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Measuring the Diversity and Dynamics of Mobility Patterns Using Smart Card Data

  • Chengmei Liu
  • Chao Gao
  • Yingchu Xin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

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.

Keywords

Public transportation Mobility patterns Diversity and dynamics Crowd flow network 

Notes

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).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer and Information ScienceSouthwest UniversityChongqingChina

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