GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

Abstract

Ride-hailing, as a popular shared-transportation method, has been operated in many areas all over the world. Researchers conducted various researches based on global cases. They argued on whether car-hailing is an effective travel mode for emission reduction and drew different conclusions. The detailed emission performance of the ride-hailing system depends on the cases. Therefore, there is an urgent demand to reduce the overall picking up distance during the dispatch. In this study, we try to satisfy this demand by proposing an optimization method combined with a prediction model to minimize the global void cruising distance when solving the dispatch problem. We use Didi ride-hailing data on one day for simulation and found that our method can reduce the picking up distance by 7.51% compared with the baseline greedy algorithm. The proposed algorithm additionally makes the average waiting time of passengers more than 4 minutes shorter. The statistical results also show that the performance of our method is stable. Almost the metric in all cases can be kept in a low interval. What is more, we did a day-to-day comparison. We found that, despite the different spatial-temporal distribution of orders and drivers on different day conditions, there are little differences in the performance of the method. We also provide temporal analysis on the changing pattern of void cruising distance and quantity of orders on weekdays and weekends. Our findings show that our method can averagely reduce more void cruising distance when ride-hailing is active compared with the traditional greedy algorithm. The result also shows that the method can stably reduce void cruising distance by about 4000 to 5000 m per order across one day. We believe that our findings can improve deeper insight into the mechanism of the ride-hailing system and contribute to further studies.

Impacts of ride-hailing on energy and the environment: a systematic review

“…Theoretical studies show great promise of ride-hailing for reducing deadheading, VMT, and ultimately emissions. Wang et al [57] perform an optimization with deep learning neural network to minimize deadheading. Simulations with DiDi Chuxing data from Chengdu, China, show the system can reduce picking up distance by 7.5%.…”

Modeling the Impacts of Driver Income Distributions on Online Ride-Hailing Services

“…Set the initial arrival rate of drivers λ 0 � 2500 vehicles/h, the initial arrival rate of passengers μ 0 � 5000 Passengers/h, the average travel time t � 0.5 hours, and the probability of drivers leaving the system q leave � 0.8. Assuming the psychological expected price of drivers and passengers f c , f V ∼ N (3,1). e driver cost is 1.11 yuan/km, and the platform cost is 0.14p.…”

“…Ride-hailing, referring to the activity of calling a vehicle or driver to go to a destination, rises in many metropolitan areas and takes a considerable part of mobility services [1]. e online ride-hailing platform is a subversion of the traditional travel industry in the context of the Internet; it transmits the traditional service mode from the driver side to the passenger side by the combination of online information and offline experience [2,3].…”

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

“…Xiao et al found that Shanghai presents a two-level hierarchical polycentric urban structure feature by applying a spatial embedded network model [40]. To build the original destination (OD) matrix, the common types of traffic analysis unit, include regular grids [41][42][43][44][45], hexagons [46], and irregular polygons (usually obtained by dividing the road network or district boundary). In particular, the regular grid is the most common due to its advantages, such as ease of use and visualization.…”