Characterizing the human mobility pattern in a large street network.

By Bin Jiang, Junjun Yin, S. Zhao. Physical review. E, Statistical, nonlinear, and soft matter physics, 2008

315 citations recorded by the source corpus.

Previous studies demonstrated empirically that human mobility exhibits Lévy flight behavior. However, our knowledge of the mechanisms governing this Lévy flight behavior remains limited. Here we analyze over 72,000 people's moving trajectories, obtained from 50 taxicabs during a six-month period in a large street network, and illustrate that the human mobility pattern, or the Lévy flight behavior, is mainly attributed to the underlying street network. In other words, the goal-directed nature of human movement has little effect on the overall traffic distribution. We further simulate the mobility of a large number of random walkers and find that (1) the simulated random walkers can reproduce the same human mobility pattern, and (2) the simulated mobility rate of the random walkers correlates pretty well (an R square up to 0.87) with the observed human mobility rate.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/06f87eff212706364b192a3b4310f3b9d1dd6dff

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Citations in the corpus

Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “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.”
Recommendations in location-based social networks: a survey (2015, unknown): “, [21,11,49].”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, background): “Examples include the use of taxi data that is mostly suitable for studying drivers’ patterns in searching for passengers (Jiang et al., 2009; Liu et al., 2012), use of transit smart card data that only captures the use of transit modes (Long and Thill, 2015), or the use of social media data whose sp”
Intermittent search strategies (2011, background): “For example, when targets location is known, exploitation could be optimized instead of search, and Lévy walks could emerge from interactions between the environment and the searcher (Boyer et al., 2006; Jiang et al., 2009; Santos et al., 2007).”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “Recently, human mobility patterns have drawn much attention in the areas of physics [7], geography [8,9], and computer science [10], with the availability of multi-sourced trajectory data [11].”
Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity (2014, background): “Interestingly, the scaling of geographic space has had an enormous effect on human activities; human activities and interactions in geographic space exhibit power law distributions as well (Brockmann et al. 2006, Gonzalez et al. 2008, Jiang et al. 2009).”