Abstract
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.
Highlights
The modeling of human mobility is an emergent research area
This study models the distributions of human mobility metrics based on actual trajectory datasets, including about 18,000 online car-hailing rides, collected in Xi’an, China
Three trip metrics—travel distance, travel time, and travel speed—are highlighted in order to establish a base for human mobility research
Summary
The modeling of human mobility is an emergent research area. Studying the regularity and characteristics of human spatiotemporal mobility is of great significance in many fields, such as urban planning [1,2], traffic forecasting [3], and epidemic prevention [4,5].When modeling human mobility, it is common to consider the probability distribution function (PDF) of its metrics (e.g., travel distance, travel time, and travel speed). The modeling of human mobility is an emergent research area. Studying the regularity and characteristics of human spatiotemporal mobility is of great significance in many fields, such as urban planning [1,2], traffic forecasting [3], and epidemic prevention [4,5]. It is common to consider the probability distribution function (PDF) of its metrics (e.g., travel distance, travel time, and travel speed). It is generally accepted that daily and hourly human mobility metrics have a representative distribution [6]. Modeling the distributions of these metrics is fundamental, necessary, and beneficial for studying underlying travel patterns and establishing a base for human mobility research. With the rapid development of information and communication technique (ICT) and location-based service (LBS) applications, online car-hailing equipped with
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