01697402

Component

Vacant taxi drivers' cruising behavior to seek the next potential passenger in a road network generates additional vehicle traveled miles, adding congestion and pollution into the road network and the environment. This paper aims to employ a Markov Decision Process (MDP) to model idle e-hailing drivers' optimal sequential decisions in passenger-seeking. While there exist a few studies that applied MDP to taxi drivers searching behavior, these studies were primarily focused on modeling traditional taxi drivers behavior. Transportation network companies (TNC) or e-hailing (e.g., Didi, Uber) drivers exhibit different behaviors from traditional taxi drivers because the e-hailing drivers do not need to actually search passengers. Instead, they reposition themselves so that the matching platform can match a passenger. Accordingly, the authors incorporate e-hailing drivers' new behavioral features into our MDP. They then use 5,367 Didi drivers 1-month trajectories to train the model. To validate the effectiveness of the model, a Monte Carlo simulation is conducted to simulate the performance of drivers if they follow optimal policies derived from our model. Two metrics, the rate of return and taxi utilization rate, show that the authors' model can help drivers improve taxi utilization rate by 9% in morning peak and off-peak and 6% in the afternoon peak and increase drivers rate of return by 25% in the morning peak and 14%-16% during the off-peak and the afternoon peak.

This paper was sponsored by TRB committee ABE90 Standing Committee on Transportation in the Developing Countries.

19-04595

English

7p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Behavior; Demand responsive transportation; Markov processes; Monte Carlo method; Simulation; Taxi services; Taxicab drivers

Ridehailing

Highways; Passenger Transportation; Society

Transportation Research Board Annual Meeting 2019 Paper #19-04595

TRIS, TRB, ATRI

Dec 7 2018 9:26AM