Structural complexity and spatial differentiation characteristics of taxi trip trajectory network
Article Text (Baidu Translation)
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摘要: 基于出租汽车运行GPS轨迹数据, 构建了一类城市出行复杂网络; 使用有向加权复杂网络测度分析方法, 研究了出租汽车出行轨迹网络结构复杂性与空间分异特征; 以西安市数据为例, 进行了网络指标测算。分析结果表明: 出租汽车出行轨迹网络的平均最短路径长度为2.070 (边数), 聚类系数为0.653, 网络密度为0.554, 说明了该网络是一类典型复杂网络, 具有典型的小世界和集团化特征, 且实际平均出行距离符合对数正态分布; 网络的节点强度均值为411, 最大K-核值为59, 网络中强度小于600的节点占77.97%, 强度小于300的节点占50.24%, 呈现典型的大少小多的空间分布特点; 该网络具有显著的空间分异特征, 重要小区的出行辐射范围具有全局性特征, 总体出行强度空间布局与城市公共交通干线走向一致, 呈十字型分布; 在整个网络范围内, 强中心性交通小区呈现集聚性分布, 重要交通枢纽(车站) 与商圈等区域节点强度大于2 200;出租汽车上下客区域呈现空间非均衡特征, 即在城市重要功能聚集区的上客水平高于下客水平。研究结果反映了出租汽车出行轨迹网络的拓扑结构与空间分异特征间的相互关系, 揭示了城市居民活动的空间特征、活动规律及其与城市功能空间布局之间的相互影响作用。Abstract: Based on the GPS trajectory data of taxis, a kind of urban trip complex network was constructed.The structural complexity and differentiation characteristics of taxi trip trajectory network were researched by using the directed-weighted complex network measuring method.Based on the taxi trip data of Xi'an, the network indexes were calculated.Analysis result shows that the average shortest path length of taxi trip trajectory network is 2.07 (edge number), the clustering coefficient is 0.653, and the network density is 0.554.So the network is a kind of typical complex network, with typical "small-world"and "collective"characteristic, and the actual average trip distance obeys log-normal distribution.The average value of node strengthsfor the network is 411, the largest K-nuclear value is 59, and the proportions of nodes with the strengths being less than 600 and 300 are 77.97% and 50.24%, respectively, which shows the typical spatial distribution that is described as"less nodes with greater strengths but more nodes with less strengths".The network has significant spatial differentiation characteristic, the traveling radiation scopes of important traffic analysis zones (TAZs) have overall characteristic, the whole spatial layout of trip intensities is consistent with public transport arterial lines, and presents a cross type distribution.The high-level centricity TAZs in whole network present agglomeration distribution, and the node strengths of important transport hubs (stations) and CBD areas are more than 2 200.The distributions of pick-up and drop-off areas of taxis are nonequilibrium, and the pick-up level is higher than the drop-off level in the important functional zones of city.Obviously, this research result indicates the interaction relationship between the topology structure and spatial differentiation of taxi trip trajectory network, and reveals urban resident activities' spatial characteristics, movement rules and the mutual influence of urban functions' spatial layout and resident activities.
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Key words:
- traffic engineering /
- taxi /
- complex network /
- GPS trajectory data /
- topology structure /
- spatial differentiation
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图 4 各出行小区平均最短路径频次分布
Figure 4. Each travel zone's average shortest path frequency distribution
图 6 西安市出租汽车复杂网络节点强度分布
Figure 6. Node strength distribution of Xi'an's taxi complex network
图 7 节点强度排名前11位与后10位的交通小区
Figure 7. Traffic zones having top 11and last 10node strengths
图 9 西安市出租汽车复杂网络节点强度等级分布
Figure 9. Node strength grade distribution of Xi'an's taxi complex network
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[1] TANG Jin-jun, JIANG Han, LI Zhi-bin, et al. A two-layer model for taxi customer searching behaviors using GPS trajectory data[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17 (11): 3318-3324. doi: 10.1109/TITS.2016.2544140 [2] 焦龙, 刘岳峰, 司若辰. 距离约束在出租车客流网络结构中的作用研究[J]. 北京大学学报: 自然科学版, 2014, 50 (5): 880-886. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201405012.htmJIAO Long, LIU Yue-feng, SI Ruo-chen. Role of distance constraint in the structure of taxi passenger flow network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50 (5): 880-886. