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Modeling the distribution of daily and hourly human mobility metrics is beneficial for studying underlying human travel patterns. In previous studies, some probability distribution functions were employed in order to establish a base for human mobility research. However, the selection of the most suitable distribution is still a challenging task. In this paper, we focus on modeling the distributions of travel distance, travel time, and travel speed. The daily and hourly trip data are fitted with several candidate distributions, and the best one is selected based on the Bayesian information criterion. A case study with online car-hailing data in Xi’an, China, is presented to demonstrate and evaluate the model fit. The results indicate that travel distance and travel time of daily and hourly human mobility tend to follow Gamma distribution, and travel speed can be approximated by Burr distribution. These results can contribute to a better understanding of online car-hailing travel patterns and establish a base for human mobility research.

Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data—An Empirical Study in Xi’an, China