrained flow data[J]. Geographical Analysis, 2021. DOI:10.1111/gean.12300.
[100] 许学强, 周一星, 宁越敏. 城市地理学[M]. 2版. 北京:高等教育出版社, 2009. XU Xueqiang, ZHOU Yixing, NING Yuemin. Urban geography[M]. 2nd ed. Beijing:Higher Education Press, 2009.
[101] 钮心毅, 丁亮, 宋小冬. 基于手机数据识别上海中心城的城市空间结构[J]. 城市规划学刊, 2014(6):61-67. NIU Xinyi, DING Liang, SONG Xiaodong. Understanding urban spatial structure of shanghai central city based on mobile phone data[J]. Urban Planning Forum, 2014(6):61-67.
[102] 王德, 王灿, 谢栋灿, 等. 基于手机信令数据的上海市不同等级商业中心商圈的比较——以南京东路、五角场、鞍山路为例[J]. 城市规划学刊, 2015(3):50-60. WANG De, WANG Can, XIE Dongcan, et al. Comparison of retail trade areas of retail centers with different hierarchical levels:a case study of east Nanjing road, Wujiaochang, Anshan road in Shanghai[J]. Urban Planning Forum, 2015(3):50-60.
[103] 罗桑扎西, 甄峰. 基于手机数据的城市公共空间活力评价方法研究——以南京市公园为例[J]. 地理研究, 2019, 38(7):1594-1608. LUO Sangzhaxi, ZHEN Feng. How to evaluate public space vitality based on mobile phone data:an empirical analysis of Nanjing's parks[J]. Geographical Research, 2019, 38(7):1594-1608.
[104] 秦昆, 周勍, 徐源泉, 等. 城市交通热点区域的空间交互网络分析[J]. 地理科学进展, 2017, 36(9):1149-1157. QIN Kun, ZHOU Qing, XU Yuanquan, et al. Spatial interaction network analysis of urban traffic hotspots[J]. Progress in Geography, 2017, 36(9):1149-1157.
[105] 郑林江, 赵欣, 蒋朝辉, 等. 基于出租车轨迹数据的城市热点出行区域挖掘[J]. 计算机应用与软件, 2018, 35(1):1-8. ZHENG Linjiang, ZHAO Xin, JIANG Zhaohui, et al. Mining urban attractive areas using taxi trajectory data[J]. Computer Applications and Software, 2018, 35(1):1-8.
[106] LWIN K K, SEKIMOTO Y, TAKEUCHI W. Estimation of hourly link population and flow directions from mobile CDR[J]. ISPRS International Journal of Geo-Information, 2018, 7(11):449.
[107] GAO Jie, YANG Xue, TANG Luliang, et al. Understanding urban hospital bypass behaviour based on big trace data[J]. Cities, 2020, 103:102739.
[108] 孙道胜, 柴彦威. 城市社区生活圈体系及公共服务设施空间优化——以北京市清河街道为例[J]. 城市发展研究, 2017, 24(9):7-14, 25. SUN Daosheng, CHAI Yanwei. Study on the urban community life sphere system and the optimization of public service facilities:a case study of Qinghe area in Beijing[J]. Urban Development Studies, 2017, 24(9):7-14, 25.
[109] 柴彦威, 李春江, 张艳. 社区生活圈的新时间地理学研究框架[J]. 地理科学进展, 2020, 39(12):1961-1971. CHAI Yanwei, LI Chunjiang, ZHANG Yan. A new time-geography research framework of community life circle[J]. Progress in Geography, 2020, 39(12):1961-1971.
[110] XIE Peng, LI Tianrui, LIU Jia, et al. Urban flow prediction from spatiotemporal data using machine learning:a survey[J]. Information Fusion, 2020, 59:1-12.
[111] 方孟元, 唐炉亮, 杨雪, 等. 基于低频GNSS轨迹的转向级城市交通信息精细预测[J]. 测绘学报, 2021, 50(11):1469-1477. DOI:10.11947/j.AGCS.2021.20210252. FANG Mengyuan, TANG Luliang, YANG Xue, et al. Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11):1469-1477. DOI:10.11947/j.AGCS.2021.20210252.
[112] WANG Jingyuan, WU Junjie, WANG Ze, et al. Understanding urban dynamics via context-aware tensor factorization with neighboring regularization[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(11):2269-2283.