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201405012.htm [3] 周静一, 李晔. 基于浮动车技术的杭州市出租汽车运行特征分析[J]. 科技信息, 2011 (3): 120-122. https://www.cnki.com.cn/Article/CJFDTOTAL-KJXX201103084.htmZHOU Jing-yi, LI Ye. Operation characteristics analysis of Hangzhou taxis based on floating car technology[J]. Science and Technology Information, 2011 (3): 120-122. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KJXX201103084.htm [4] 辛飞飞, 陈小鸿, 林航飞. 浮动车数据路网时空分布特征研究[J]. 中国公路学报, 2008, 21 (4): 105-110. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200804017.htmXIN Fei-fei, CHEN Xiao-hong, LIN Hang-fei. Research on time space distribution characteristics of floating car data in road network[J]. China Journal of Highway and Transport, 2008, 21 (4): 105-110. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200804017.htm [5] 袁长伟, 米雪玉, 吴群琪, 等. 交通拥堵环境下的城市出租车候时费优化模型[J]. 交通运输工程学报, 2014, 14 (2): 75-81. doi: 10.3969/j.issn.1671-1637.2014.02.013YUAN Chang-wei, MI Xue-yu, WU Qun-qi, et al. Optimal model of taxi waiting time fee under traffic congestion condition[J]. Journal of Traffic and Transportation Engineering, 2014, 14 (2): 75-81. (in Chinese). doi: 10.3969/j.issn.1671-1637.2014.02.013 [6] COMERT G. Effect of stop line detection in queue length estimation at traffic signals from probe vehicles data[J]. European Journal of Operational Research, 2013, 226 (1): 67-76. doi: 10.1016/j.ejor.2012.10.035 [7] SUN Jian, ZHANG Chun, ZHANG Li-hui, et al. Urban travel behavior analyses and route prediction based on floating car data[J]. Transportation Letters: the International Journal of Transportation Research, 2014, 6 (3): 118-125. [8] 俞春辉, 杨晓光, 马万经. 考虑随机需求的出租车上客区泊位设置模式和规模优化方法[J]. 中国公路学报, 2015, 28 (3): 102-109. doi: 10.3969/j.issn.1001-7372.2015.03.014YU Chun-hui, YANG Xiao-guang, MA Wan-jing. Optimization method for management mode and scale of taxi pick-up zone with stochastic demand[J]. China Journal of Highway and Transport, 2015, 28 (3): 102-109. (in Chinese). doi: 10.3969/j.issn.1001-7372.2015.03.014 [9] 杨英俊, 赵祥模. 基于出租车运行信息的城市出租车运量投放计划模型[J]. 中国公路学报, 2012, 25 (5): 120-125. doi: 10.3969/j.issn.1001-7372.2012.05.019YANG Ying-jun, ZHAO Xiang-mo. Schedule model of urban taxi quantity based on taxi running information[J]. China Journal of Highway and Transport, 2012, 25 (5): 120-125. (in Chinese). doi: 10.3969/j.issn.1001-7372.2012.05.019 [10] TANG Jin-jun, LIU Fang, WANG Yin-hai, et al. Uncovering urban human mobility from large scale taxi GPS data[J]. Physica A: Statistical Mechanics and its Applications, 2015, 438: 140-153. doi: 10.1016/j.physa.2015.06.032 [11] ZHOU Zuo-jian, DOU Wan-chun, JIA Guo-chao, et al. A method for real-time trajectory monitoring to improve taxi service using GPS big data[J]. Information and Management, 2016, 53 (8): 964-977. doi: 10.1016/j.im.2016.04.004 [12] ZHAO Shuang-ming, ZHAO Peng-xiang, CUI Yun-fan. A network centrality measure framework for analyzing urban traffic flow: a case study of Wuhan, China[J]. Physica A: Statistical Mechanics and its Applications, 2017, 478: 143-157. doi: 10.1016/j.physa.2017.02.069 [13] 刘岳峰, 司若辰, 康葳. 城市出租车客流网络结构复杂性特征研究[J]. 北京大学学报: 自然科学版, 2014, 50 (5): 873-879. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201405011.htmLIU Yue-feng, SI Ruo-chen, KANG Wei. Complex structural properties of urban taxi passenger flow network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50 (5): 873-879. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201405011.htm [14] CUI Jian-xun, LIU Feng, JANSSENS D, et al. Detecting urban road network accessibility problems using taxi GPS data[J]. Journal of Transport Geography, 2016, 51: 147-157. doi: 10.1016/j.jtrangeo.2015.12.007 [15] 周江评, 陈晓键, 黄伟, 等. 中国中西部大城市的职住平衡与通勤效率——以西安为例[J]. 地理学报, 2013, 68 (10): 1316-1330. doi: 10.11821/dlxb201310002ZHOU Jiang-ping, CHEN Xiao-jian, HUANG Wei, et al. Jobs-housing balance and commute efficiency in cities of central and western China: a case study of Xi'an[J]. Acta Geographica Sinica, 2013, 68 (10): 1316-1330. (in Chinese). doi: 10.11821/dlxb201310002 [16] SEATON K A, HACKETT L M. Station, trains and smallworld networks[J]. Physical A: Statistical Mechanics and its Applications, 2004, 339 (3/4): 635-644. [17] 孙健, 张颖, 张纯. 