Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

Abstract

With the rapid development of information communication technology and data mining technology, we can obtain taxi vehicle’s real time operation status through the large-scale taxi GPS trajectories data and explore the drivers’ activity distribution characteristics. Based on the 204 continuous hours of 3198 taxi vehicles’ operation data of Shenzhen, China, this paper analyzed the urban taxi driver’s activity distribution characteristics from different temporal and spatial levels. In the time level, we identified the difference with taxi daily operation pattern (weekday versus weekends), continuous time in one day, passengers in vehicle time, and taxi drivers’ operation frequency; in the space level, we explored the taxi driver’s searching pattern, including searching activity space distribution and the relationship between the pick-up locations and the drop-off locations. This research can be helpful for urban taxi drivers’ operation and behavior pattern identification, as well as the contribution to the geographical activity space analysis.

Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

“…In Shenzhen, the peak hour of taxi passenger's is almost at the midnight, such as in TAZ2, TAZ7, and TAZ8, which is similar to the research of Hu et al (2014).…”

“…Each taxi driver's daily activity space area mean center may have the relationship with the centroid of the whole taxi drivers' activity space [19], similar to the Susilo & Kitamura (2005) [30] analysis of the worker's daily activity locations relationship. We can analyze taxi driver's day-to-day variation on activity space and statistically analyze the second moments of activity locations.…”

“…Recently researchers have combined taxi GPS data with mathematical models (Lévy flights model or Zipf distribution law) to analyze the passenger's visiting frequency at one area [17], trip length distribution [18], and drivers' behavior [11,19]. However, the existing researchers paid less attention to the taxi drivers' behavior for different lengths of observation 2 Journal of Advanced Transportation period; meanwhile, the relationship between land use and passenger demand has not been explored.…”

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

“…For example, Hu et al [2] found that the location in which taxi drivers' cruise and search for the next passenger' pick-up location will be near the previous drop-off location by analyzing the statistical results of the distance between each taxi driver's pick-up locations' mean center and drop-off locations' mean center. Dong et al [5] classified drivers into three groups based on revenue and compared different driver groups in terms of spatiotemporal and strategy aspects.…”

“…Cabdrivers' mobility patterns and operating strategies have been widely studied since large-scale taxi GPS trajectories became obtainable due to the rapid development of information and communication technologies (ICT). For example, previous studies analyzed the distance and direction distributions of intraurban trips [1], urban cabdrivers' activity distribution [2], spatial variations in urban taxi ridership [3], cabdrivers' operation strategies with respect to revenue [4,5], and alternative taxi services strategies [6].…”