[113] 易嘉伟, 王楠, 千家乐, 等. 基于大数据的极端暴雨事件下城市道路交通及人群活动时空响应[J]. 地理学报, 2020, 75(3):497-508. YI Jiawei, WANG Nan, QIAN Jiale, et al. Spatio-temporal responses of urban road traffic and human activities in an extreme rainfall event using big data[J]. Acta Geographica Sinica, 2020, 75(3):497-508.
[114] 唐炉亮, 戴领, 任畅, 等. 现实与赛博空间数据相结合的城市活动事件时空建模[J]. 测绘学报, 2019, 48(5):618-629. DOI:10.11947/j.AGCS.2019.20180327. TANG Luliang, DAI Ling, REN Chang, et al. Spatio-temporal modeling of city events combining datasets in cyberspace and real space[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(5):618-629. DOI:10.11947/j.AGCS.2019.20180327.
[115] REN Mengyao, LIN Yaoyu, JIN Meihan, et al. Examining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records[J]. Transportation, 2020, 47(4):1607-1629.
[116] 唐炉亮, 阚子涵, 任畅, 等. 利用GPS轨迹的转向级交通拥堵精细分析[J]. 测绘学报, 2019, 48(1):75-85. DOI:10.11947/j.AGCS.2019.20170448. TANG Luliang, KAN Zihan, REN Chang, et al. Fine-grained analysis of traffic congestions at the turning level using GPS traces[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):75-85. DOI:10.11947/j.AGCS.2019.20170448.
[117] TSEKERIS T, GEROLIMINIS N. City size, network structure and traffic congestion[J]. Journal of Urban Economics, 2013, 76:1-14.
[118] JIANG Zhihan, LIU Yan, FAN Xiaoliang, et al. Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data[J]. Frontiers of Computer Science, 2020, 14(5):145310.
[119] XU Lin, YUE Yang, LI Qingquan. Identifying urban traffic congestion pattern from historical floating car data[C]//Proceedings of the 13th COTA International Conference of Transportation Professionals (CICTP). Shenzhen:CICTP, 2013:2084-2095.
[120] DOUXCHAMPS D, MACQ B, CHIHARA K. High accuracy traffic monitoring using road-side line-scan cameras[C]//Proceedings of 2006 IEEE Intelligent Transportation Systems Conference. Toronto:IEEE, 2006:875-878.
[121] 郑治豪, 吴文兵, 陈鑫, 等. 基于社交媒体大数据的交通感知分析系统[J]. 自动化学报, 2018, 44(4):656-666. ZHENG Zhihao, WU Wenbing, CHEN Xin, et al. A traffic sensing and analyzing system using social media data[J]. Acta Automatica Sinica, 2018, 44(4):656-666.
[122] 刘昭, 何赏璐, 刘英舜. 基于社交网络数据的交通突发事件识别方法[J]. 交通信息与安全, 2021, 39(2):53-60. LIU Zhao, HE Shanglu, LIU Yingshun. A method to identify traffic incidents based on social network data[J]. Journal of Transport Information and Safety, 2021, 39(2):53-60.
[123] D'ANDREA E, MARCELLONI F. Detection of traffic congestion and incidents from GPS trace analysis[J]. Expert Systems with Applications, 2017, 73:43-56.
[124] 付子圣, 李秋萍, 柳林, 等. 利用GPS轨迹二次聚类方法进行道路拥堵精细化识别[J]. 武汉大学学报(信息科学版), 2017, 42(9):1264-1270. FU Zisheng, LI Qiuping, LIU Lin, et al. Identification of urban network congested segments using GPS trajectories double-clustering method[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9):1264-1270.
[125] 陈林武. 基于浮动车数据的城市道路交通事件自动检测技术研究[D]. 金华:浙江师范大学, 2020. CHEN Linwu. Research on automatic detection technology of urban road traffic events based on floating vehicle data[D]. Jinhua:Zhejiang Normal University, 2020.
[126] 周思远. 基于车辆轨迹大数据的城市道路交通事件检测方法研究[D]. 北京:北方工业大学, 2019. ZHOU Siyuan. Research on urban road traffic incident detection method based on big data of vehicle trajectory[D]. Beijing:North China University of Technology, 2019.
[127] 段滢滢, 陆锋. 基于道路结构特征识别的城市交通状态空间自相关分析[J]. 地球信息科学学报, 2012, 14(6):768-774. DUAN Yingying, LU Feng. Spatial autocorrelation of urban road traffic based on road network characterization[J]. Journal of Geo-information Science, 2012, 14(6):768-774.
