Understanding intra-urban trip patterns from taxi trajectory data

Abstract

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.

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Notes

  1. 1.

    The traffic measured in Erlang values represents the average number of concurrent calls carried by a mobile phone tower. The motion of mobile users leads to varying traffic intensities of corresponding base stations, which can be measured using Erlang values.

  2. 2.

    A number of types of floating car data, such as cellular network-based data and electronic toll-based data, are available at present. This research uses GPS-based floating car data.

  3. 3.

    According to the report of Shanghai Municipal Transport and Port Authority, http://www.jt.sh.cn/.

  4. 4.

    It should be noted that all PUPs and DOPs are in streets, and people usually walk to a street for taxi services. This makes the extracted PUPs and DOPs slightly different from the actual origins and destinations. With respect to the global trips patterns, such differences do not change the distributions of distance and direction much.

  5. 5.

    http://www.ornl.gov/sci/landscan/.

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Acknowledgments

This research is supported by NSFC (Grant nos. 40928001 and 41171296) and the National High Technology Development 863 Program of China (Grant nos. 2011AA120301 and 2011AA120303).

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Correspondence to Yu Liu.

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Liu, Y., Kang, C., Gao, S. et al. Understanding intra-urban trip patterns from taxi trajectory data. J Geogr Syst 14, 463–483 (2012). https://doi.org/10.1007/s10109-012-0166-z

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Keywords

  • Intra-urban human mobility
  • Taxi trajectory
  • Geographical heterogeneity
  • Distance decay
  • Monte Carlo simulation

JEL Classification

  • C15
  • R40