Abstract
Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.
Highlights
In recent years, smart cities have been recognized as a promising research hotspot around the world [1,2,3]
The Gamma distribution can fit more than 90% of the hourly travel distance data, and the Burr distribution can achieve a good fit for 85% of the hourly travel speed data
We use the trajectory data collected from Didi Chuxing in Xi’an, China to explore the temporal characterizations of intra-urban human travel patterns
Summary
Smart cities have been recognized as a promising research hotspot around the world [1,2,3]. Exploring the spatio-temporal patterns of human mobility based on multi-source big data plays an important role in analyzing the formation of social-economic phenomena in smart cities. Online car-hailing data are characterized by large-scale, high-resolution and high-quality, which compensates for the shortcomings of the data mentioned above This brings about new opportunities and challenges to further understand human travel behavior and intra-urban mobility. This study is indispensable and can gain valuable insight into human mobility patterns, so as to address some of the challenges in smart cities To address these questions, this paper adopts a two-month dataset collected from about 18,000 online car-hails to analyze intra-urban travel patterns. (Q1), median (Q2), third quartile (Q3), interior upper limit, and extreme upper limit, are calculated and adopted to present the characteristics of daily and hourly mobility patterns
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