基于热点探测模型的城市居民出行特征分析
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摘要
城市居民每天的交通出行活动伴随着一定的规律性和时空特征。现阶段对居民出行特征的研究方法中以聚类算法为主。然而由于聚类算法的参数复杂性,使得低值热点区域往往被忽略。此外研究中对出行OD的无差别考虑,使得很多居民出行特征不能被充分挖掘。针对这一问题,提出了基于密度场的热点探测模型,分别从出租车上、下车密度场中提取热点并对热点进行分级。并以西安出租车GPS数据为例展开实证分析。研究结果表明:①基于密度场的热点探测模型可有效解决传统聚类算法中低值热点无法获取问题;②研究区内城市居民一天中各时段上车频次和下车频次变化趋势基本吻合;③在空间分布上,上下车热点区域集中分布在交通服务区和城市主干道周围;④结合城市功能定位,大型交通服务区及城市道路关键节点上下车热点等级较高,并且工作日和非工作日无明显差异;⑤商业服务区表现为午、晚高峰的上下车热点等级高于早高峰,非工作日早高峰的上车热点等级明显高于工作日。
Daily travel activities of urban residents present certain regularities and spatial-temporal characteristics. Current Methods for studying travel characteristics of urban residents are mainly based on clustering algorithms. However, due to complexity of parameters of clustering algorithms, hot spot areas with low value are commonly ignored. Moreover, indiscriminate consideration of travel OD in previous studies makes potential travel characteristics of urban residents cannot be fully studied. To solve these problems, a hotspot detection model based on density field is proposed, and hotspots in density field of boarding and getting off taxies are identified and graded. A case study is conducted based on GPS data of taxies in Xi′an. The results show that: ①the model can solve the problems that low-value hot spots cannot be identified in traditional clustering algorithms; ②changing trends of frequency of boarding and getting off taxies are basically consistent; ③in spatial distribution, the hotspots of boarding and getting off taxies mainly distribute in service areas and nearby urban arterial roads; ④according to urban functional areas, service areas with large scale and key nodes of urban road network have higher grade of hotspot, and there is no significant difference between weekdays and weekends; ⑤business areas have higher grade of hotspot during noon and evening peak hours than which in morning peak hours. Its grade at weekends is significantly higher than that which on weekdays during morning peak hours.
Daily travel activities of urban residents present certain regularities and spatial-temporal characteristics. Current Methods for studying travel characteristics of urban residents are mainly based on clustering algorithms. However, due to complexity of parameters of clustering algorithms, hot spot areas with low value are commonly ignored. Moreover, indiscriminate consideration of travel OD in previous studies makes potential travel characteristics of urban residents cannot be fully studied. To solve these problems, a hotspot detection model based on density field is proposed, and hotspots in density field of boarding and getting off taxies are identified and graded. A case study is conducted based on GPS data of taxies in Xi′an. The results show that: ①the model can solve the problems that low-value hot spots cannot be identified in traditional clustering algorithms; ②changing trends of frequency of boarding and getting off taxies are basically consistent; ③in spatial distribution, the hotspots of boarding and getting off taxies mainly distribute in service areas and nearby urban arterial roads; ④according to urban functional areas, service areas with large scale and key nodes of urban road network have higher grade of hotspot, and there is no significant difference between weekdays and weekends; ⑤business areas have higher grade of hotspot during noon and evening peak hours than which in morning peak hours. Its grade at weekends is significantly higher than that which on weekdays during morning peak hours.
