利用导航大数据挖掘城市热点区域关联性
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摘要
导航大数据是大量与导航相关且具有泛在导航、定位、授时特征的数据集合。城市环境的特性影响居民的出行活动,而居民出行活动中产生的导航大数据则蕴含了城市环境的时空信息。热点区域空间分布以及热点区域之间的关联性特征是城市环境时空特性的重要组成部分,由客观的环境现状和主观的人为活动造成。通过挖掘导航大数据可以揭示这些特征。本文提出了利用导航大数据的城市热点区域关联性挖掘方法。首先,通过对居民出行的起点和终点坐标进行空间聚类,挖掘城市中的热点区域,并依据点的分布特点对城市热点区域进行离散化;然后,利用基于谱聚类和蚁群算法的方法分析居民出行特征,揭示城市中热点区域之间存在的关联性。本文提出的方法能够充分利用导航大数据对城市动态的感知能力。以上海市2007年2月20日的出租车轨迹数据为例进行分析,结果表明:利用导航大数据分析城市热点区域之间的关联性,可以得到具有紧密关联性的热点区域的空间分布特征;上海市居民出行活动频繁的热点区域被划分为15个内部紧密关联的子图,形成该分布特征的内在机制以及居民流通规律与上海市的土地资源利用及道路交通建设现状密切相关。分析方法和结果可为合理的城市功能区域规划,智慧城市建设等提供决策支持和参考信息。
Navigation big data is a set of massive data produced by navigation and positioning technology with both the characteristics of PNT(position, navigation, and time) and 5 V(volume, velocity, variety, value, and veracity). Urban environment affects the travel behavior of residents. Consequently, navigation big data generated in daily travel activities contain abundant spatiotemporal information about both residents' behavior and environment. The spatial distribution of hotspots and their latent association are key aspects of urban dynamics. This kind of characteristics is influenced by objective urban environment condition and subjective residents' activities. The knowledge about urban hotspot region association, which can be detected via mining navigation big data, is useful for urban environment management and intelligent transportation, etc. However,most studies focus on only determining hotspots. To better understand urban dynamics, a method based on spectral clustering and ant colony algorithm was proposed in the present study to mine the knowledge of association among urban hotspots from navigation big data. Implementation of this method included three main steps. Firstly, data preprocessing including data cleaning and OD(Origin and Destination) points extraction was performed on raw data. Then, the hotspot regions were extracted by two-step density-based clustering of the OD points. These hotspot regions were discretized by k-means clustering and Voronoi polygon. The proposed discretization strategy efficiently retained the intrinsic spatial distribution characteristics of the OD points.Lastly, we defined the degree of association among hotspot regions based on travel frequency. This measurement intuitively described the relationship among different regions embedded in travel activities. The association among hotspot regions was investigated using spectral clustering and ant colony algorithm. The two algorithms converted the association mining into the issues of graph partition and optimal path solution. The proposed method takes advantage of using navigation big data as a proxy of urban dynamics. We applied it to real-world taxi trajectory data in Shanghai. Results show the spatial distribution characteristics of hotspot regions with tight association and the pattern of residents' travel behavior. Discretized hotspot regions frequently visited by residents were clustered into 15 groups. Regions of each group formed a strongly associated structure. With landuse and road network information, the intrinsic mechanism of this characteristics was also analyzed. Findings of this paper could provide decision-making support and useful knowledge for layout design of urban function region and the construction of smart cities.
