Space-time dynamics of cab drivers' stay behaviors and their relationships with built environment characteristics
Introduction
As one of the key components of urban transportation systems, taxis provide an all-weather, convenient, and personalized travel service for urban residents. Improving the efficiency of taxi services based on the knowledge of cab drivers' behavioral patterns is essential for transport planning and management of urban facilities (Kang & Qin, 2016). When and where taxis tend to stay are important but often understudied aspects of drivers' behaviors. It is necessary to investigate cab drivers' stay behaviors for several reasons. First, cab drivers' stay behaviors reflect specific types of activities that are linked to the spatial configuration of urban facilities. For instance, many vacant taxis queue up in specific areas for purposes including refueling or dining. Improving our knowledge of these behaviors could help cities better satisfy the needs of taxi drivers. Second, cab drivers' stay behaviors also reflect their operation behaviors (e.g., waiting for passengers), which in turn convey useful information about taxi supply and the areas where potential demand might be high.
Previous studies on cab drivers' activities mainly rely on data collected through travel surveys, questionnaires or interviews. Most of these studies focus on analyzing driving behavior without paying enough attention to drivers' stay activities (Kalhori, Foroughinia, & Ziapour, 2017; Ma, Yan, Huang, & Abdel-Aty, 2010; Newnam, Mamo, & Tulu, 2014; Shi, Tao, Li, Xiao, & Atchley, 2014; Zhang et al., 2018). The few studies that analyze cab drivers' dining behavior are based on questionnaire and survey data (Song et al., 2012; Zhang, Zheng, Lu, & Chai, 2009). This has led to a scarcity of knowledge on cab drivers' stay behavior, an important dimension of their activities beyond movements along road networks.
Overall, studies on cab drivers' stay behavior are sparse. As data collection of surveys and questionnaires are costly and time consuming, they usually cover small sample sizes, which may not be able to delineate cab drivers' behavior at a large scale. In recent years, the explosive growth of GPS-based taxi trajectories has provided massive opportunities for analyzing cab drivers' behaviors. Compared to traditional data based on travel surveys or questionnaires, GPS-based taxi trajectory data are collected by tens of thousands of taxis simultaneously at low costs and high efficiency. A strand of studies on quantifying cab driver's mobility patterns based on GPS taxi trajectory data is continuously emerging. For instance, cab drivers' operational behavior (Gao, Jiang, & Xu, 2018; Liu, Andris, & Ratti, 2010; Manley, Addison, & Cheng, 2015; Tang et al., 2016), refueling behavior (Niu, Liu, Fu, Liu, & Lang, 2016; Zhang, Yuan, Wilkie, Zheng, & Xie, 2015), and shift handover behavior (Sun & Yu, 2014; Zhang et al., 2015) have been investigated based on GPS taxi trajectory data. Despite the various types of cab drivers' behaviors examined in these studies, little effort has been devoted to systematically investigating cab drivers' stay behaviors.
In this paper, we use GPS-based taxi trajectory data collected in Wuhan, China to investigate three aspects of cab drivers' stay behaviors, namely, the regularities of cab drivers' stay behaviors across space, the spatiotemporal variations of stay frequency, and the relationship between stay frequency and certain built environment characteristics. Our specific research questions are as follows: (1) What are the key characteristics of the spatial distributions of these stay activities? (2) How do the spatial patterns of such activities change over time? and (3) How are cab drivers' collective stay patterns related to the underlying built environment? To answer these questions, we apply a trajectory prepossessing method to identify taxis' stay points, from which the intensity and evolution of cab drivers' stay activities are mapped and analyzed. Two models are utilized to measure stay activity frequency in this study, namely the rank-size distribution and the frequency probability distribution. Second, the spatial distributions of stay activities during different periods are analyzed. Finally, we apply ordinary least squares regression (OLS) and geographically weighted regression (GWR) models to investigate the relationships between stay activities and certain built environment characteristics.
Section snippets
Quantifying cab drivers' behavioral patterns
The increasing availability of GPS-based taxi trajectory data has led to an enormous amount of studies on human behavioral patterns, including both passengers and cab drivers (Liu, Kang, Gao, Xiao, & Tian, 2012; Tang, Liu, Wang, & Wang, 2015; Zhang, Xu, Tu, & Ratti, 2018; Zhao, Qin, Ye, Wang, & Chen, 2017). In this section, we mainly focus on studies on cab drivers' behavioral patterns based on GPS taxi trajectory data. The related studies are discussed as follows. For instance, Liu et al.
Study area and datasets
Wuhan — the capital city of Hubei Province — is a densely populated metropolitan area in central China. It has an area of 8594 km2 and a population of about 11 million as of 2017. The city is an economic, education, and cultural hub in central China. In this study, we focus on the area within the Wuhan Outer Ring Road, where the majority of taxi trips occurred (Fig. 1a). Traffic analysis zone (TAZ) is selected as the analysis unit in this work. TAZs are normally constructed based on the
The regularities of cab drivers' stay activities
In this subsection, we explore the regularities of cab drivers' stay frequency based on the rank-size distribution and probability distribution. Fig. 3 illustrates the rank-size distributions and probability distributions of cab drivers' stay frequency on weekday and weekend at the TAZ level. Stay frequencies on weekdays and weekends are obtained by calculating the average values of frequency on weekdays and weekends respectively. As shown in Fig. 3(a), stay frequency decays dramatically for
Discussion and conclusions
Understanding cab drivers' stay behaviors could benefit the planning and management of public facilities that are tied to taxis' daily operations. However, very limited efforts have been devoted to studying this particular aspect of driver behavior. To fill the research gap, this study investigates cab drivers' stay behaviors using a GPS-based taxi trajectory dataset collected in Wuhan, China. The stay points of cab drivers are extracted from the trajectory data. These activities are then
CRediT authorship contribution statement
Pengxiang Zhao:Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing.Yang Xu:Conceptualization, Methodology, Investigation, Writing - original draft, Writing - review & editing.Xintao Liu:Conceptualization, Investigation, Writing - review & editing.Mei-Po Kwan:Conceptualization, Investigation, Writing - review & editing.
Acknowledgement
The authors would like to thank the anonymous reviewers and the editor for their valuable comments and suggestions on earlier versions of the manuscript. This research is jointly supported by the Hong Kong Polytechnic University Start-Up Grant (no. 1-ZE6P, no. 1-BE0J), the National Natural Science Foundation of China (no. 41529101, no. 41801372), and a grant from the Research Committee on Research Sustainability of Major RGC Funding Scheme of the Chinese University of Hong Kong.
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