[128] 刘康, 段滢滢, 陆锋. 基于拓扑与形态特征的城市道路交通状态空间自相关分析[J]. 地球信息科学学报, 2014, 16(3):390-395. LIU Kang, DUAN Yingying, LU Feng. Spatial autocorrelation analysis of urban road traffic based on topological and geometric properties[J]. Journal of Geo-information Science, 2014, 16(3):390-395.
[129] WANG Zuchao, LU Min, YUAN Xiaoru, et al. Visual traffic jam analysis based on trajectory data[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12):2159-2168.
[130] YE Jiexia, ZHAO Juanjuan, YE Kejiang, et al. How to build a graph-based deep learning architecture in traffic domain:a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. DOI:10.1109/TITS.2020.3043250.
[131] 王竟成, 张勇, 胡永利, 等. 基于图卷积网络的交通预测综述[J]. 北京工业大学学报, 2021, 47(8):954-970. WANG Jingcheng, ZHANG Yong, HU Yongli, et al. Survey on graph convolutional neural network-based traffic prediction[J]. Journal of Beijing University of Technology, 2021, 47(8):954-970.
[132] 任建华, 朱尧, 孟祥福, 等. 使用动态时空神经网络的市区交通流量预测[J/OL]. 小型微型计算机系统.[2022-04-07]. https://kns.cnki.net/kcms/detail/21.1106.TP.20211213.1804.030.html. REN Jianhua, ZHU Yao, MENG Xiangfu, et al. Predicting citywide traffic flow using dynamic spatial-temporal neural networks[J/OL]. Journal of Chinese Computer Systems.[2022-04-08]. https://kns.cnki.net/kcms/detail/21.1106.TP.20211213.1804.030.html.
[133] CASTRO P S, ZHANG Daqing, LI Shijian. Urban traffic modelling and prediction using large scale taxi GPS traces[C]//Proceedings of the 10th International Conference on Pervasive Computing. Newcastle:Springer, 2012:57-72.
[134] TANG Jinjun, HU Jin, HAO Wei, et al. Markov Chains based route travel time estimation considering link spatio-temporal correlation[J]. Physica A:Statistical Mechanics and its Applications, 2020, 545:123759.
[135] 黄敏, 毛锋, 钱宇翔. 基于出租车司机经验的约束深度强化学习算法路径挖掘[J]. 计算机应用研究, 2020, 37(5):1298-1302. HUANG Min, MAO Feng, QIAN Yuxiang. Mining fastest route using taxi drivers'experience via constrained deep reinforcement learning[J]. Application Research of Computers, 2020, 37(5):1298-1302.
[136] NOLLE T, SCHWEIZER I, JANSSEN F. Data-driven detection of congestion-affected roads[R]. Technical Report TUD-KE-2014-02, 2014.
[137] JIANG Kun, YANG Diange, LIU Chaoran, et al. A flexible multi-layer map model designed for lane-level route planning in autonomous vehicles[J]. Engineering, 2019, 5(2):305-318.
[138] KAN Zihan, TANG Luliang, KWAN M P, et al. Traffic congestion analysis at the turn level using Taxis' GPS trajectory data[J]. Computers, Environment and Urban Systems, 2019, 74:229-243.
[139] FANG Mengyuan, TANG Luliang, YANG Xue, et al. FTPG:a fine-grained traffic prediction method with graph attention network using big trace data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. DOI:10.1109/TITS.2021.3049264.
[140] 诸立超, 甄伟, 刘昭然. 生命周期视角下货运通道能耗测度与节能潜力分析[J]. 交通运输系统工程与信息, 2022, 22(1):24-29, 36. ZHU Lichao, ZHEN Wei, LIU Zhaoran. Energy consumption measurement and energy-saving potential analysis of freight corridors from perspective of life cycle[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1):24-29, 36.
[141] KOUPAL J, BEARDSLEY M, BRZEZINSKI D, et al. US EPA's MOVES2010 vehicle emission model:overview and considerations for international application[EB/OL]. Ann Arbor, MI:US Environmental Protection Agency, Office of Transportation and Air Quality.[2022-03-10].https://www.epa.gov/sites/default/files/2019-08/documents/paper137-tap2010.pdf