Navigation big data is a set of massive data produced by navigation and positioning technology with both the characteristics of PNT(position, navigation, and time) and 5 V(volume, velocity, variety, value, and veracity). Urban environment affects the travel behavior of residents. Consequently, navigation big data generated in daily travel activities contain abundant spatiotemporal information about both residents' behavior and environment. The spatial distribution of hotspots and their latent association are key aspects of urban dynamics. This kind of characteristics is influenced by objective urban environment condition and subjective residents' activities. The knowledge about urban hotspot region association, which can be detected via mining navigation big data, is useful for urban environment management and intelligent transportation, etc. However,most studies focus on only determining hotspots. To better understand urban dynamics, a method based on spectral clustering and ant colony algorithm was proposed in the present study to mine the knowledge of association among urban hotspots from navigation big data. Implementation of this method included three main steps. Firstly, data preprocessing including data cleaning and OD(Origin and Destination) points extraction was performed on raw data. Then, the hotspot regions were extracted by two-step density-based clustering of the OD points. These hotspot regions were discretized by k-means clustering and Voronoi polygon. The proposed discretization strategy efficiently retained the intrinsic spatial distribution characteristics of the OD points.Lastly, we defined the degree of association among hotspot regions based on travel frequency. This measurement intuitively described the relationship among different regions embedded in travel activities. The association among hotspot regions was investigated using spectral clustering and ant colony algorithm. The two algorithms converted the association mining into the issues of graph partition and optimal path solution. The proposed method takes advantage of using navigation big data as a proxy of urban dynamics. We applied it to real-world taxi trajectory data in Shanghai. Results show the spatial distribution characteristics of hotspot regions with tight association and the pattern of residents' travel behavior. Discretized hotspot regions frequently visited by residents were clustered into 15 groups. Regions of each group formed a strongly associated structure. With landuse and road network information, the intrinsic mechanism of this characteristics was also analyzed. Findings of this paper could provide decision-making support and useful knowledge for layout design of urban function region and the construction of smart cities.
引文
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[5] Dabiri S, Heaslip K. Inferring transportation modes from GPS trajectories using a convolutional neural network[J].Transportation Research Part C:Emerging Technologies,2018,86:360-371.
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[9]孙涛,吴琳,王飞,等.大规模航运数据下“一带一路”国家和地区贸易网络分析[J].地球信息科学学报,2018,20(5):593-601.[Sun T, Wu L, Wang F, et al. Analysis on the trade networks of the Belt and Road countries and regions under large scale shipping data[J]. Journal of Geoinformation Science, 2018,20(5):593-601.]
[10] Chang H W, Tai Y C, Hsu Y J. Context-aware taxi demand hotspots prediction[J]. International Journal of Business Intelligence&Data Mining, 2009,5(1):3-18.
[11] Pan G, Qi G D, Wu Z H, et al. Land-use classification using taxi GPS traces[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(1):113-123.
[12] Liu Y, Wang F H, Xiao Y, et al. Urban land uses and traffic‘source-sink areas':Evidence from GPS-enabled taxi data in Shanghai[J]. Landscape&Urban Planning, 2012,106(1):73-87.
[13] Zhuang C Y, Yuan N J, Song R H, et al. Understanding people lifestyles:Construction of urban movement knowledge graph from GPS trajectory[C]. Melbourne:Proceedings of 26thInternational Joint Conference on Artificial Intelligence, 2017:3616-3623.
[14] Newman M E, Girvan M. Finding and evaluating community structure in networks[J]. Physical Review E Statistical Nonlinear&Soft Matter Physics, 2004,69(2):026113.
[15] Liu X, Gong L, Gong Y X, et al. Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 2015,43:78-90.
[16] Xiao L Z, Xu W T, Liu J X. Detecting urban dynamics with taxi trip data for evaluation and optimizing of spatial planning:The example of Xiamen city, China[J]. International Review for Spatial Planning and Sustainable Development, 2016,4(3):14-26.
[17]李勇.基于出租车GPS数据的城市交通拥堵识别和关联性分析[D].哈尔滨:哈尔滨工业大学,2016.[Li Y. Congestion identification and correlation analysis on urban traffic based on taxi GPS data[D]. Haerbin:Harbin Institute of Technology, 2016.]
[18]陈泽东,谯博文,张晶.基于居民出行特征的北京城市功能区识别与空间交互研究[J].地球信息科学学报,2018,20(3):291-301.[Chen Z D, Qiao B W, Zhang J. Identification and spatial interaction of urban functional regions in Beijing based on the characteristics of residents'traveling[J].Journal of Geo-information Science, 2018,20(3):291-301.]
[19] https://www.cse.ust.hk/scrg/,2018.8.15.
[20] Liu Y, Kang C G, Gao S, et al. Understanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 2012,14(4):463-483.
[21] Khetarpaul S, Chauhan R, Gupta S K, et al. Mining GPS data to determine interesting locations[C]. Hyderabad:Proceedings of International Workshop on Information Integration on the Web:in Conjunction with WWW. ACM, 2011:1-6.
