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It is often difficult for the ridesourcing drivers to get a trip immediately after dropping off a passenger. The main objective of the drivers is to increase their income by serving more trips. The most prominent options available to the drivers after reaching passengers’ destinations are: (a) park and wait in and around their drop-off location, (b) cruise in and around their drop-off location and (c) drive to another location to receive trip requests quickly. Previous studies were conducted to understand the driver behaviour in a taxi and other similar services. However, the perception of ridesourcing drivers on parking and waiting after dropping off passengers is yet to be explored. The drivers’ decision on waiting can affect users’ waiting time, the number of matched trips by the TNCs, and parking spaces in the city. Moreover, drivers’ waiting time tolerance can also impact other drivers’ total number of trips, total earnings, total distance travelled in the city, and fleet size. The aim of this study is to understand the influence of drivers’ characteristics on drivers’ decision to park and wait after dropping off a passenger. This study estimates and compares the waiting time tolerance of the ridesourcing drivers using a zero-inflated cox spline model between Perth and Kolkata. It is observed that drivers in Kolkata have higher waiting time tolerance than Perth drivers. Moreover, the drivers in both the cities are more likely to wait at high-demand areas urging the urban authorities to determine spatio-temporal parking demand to design the parking infrastructure for such areas.
It is often difficult for the ridesourcing drivers to get a trip immediately after dropping off a passenger. The main objective of the drivers is to increase their income by serving more trips. The most prominent options available to the drivers after reaching passengers’ destinations are: (a) park and wait in and around their drop-off location, (b) cruise in and around their drop-off location and (c) drive to another location to receive trip requests quickly. Previous studies were conducted to understand the driver behaviour in a taxi and other similar services. However, the perception of ridesourcing drivers on parking and waiting after dropping off passengers is yet to be explored. The drivers’ decision on waiting can affect users’ waiting time, the number of matched trips by the TNCs, and parking spaces in the city. Moreover, drivers’ waiting time tolerance can also impact other drivers’ total number of trips, total earnings, total distance travelled in the city, and fleet size. The aim of this study is to understand the influence of drivers’ characteristics on drivers’ decision to park and wait after dropping off a passenger. This study estimates and compares the waiting time tolerance of the ridesourcing drivers using a zero-inflated cox spline model between Perth and Kolkata. It is observed that drivers in Kolkata have higher waiting time tolerance than Perth drivers. Moreover, the drivers in both the cities are more likely to wait at high-demand areas urging the urban authorities to determine spatio-temporal parking demand to design the parking infrastructure for such areas.
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