Structural complexity and spatial differentiation characteristics of taxi trip trajectory network

FU Xin; YANG Yu; SUN Hao

Volume 17 Issue 2

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2017 > 17(2): 106-116

Next Previous

FU Xin, YANG Yu, SUN Hao. Structural complexity and spatial differentiation characteristics of taxi trip trajectory network[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 106-116.

Citation:

FU Xin, YANG Yu, SUN Hao. Structural complexity and spatial differentiation characteristics of taxi trip trajectory network[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 106-116.

Citation:

PDF( 4953 KB)

Structural complexity and spatial differentiation characteristics of taxi trip trajectory network

FU Xin^{1, 2
,},
YANG Yu²,
SUN Hao³

1.
School of Economics and Management, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3.
School of Civil Engineering, Xi'an University of Architecture andTechnology, Xi'an 710055, Shaanxi, China

More Information

Author Bio:
FU Xin(1982-), male, lecturer, PhD, +86-29-82334857, fuxin@chd.edu.cn
Received Date: 2016-11-18
Publish Date: 2017-04-25

Abstract

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.
- traffic engineering,
- taxi,
- complex network,
- GPS trajectory data,
- topology structure,
- spatial differentiation

FullText(HTML)

References(31)

References

[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]	JIAO 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]	ZHOU 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]	XIN 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]	YUAN 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]	YU 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]	YANG 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]	LIU 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]	ZHOU 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]	SUN 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]	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]	ZHAO 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]	LUO 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]	WANG 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]	HU 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]	LIN 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