[22] Liu P, Zhou D, Wu N J. VDBSCAN:Varied density based spatial clustering of applications with noise[C]. Chengdu:International Conference on Service Systems and Service Management, IEEE, 2007:1-4.
[23]信睿,艾廷华,杨伟,等.顾及出租车OD点分布密度的空间Voronoi剖分算法及OD流可视化分析[J].地球信息科学学报,2015,17(10):1187-1195.[Xin R, Ai T H, Yang W,et al. A new network Voronoi diagram considering the OD point density of taxi and visual analysis of OD flow[J].Journal of Geo-information Science, 2015,17(10):1187-1195.]
[24] Han J W, Kamber M. Data mining:Concepts and techniques[J]. Data Mining Concepts Models Methods&Algorithms Second Edition, 2011,5(4):1-18.
[25]蔡晓妍,戴冠中,杨黎斌.谱聚类算法综述[J].计算机科学,2008,35(7):14-18.[Cai X Y, Dai G Z, Yang L B. Survey on spectral clustering algorithms[J]. Computer Science,2008,35(7):14-18.]
[26] Luxburg U V, Belkin M, Bousquet O. Consistency of spectral clustering[J]. Annals of Statistics, 2008,36(2):555-586.
[27] Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(8):888-905.
[28]段海滨,王道波,朱家强,等.蚁群算法理论及应用研究的进展[J].控制与决策,2004,19(12):1321-1326.[Duan H B, Wang D B, Zhu J Q, et al. Development on ant colony algorithm theory and its application[J]. Control&Decision, 2004,19(12):1321-1320.]
[29]杨平,郑金华.遗传选择算子的比较与研究[J].计算机工程与应用,2007,43(15):59-62.[Yang P, Zheng J H. Comparison and research over genetic election operators[J].Computer Engineering&Applications, 2007,43(15):59-62.]
[2]陈明剑,李广云.北斗导航大数据建设与思考[J].卫星应用,2017(2):56-59.[Chen M, Li G. Construction and thinking about Beidou navigation big data[J]. Satellite Application, 2017(2):56-59.]
[3]陈卓然,黄翀,刘高焕,等.基于出租车GPS数据的居民就医时空特征分析[J].地球信息科学学报,2018,20(8):1111-1122.[Chen Z R, Huang C, Liu G H, et al. Analysis of spatial-temporal characteristics of resident travel for hospitals based on taxi GPS data[J]. Journal of Geo-information Science, 2018,20(8):1111-1122.]
[4]王永程,褚衍杰.基于谱聚类的用户关联关系挖掘[J].电讯技术,2016,56(1):32-37.[Wang Y C, Chu Y J, et al. User association mining based on spectral clustering[J].Telecommunication Engineering, 2016,56(1):32-37.]
[5] Dabiri S, Heaslip K. Inferring transportation modes from GPS trajectories using a convolutional neural network[J].Transportation Research Part C:Emerging Technologies,2018,86:360-371.
[6]刘菊,许珺,蔡玲,等.基于出租车用户出行的功能区识别[J].地球信息科学学报,2018,20(11):1550-1561.[Liu J,Xu J, Cai L, et al. Identifying functional regions based on the spatio-temporal pattern of taxi trajectories[J]. Journal of Geo-information Science, 2018,20(11):1550-1561.]
[7]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.[Yang W, Ai T H. Refueling stop activity detection and gas station extraction using crowdsourcing vehicle trajectory data[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(7):918-927.]
[8] Liu S Y, Liu Y H, Ni L, et al. Detecting crowdedness spot in city transportation[J]. IEEE Transactions on Vehicular Technology, 2013,62(4):1527-1539.
[9]孙涛,吴琳,王飞,等.大规模航运数据下“一带一路”国家和地区贸易网络分析[J].地球信息科学学报,2018,20(5):593-601.[Sun T, Wu L, Wang F, et al. Analysis on the trade networks of the Belt and Road countries and regions under large scale shipping data[J]. Journal of Geoinformation Science, 2018,20(5):593-601.]
[10] Chang H W, Tai Y C, Hsu Y J. Context-aware taxi demand hotspots prediction[J]. International Journal of Business Intelligence&Data Mining, 2009,5(1):3-18.
