Authors
Abstract
This paper aims to explore the human mobility pattern by using taxi positioning data in the city of Isfahan. Results show that Lognormal yields the most suitable fit to the data at hand which has been collected from 53000 records throughout Isfahan in a week. Power law did not show acceptable performance and exponential function did not function as good as Lognormal. It was also shown that transportation hierarchy does not sufficiently explain the human mobility pattern. The trip lengths are shown to be 5 and 7 kilometers for working days and holidays respectively. Peak hour trip length distribution function shows lower mean, standard deviation, skewness, and kurtosis compared to the whole day figures.
Keywords
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[3] Belik, V., Geisel, T. & Brockmann, D. Natural human mobility patterns and spatial spread of infectious diseases. Phys. Rev. X 1, 011001 (2011).
[4] Gabaix, X., Gopikrishnan, P., Plerou, V. & Stanley, H. E. A theory of power-law distribution in financial market fluctuations. Nature 423, 267-270, (2003).
[5] Han, X. P., Hao, Q., Wang, B. H., & Zhou, T. Origin of the scaling law in human mobility: Hierarchy of traffic systems. Physical Review E, 83(3), 036117, (2011).
[6] Zhao, K., Musolesi, M., Hui, P., Rao, W., & Tarkoma, S. Explaining the power-law distribution of human mobility through transportation modality decomposition. Scientific reports, 5, (2015).
[7] Liang, X., Zhao, J., Dong, L., & Xu, K. Unraveling the origin of exponential law in intra-urban human mobility. Scientific reports, 3, (2013).
[8] Liang, X., Zheng, X., Lv, W., Zhu, T., & Xu, K. The scaling of human mobility by taxis is exponential. Physica A: Statistical Mechanics and its Applications, 391(5), 2135-2144, (2012).
[9] Zhao, K., Musolesi, M., Hui, P., Rao, W. & Tarkoma, S. Explaining the power-law distribution of human mobility through transportation modality decomposition. Scientific reports 5 (2015).
[10] Gallotti, R., Bazzani, A., & Rambaldi, S. Understanding the variability of daily travel-time expenditures using GPS trajectory data. EPJ Data Science, 4(1), 1-14, (2015).
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