引文
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[12] 李杰,贾瑞玉,张璐璐.一个改进的基于DBSCAN的空间聚类算法研究[J].计算机技术与发展,2007,17(1):114-116. LI Jie, JIA Ruiyu, ZHANG Lulu. Research on improving spatial clustering algorithm based on dbscan[J].Computer Technology and Development, 2007,17(1):114-116. (in Chinese)
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[14] 冯琦森.基于出租车轨迹的居民出行热点路径和区域挖掘[D].重庆:重庆大学,2017. FENG Qisen. Research on residents′ trip hot routes and attractive areas based on taxi trajectory data[D]. Chongqing: Chongqing University, 2017. (in Chinese)
[15] 江慧娟,余洋.出租车载客热点精细提取的改进DBSCAN算法[J].地理空间信息,2017,15(10):16-20. JIANG Huijuan, YU Yang. Extracting fine pickup hot zone by an improved DBSCAN algorithm[J]. Geospatial Information, 2017,15(10):16-20. (in Chinese)
[16] 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 Rui, AI Tinghua, YANG Wei, et al. A new network voronoi diagram considering the OD point density of taxi and visual analysis of OD flow[J]. Jouranl of Geo-information Science, 2015,17(10):1187-1195. (in Chinese)
[17] 何林,柳林涛,许超钤,等.常见平面坐标系之间相互转换的方法研究:以1954北京坐标系、1980西安坐标系、2000国家大地坐标系之间的平面坐标相互转换为例[J].测绘通报,2014(9):6-11. HE Lin, LIU Lintao, XU Chaoling, et al. Study on common plane coordinate system conversion:plane coordinate conversion between BJ54, XA80 and CGCS2000[J]. Bulletin of Surveying and Mapping, 2014(9):6-11. (in Chinese)
[18] 余莉,甘淑,袁希平,等.基于空间邻近的点目标聚类方法[J].计算机应用,2016,36(5):1267-1272. YU Li, GAN Shu, YUAN Xiping, et al. Clustering for point objects based on spatial proximity[J]. Journal of Computer Applications, 2016,36(5):1267-1272. (in Chinese)
[19] Khosrowdehnan. Density estimation for statistics and data analysis[J]. Technometrics, 1986,29(4):495-495.
[20] 吴嘉逸,席唱白,苑振宇,等.核密度法的南京苏果超市分布热点探测[J].测绘科学,2017,42(11):72-77. WU Jiayi, XI Changbai, YUAN Zhenyu, et al. Hotspot detection of Suguo stores in Nanjing city supported by kernel density estimation[J]. Science of Surveying and Mapping, 2017,42(11):72-77. (in Chinese)
[2] 柴彦威,申悦,马修军,等.北京居民活动与出行行为时空数据采集与管理[J].地理研究,2013,32(3):441-451. CHAI Yanwei, SHEN Yue, MA Xiujun, et al. The collection and management of Space-time data of individual behavior based on location-based technologies: A case study of activity-travel survey in Beijing[J]. Geographical Research, 2013,32(3):441-451. (in Chinese)
[3] 杨超,朱荣荣,涂然.基于智能手机调查数据的居民出行活动特征分析[J].交通信息与安全,2015,33(6):25-32. YANG Chao, ZHU Rongrong, XU Ran. Analysis of the travel characteristics of residents in shanghai using the itinerary data collected from smartphones[J]. Journal of Transport Information and Safety, 2015,33(6):25-32. (in Chinese)
[4] 赵昕,关宏志,刘诗序.基于出行链的有车家庭假日出行方式组合研究[J].武汉理工大学学报(交通科学与工程版),2011,35(6):1139-1142. ZHAO Xin, GUAN Hongzhi, LIU Shixu. Study on combined mode choice behavior in holiday based on trip-chain[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2011,35(6):1139-1142. (in Chinese)
[5] ZHENG Y, ZHANG L, XIE X, et al. Mining interesting locations and travel sequences from GPS trajectories[C]. International Conference on World Wide Web,Madrid,Spain: ACM, 2009.
[6] YUE Y, ZHUANG Y, LI Q, et al. Mining time-dependent attractive areas and movement patterns from taxi trajectory data[C]. International Conference on Geoinformatics,Fairfax,VA. IEEE, 2009.
