Understanding Human Mobility Within Metro Networks – Flow Distribution and Community Detection

  • Zuoxian Gan Dalian Maritime University
  • Jing Liang Dalian Maritime University
Keywords: metro system, OD flows, shifted power law, community partition

Abstract

In this paper, smart card data collected from the Nanjing Metro over 2-hour time periods are used to characterize within- and between-day human mobility patterns within the metro network. Results show that the OD (origin to destination) flows can be characterized well by shifted power law distributions with similar exponents around 2, which reflects the fact that a few OD pairs in the system play a dominant role and undertake disproportionately large OD flow distribution. The different exponents signify heterogeneous human movement in within- and between-day ranges. In addition, we analyze the metro community structures over different time periods based on the community detection method using random walks to visualize and understand passenger movement from a spatial perspective. Normalized mutual information is used to compare community partitions over different time-intervals. The results show that the properties of human mobility during different time periods have a similar rhythm, although some nuances exist, and the community structure is usually divided according to the line distribution. This empirical study provides spatiotemporal insights into understanding urban human mobility and some potential applications for transportation management.

References

Foell S, Phithakkitnukoon S, Veloso M, Kortuem G, Bento C. Regularity of Public Transport Usage: A Case Study of Bus Rides in Lisbon. Portugal, Journal of Public Transportation. 2016;19(4): 161-71.

Roth C, Kang SM, Batty M and Barthélemy M. Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS ONE 2011;6(1): e15923.

Mari L, Bertuzzo E, Righetto L, Casagrandi R, Gatto M, Rodriguez-Iturbe I, Rinaldo A. Modelling cholera epidemics: the role of waterways, human mobility and sanitation. Journal of the Royal Society Interface. 2012;67(9): 376-88.

Zhong C, Arisona SM, Huang X, Batty M, Schmitt G. Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science. 2014;28(11): 2178-2199.

Belik V, Geisel T, Brockmann D. Natural human mobility patterns and spatial spread of infectious diseases. Physical Review X. 2011;1(1): 011001.

Morency C, Trépanier M, Agard B. Measuring transit use variability with smart-card data. Transport Policy 2007;14(3): 193-203.

Chen C, Chen J, Barry J. Diurnal pattern of transit ridership: a case study of the New York City subway system. Journal of Transport Geography. 2009;17(3): 176-186.

Kim K, Oh K, Lee YK, Kim S, Jung JY. An analysis on movement patterns between zones using smart card data in subway networks. International Journal of Geographical Information Science. 2014;28(9): 1781-1801.

Reades J, Zhong C, Manley ED, Milton R, Batty M. Finding Pearls in London's Oysters, Built Environment. 2016;42(3): 365-381.

Sun L, Ling X, He K, Tan Q. Community structure in traffic zones based on travel demand. Physica A 2016;457: 356-363.

Xu Q, Mao BH, Bai Y. Network structure of subway passenger flows. Journal of Statistical Mechanics: Theory and Experiment. 2016;3: 033404.

El Mahrsi MK, Côme E, Oukhellou L, Verleysen M. Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems. 2017;18(3): 712-728.

Wang Y, de Almeida Correia GH, de Romph E, Timmermans H. Using metro smart card data to model location choice of after-work activities: An application to Shanghai. Journal of Transport Geography. 2017;63: 40-47.

Jiang S, Guan W, Zhang W, Chen X, Yang L. Human mobility in space from three modes of public transportation. Physica A. 2017;483: 227-238.

Soh H, Lim S, Zhang T, Fu X, Lee GKK, Hung TGG, Di P, Prakasam S, Wong L. Weighted complex network analysis of travel routes on the Singapore public transportation system. Physica A 2010;389: 5852-5863.

Newman ME, Girvan M. Finding and evaluating community structure in networks. Physical Review E 2004;69: 026113.

Danon L, Diaz-Guilera A, Duch J, Arenas A. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment. 2005;9: P09008.

Pons P, Latapy M. Computing communities in large networks using random walks, Journal of Graph Algorithms and Applications. 2005;10(2): 191-218.

Orman GK, Labatut V, Cherifi H. Comparative evaluation of community detection algorithms: a topological approach. Journal of Statistical Mechanics: Theory and Experiment. 2012;8: P08001.

Liu X, Gong L, Gong Y, Liu Y. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography. 2015;43: 78-90.

Gallos LK, Song C, Makse HA. A review of fractality and self-similarity in complex networks. Physica A 2007;386: 686-691.

Clauset A, Shalizi CR, Newman ME. Power-law distributions in empirical data. SIAM Review. 2009;51: 661-703.

Liang X, Zheng X, Lv W, Zhu T, Xu K. The scaling of human mobility by taxis is exponential. Physica A 2012;391: 2135-2144.

Chang H, Su BB, Zhou YP, He DR. Assortativity and act degree distribution of some collaboration networks. Physica A 2007;383: 687-702.

Csardi G, Nepusz T. The igraph software package for complex network research. Complex System. 2006;1695: 1-9.

Published
2021-06-01
How to Cite
1.
Gan Z, Liang J. Understanding Human Mobility Within Metro Networks – Flow Distribution and Community Detection. Promet - Traffic&Transportation. 2021;33(3):413-2. DOI: 10.7307/ptt.v33i3.3594
Section
Articles