[11] Pan G, Qi G D, Wu Z H, et al. Land-use classification using taxi GPS traces[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(1):113-123.
[12] Liu Y, Wang F H, Xiao Y, et al. Urban land uses and traffic‘source-sink areas':Evidence from GPS-enabled taxi data in Shanghai[J]. Landscape&Urban Planning, 2012,106(1):73-87.
[13] Zhuang C Y, Yuan N J, Song R H, et al. Understanding people lifestyles:Construction of urban movement knowledge graph from GPS trajectory[C]. Melbourne:Proceedings of 26thInternational Joint Conference on Artificial Intelligence, 2017:3616-3623.
[14] Newman M E, Girvan M. Finding and evaluating community structure in networks[J]. Physical Review E Statistical Nonlinear&Soft Matter Physics, 2004,69(2):026113.
[15] Liu X, Gong L, Gong Y X, et al. Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 2015,43:78-90.
[16] Xiao L Z, Xu W T, Liu J X. Detecting urban dynamics with taxi trip data for evaluation and optimizing of spatial planning:The example of Xiamen city, China[J]. International Review for Spatial Planning and Sustainable Development, 2016,4(3):14-26.
[17]李勇.基于出租车GPS数据的城市交通拥堵识别和关联性分析[D].哈尔滨:哈尔滨工业大学,2016.[Li Y. Congestion identification and correlation analysis on urban traffic based on taxi GPS data[D]. Haerbin:Harbin Institute of Technology, 2016.]
[18]陈泽东,谯博文,张晶.基于居民出行特征的北京城市功能区识别与空间交互研究[J].地球信息科学学报,2018,20(3):291-301.[Chen Z D, Qiao B W, Zhang J. Identification and spatial interaction of urban functional regions in Beijing based on the characteristics of residents'traveling[J].Journal of Geo-information Science, 2018,20(3):291-301.]
[19] https://www.cse.ust.hk/scrg/,2018.8.15.
[20] Liu Y, Kang C G, Gao S, et al. Understanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 2012,14(4):463-483.
[21] Khetarpaul S, Chauhan R, Gupta S K, et al. Mining GPS data to determine interesting locations[C]. Hyderabad:Proceedings of International Workshop on Information Integration on the Web:in Conjunction with WWW. ACM, 2011:1-6.
[22] Liu P, Zhou D, Wu N J. VDBSCAN:Varied density based spatial clustering of applications with noise[C]. Chengdu:International Conference on Service Systems and Service Management, IEEE, 2007:1-4.
[23]信睿,艾廷华,杨伟,等.顾及出租车OD点分布密度的空间Voronoi剖分算法及OD流可视化分析[J].地球信息科学学报,2015,17(10):1187-1195.[Xin R, Ai T H, Yang W,et al. A new network Voronoi diagram considering the OD point density of taxi and visual analysis of OD flow[J].Journal of Geo-information Science, 2015,17(10):1187-1195.]
[24] Han J W, Kamber M. Data mining:Concepts and techniques[J]. Data Mining Concepts Models Methods&Algorithms Second Edition, 2011,5(4):1-18.
[25]蔡晓妍,戴冠中,杨黎斌.谱聚类算法综述[J].计算机科学,2008,35(7):14-18.[Cai X Y, Dai G Z, Yang L B. Survey on spectral clustering algorithms[J]. Computer Science,2008,35(7):14-18.]
[26] Luxburg U V, Belkin M, Bousquet O. Consistency of spectral clustering[J]. Annals of Statistics, 2008,36(2):555-586.
[27] Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(8):888-905.
[28]段海滨,王道波,朱家强,等.蚁群算法理论及应用研究的进展[J].控制与决策,2004,19(12):1321-1326.[Duan H B, Wang D B, Zhu J Q, et al. Development on ant colony algorithm theory and its application[J]. Control&Decision, 2004,19(12):1321-1320.]
[29]杨平,郑金华.遗传选择算子的比较与研究[J].计算机工程与应用,2007,43(15):59-62.[Yang P, Zheng J H. Comparison and research over genetic election operators[J].Computer Engineering&Applications, 2007,43(15):59-62.]
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