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Understanding intra-urban trip patterns from taxi trajectory data

Listed author(s):
  • Yu Liu


  • Chaogui Kang
  • Song Gao
  • Yu Xiao
  • Yuan Tian
Registered author(s):

    Intra-urban human mobility is investigated by means of taxi trajectory data that are collected in Shanghai, China, where taxis play an important role in urban transportation. From the taxi trajectories, approximately 1.5 million trips of anonymous customers are extracted on seven consecutive days. The globally spatio-temporal patterns of trips exhibit a significant daily regularity. Since each trip can be viewed as a displacement in the random walk model, the distributions of the distance and direction of the extracted trips are investigated in this research. The direction distribution shows an NEE–SWW-dominant direction, and the distance distribution can be well fitted by an exponentially truncated power law, with the scaling exponent β = 1.2 ± 0.15. The observed patterns are attributed to the geographical heterogeneity of the study area, which makes the spatial distribution of trajectory stops to be non-uniform. We thus construct a model that integrates both the geographical heterogeneity and distance decay effect, to interpret the observed patterns. Our Monte Carlo simulation results closely match to the observed patterns and thus validate the proposed model. According to the proposed model, in a single-core urban area, the geographical heterogeneity and distance decay effect improve each other when influencing human mobility patterns. Geographical heterogeneity leads to a faster observed decay, and the distance decay effect makes the spatial distribution of trips more concentrated. Copyright Springer-Verlag 2012

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    Article provided by Springer in its journal Journal of Geographical Systems.

    Volume (Year): 14 (2012)
    Issue (Month): 4 (October)
    Pages: 463-483

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    Handle: RePEc:kap:jgeosy:v:14:y:2012:i:4:p:463-483
    DOI: 10.1007/s10109-012-0166-z
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    1. Sunhee Sang & Morton O’Kelly & Mei-Po Kwan, 2011. "Examining Commuting Patterns," Urban Studies, Urban Studies Journal Limited, vol. 48(5), pages 891-909, April.
    2. Carlo Ratti & Riccardo Maria Pulselli & Sarah Williams & Dennis Frenchman, 2006. "Mobile Landscapes: using location data from cell phones for urban analysis," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 33(5), pages 727-748, September.
    3. Jan Vegelius & Svante Janson & Folke Johansson, 1986. "Measures of similarity between distributions," Quality & Quantity: International Journal of Methodology, Springer, vol. 20(4), pages 437-441, December.
    4. Sun, J.B. & Yuan, J. & Wang, Y. & Si, H.B. & Shan, X.M., 2011. "Exploring space–time structure of human mobility in urban space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 929-942.
    5. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
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