基于驾驶人路径选择偏好的OD行程时间预测方法[J]. 交通运输工程学报, 2016, 16 (2): 143-149. http://transport.chd.edu.cn/article/id/201602017SUN Jian, ZHANG Ying, ZHANG Chun. Prediction method of OD travel time based on driver's route choice preference[J]. Journal of Traffic and Transportation Engineering, 2016, 16 (2): 143-149. (in Chinese). http://transport.chd.edu.cn/article/id/201602017 [18] 柯文前. 高速公路交通流网络的时空特征与城市空间关联研究——以江苏省为例[D]. 南京: 南京师范大学, 2015.KE Wen-qian. Spatio-temporal characteristics of expressway traffic flow network and urban spatial interaction: a case study in Jiangsu Province[D]. Nanjing: Nanjing Normal University, 2015. (in Chinese). [19] 赵月, 杜文, 陈爽. 复杂网络理论在城市交通网络分析中的应用[J]. 城市交通, 2009, 7 (1): 57-65. https://www.cnki.com.cn/Article/CJFDTOTAL-CSJT200901012.htmZHAO Yue, DU Wen, CHEN Shuang. Application of complex network theory to urban transportation network analysis[J]. Urban Transport of China, 2009, 7 (1): 57-65. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CSJT200901012.htm [20] KHLER E, MHRING R H, SKUTELLA M. Traffic networks and flows over time[C]∥Springer. Lecture Notes in Computer Science. Berlin: Springer, 2009: 166-196. [21] SUN Jian, GUAN Shi-tuo. Measuring vulnerability of urban metro network from line operation perspective[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 348-359. doi: 10.1016/j.tra.2016.09.024 [22] 罗飞, 魏开平, 万润泽. 复杂网络中最短路径算法的研究及应用[J]. 电子测量技术, 2007, 30 (4): 169-171, 197. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL200704049.htmLUO Fei, WEI Kai-ping, WAN Run-ze. Research on algorithm for detecting shortest path in complex network and its application[J]. Electronic Measurement Technology, 2007, 30 (4): 169-171, 197. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL200704049.htm [23] 王林, 张婧婧. 复杂网络的中心化[J]. 复杂系统与复杂性科学, 2006, 3 (1): 13-20. https://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200601001.htmWANG Lin, ZHANG Jing-jing. Centralization of complex networks[J]. Complex Systems and Complexity Science, 2006, 3 (1): 13-20. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200601001.htm [24] JIANG Jiao-jiao, WEN Sheng, YU Shui, et al. The structure of communities in scale-free networks[J]. Concurrency and Computation: Practice and Experience, 2016, DOI: 10.1002/cpe.4040. [25] NIE Ting-yuan, GUO Zheng, ZHAO Kun, et al. Using mapping entropy to identify node centrality in complex networks[J]. Physical A: Statistical Mechanics and its Applications, 2016, 453: 290-297. [26] OULDISMAIL A A O, AMOUZANDEH G, GRANT S C. Structural connectivity within neural ganglia: a default smallworld network[J]. Neuroscience, 2016, 337: 276-284. [27] 胡君辉, 徐新平, 杨永栩. 三组城市公共汽车运输网的小世界性质[J]. 广西师范大学学报: 自然科学版, 2006, 24 (2): 10-14. https://www.cnki.com.cn/Article/CJFDTOTAL-GXSF200602002.htmHU Jun-hui, XU Xin-ping, YANG Yong-xu. Small-world properties of 3bus-transport networks of China[J]. Journal of Guangxi Normal University: Natural Science Edition, 2006, 24 (2): 10-14. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXSF200602002.htm [28] GHOSHAL G, BARABSI A L. Ranking stability and superstable nodes in complex networks[J]. Nature Communications, 2011, DOI: 10.1038/ncomms1396. [29] PARK J, BARABSI A L. Distribution of node characteristics in complex networks[J]. Nature Communications, 2007, 104 (46): 17916-19720. [30] LIU Jun, XIONG Qing-yu, SHI Wei-ren, et al. Evaluating the importance of nodes in complex networks[J]. Physical A: Statistical Mechanics and its Applications, 2016, 452: 209-219. [31] 林强, 曹小曙. 广州城市社区交通特征空间分异研究[J]. 现代城市研究, 2008 (4): 74-82. https://www.cnki.com.cn/Article/CJFDTOTAL-XDCS200804010.htmLIN Qiang, CAO Xiao-shu. Research on characteristic differentiation of urban community transport in Guangzhou[J]. Modern Urban Research, 2008 (4): 74-82. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XDCS200804010.htm -
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