[7] 付鑫,孙茂棚,孙皓.基于GPS数据的出租车通勤识别及时空特征分析[J].中国公路学报,2017,30(7):134-143. FU Xin, SUN Maopeng, SUN Hao. Taxi commute recognition and temporal-spatial characteristics analysis based on GPS data[J]. China Journal of Highway and Transport, 2017,30(7):134-143. (in Chinese)
[8] 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. TANG Luliang, ZHENG Wenbin, WANG Zhiqiang, et al. Space time analysis on the pick-up and drop-off taxi passengers based on GPS big data[J]. Geo-Information Science, 2015,17(10):1179-1186. (in Chinese)
[9] TANG J, LIU F, WANG Y, et al. Uncovering urban human mobility from large scale taxi GPS data[J]. Physica A: Statistical Mechanics and its Applications, 2015,438:140-153.
[10] FANHAS R S, SAPTAWATI G A P. Discovering frequent origin-destination flow from taxi GPS data[C]. International Conference on Data & Software Engineering, Denpasar, Indonesia:IEEE, 2017.
[11] PHIBOONBANAKIT T, HORANONT T. How does taxi driver behavior impact their profit? discerning the real driving from large scale GPS traces[C]. Acm International Joint Conference on Pervasive & Ubiquitous Computing: Adjunct, Heidelberg, Germany: ACM, 2016.
[12] 李杰,贾瑞玉,张璐璐.一个改进的基于DBSCAN的空间聚类算法研究[J].计算机技术与发展,2007,17(1):114-116. LI Jie, JIA Ruiyu, ZHANG Lulu. Research on improving spatial clustering algorithm based on dbscan[J].Computer Technology and Development, 2007,17(1):114-116. (in Chinese)
[13] 童晓君.基于出租车GPS数据的居民出行行为分析[D].长沙:中南大学,2012. TONG Xiaojun. Analysis of residents′ behavior based on the taxi GPS[D]. Changsha: Central South University, 2012. (in Chinese)
[14] 冯琦森.基于出租车轨迹的居民出行热点路径和区域挖掘[D].重庆:重庆大学,2017. FENG Qisen. Research on residents′ trip hot routes and attractive areas based on taxi trajectory data[D]. Chongqing: Chongqing University, 2017. (in Chinese)
[15] 江慧娟,余洋.出租车载客热点精细提取的改进DBSCAN算法[J].地理空间信息,2017,15(10):16-20. JIANG Huijuan, YU Yang. Extracting fine pickup hot zone by an improved DBSCAN algorithm[J]. Geospatial Information, 2017,15(10):16-20. (in Chinese)
[16] 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 Rui, AI Tinghua, YANG Wei, et al. A new network voronoi diagram considering the OD point density of taxi and visual analysis of OD flow[J]. Jouranl of Geo-information Science, 2015,17(10):1187-1195. (in Chinese)
[17] 何林,柳林涛,许超钤,等.常见平面坐标系之间相互转换的方法研究:以1954北京坐标系、1980西安坐标系、2000国家大地坐标系之间的平面坐标相互转换为例[J].测绘通报,2014(9):6-11. HE Lin, LIU Lintao, XU Chaoling, et al. Study on common plane coordinate system conversion:plane coordinate conversion between BJ54, XA80 and CGCS2000[J]. Bulletin of Surveying and Mapping, 2014(9):6-11. (in Chinese)
[18] 余莉,甘淑,袁希平,等.基于空间邻近的点目标聚类方法[J].计算机应用,2016,36(5):1267-1272. YU Li, GAN Shu, YUAN Xiping, et al. Clustering for point objects based on spatial proximity[J]. Journal of Computer Applications, 2016,36(5):1267-1272. (in Chinese)
[19] Khosrowdehnan. Density estimation for statistics and data analysis[J]. Technometrics, 1986,29(4):495-495.
[20] 吴嘉逸,席唱白,苑振宇,等.核密度法的南京苏果超市分布热点探测[J].测绘科学,2017,42(11):72-77. WU Jiayi, XI Changbai, YUAN Zhenyu, et al. Hotspot detection of Suguo stores in Nanjing city supported by kernel density estimation[J]. Science of Surveying and Mapping, 2017,42(11):72-77. (in Chinese)