Statistical mechanism of passenger mobility behaviors for different transportations

Shaoyong Han, Qiang Guo (), Kai Yu (), Rende Li, Bing He and Jian-Guo Liu
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Shaoyong Han: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, No. 516, Jungong Road, Shanghai 200093, P. R. China†School of Software and Big Data, Changzhou College of Information Technology, No. 22, Mingxin Middle Road, Changzhou 213164, P. R. China
Kai Yu: #xB6;School of Public Management, Xinjiang University of Finance and Economics, No. 449, Beijing Middle Road, Urumqi 830012, P. R. China
Rende Li: #x2021;Library, University of Shanghai for Science and Technology, No. 516, Jungong Road, Shanghai 200093, P. R. China
Bing He: #x2225;Inspection and Maintenance Company, SMEPC, Shanghai 200063, P. R. China
Jian-Guo Liu: #xA7;School of Accountancy and Shanghai Key Lab. of Fin. Inf. Tech., Shanghai University of Finance and Economics, No. 777, Guoding Road Shanghai 200443, P. R. China¶School of Public Management, Xinjiang University of Finance and Economics, No. 449, Beijing Middle Road, Urumqi 830012, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 06, 1-13

Abstract: Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness 〈B〉=0 and memory 〈M〉=−0.5 for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability p and a random long time with probability 1−p. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when p=0.6 for three empirical datasets, which suggests the periodical behaviors with the probability p. This work helps in deeply understanding the regularity of human mobility behaviors.

Keywords: Mobility behavior; time interval; memory effect; probability model (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120500825

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