Abstract
Currently, smart card data analytics has caused new insights of human mobility patterns. Many applications of smart card data analytics, which have been applied from the bus traffic operation optimization to the traffic network optimization. Although the human travel behavioral features have been observed and revealed based on these statistical data, the diversity and dynamics are fundamental features of mobility data, requiring an in-depth understanding of the dynamic temporal-spatial features of these patterns. This paper measures the diversity and dynamics of human mobility patterns based on the smart card data of Chongqing. First, from individual mobility patterns, the measurement results indicate that the mobility patterns of urban passengers are similar during weekdays, but there is a distinct difference between weekdays and weekends. Second, based on the aggregated mobility patterns, each station has its own temporal profile. Specifically, the profiles of some stations are similar, because the land use types around these stations are identical. Third, based on the complex network theory, stations are divided into different clusters in a temporal scale. Interestingly, though clusters of stations are changing over time, adjacent stations which with close ids are always in the same cluster, because these stations are close to each other in geography. The above findings can help policymakers to make appropriate scheduling strategies and improve the efficiency of public transportation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Cheng, X., Yang, L., Shen, X.: D2D for intelligent transportation systems: a feasibility study. IEEE Trans. Intell. Transp. Syst. 16(4), 1784–1793 (2015)
Lin, X., Tampre, C.M.J., Viti, F., Immers, B.: The cost of environmental constraints in traffic networks: assessing the loss of optimality. Netw. Spat. Econ. 16(1), 349–369 (2016)
Chen, B.Y., Lam, W.H.K., Sumalee, A., Li, Q., Shao, H., Fang, Z.: Finding reliable shortest paths in road networks under uncertainty. Netw. Spat. Econ. 13(2), 123–148 (2013)
Anisi, M.H., Abdullah, A.H.: Efficient data reporting in intelligent transportation systems. Netw. Spat. Econ. 16(2), 623–642 (2016)
Bagchi, M., White, P.R.: The potential of public transport smart card data. Transp. Policy 12(5), 464–474 (2005)
Morency, C., Trpanier, M., Agard, B.: Measuring transit use variability with smart-card data. Transp. Policy 14(3), 464–474 (2007)
Li, X., Kurths, J., Gao, C., Zhang, J., Wang, Z., Zhang, Z.: A hybrid algorithm for estimating origin-destination flows. IEEE Access 6(1), 677–687 (2018)
Zhong, C., Manley, E., Mller Arisona, S., Batty, M., Schmitt, G.: Measuring variability of mobility patterns from multiday smart-card data. J. Comput. Sci. 9, 125–130 (2015)
Kieu, L.M., Bhaskar, A., Chung, E.: Passenger segmentation using smart card data. IEEE Trans. Intell. Transp. Syst. 16(3), 1537–1548 (2015)
Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.: Understanding commuting patterns using transit smart card data. J. Transp. Geogr. 58, 135–145 (2017)
Xiao, X., Jia, L., Wang, Y.: Dynamics of subway networks based on vehicles operation timetable. Phys. A-Stat. Mech. Appl. 473, 111–121 (2017)
Li, X., Guo, J., Gao, C., Su, Z., Bao, D., Zhang, Z.: Network-based transportation system analysis: a case study in a Mountain City. Chaos, Solitons Fractals 107, 256–265 (2018)
Yang, Y., Liu, Y., Zhou, M., Li, F., Sun, C.: Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing Subway. Saf. Sci. 79, 149–162 (2015)
Xing, Y., Lu, J., Chen, S., Dissanayake, S.: Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro. Pub. Transp. 9(3), 501–525 (2017)
Wei, L.H., Chang, C.Z., Wei, P.H.: Research on development strategies of China urban public transport. Appl. Mech. Mater. 744, 2086–2089 (2015)
Sedgwick, P.: Pearson’s correlation coefficient. Br. Med. J. 345, e4483 (2012)
Adler, J., Parmryd, I.: Stockholms universitet, Naturvetenskapliga fakulteten, Wenner-Grens institut: Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the mander’s overlap coefficient. Cytom. Part A 77A(8), 733–742 (2010)
Barthlemy, M.: Spatial networks. Phys. Rep. 499(1), 1–101 (2011)
Jones, P., Clarke, M.: The significance and measurement of variability in travel behaviour. Transportation 15, 1–2 (1988)
Thiemann, C., Theis, F., Grady, D., Brune, R., Brockmann, D.: The structure of borders in a small world. PLoS ONE 5(11), e15422 (2010)
Liu, H., Fen, L., Jian, J., Chen, L.: Overlapping community discovery algorithm based on hierarchical agglomerative clustering. Int. J. Pattern Recogn. Artif. Intell. 32(3), P1850008 (2018)
Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)
Acknowledgement
The authors would like to thank all editors and the anonymous reviewers for their constructive comments and suggestions. This work is supported by the Fundamental Research Funds for the Central Universities (No. XDJK2016A008), National Natural Science Foundation of China (Nos. 61402379, 61403315), Natural Science Foundation of Chongqing (No.cstc2018jcyjAX0274).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C., Gao, C., Xin, Y. (2018). Measuring the Diversity and Dynamics of Mobility Patterns Using Smart Card Data. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-99247-1_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99246-4
Online ISBN: 978-3-319-99247-1
eBook Packages: Computer ScienceComputer Science (R0)