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Vacant taxi drivers' passenger seeking process in a road network generatesadditional vehicle miles traveled, adding congestion and pollution into theroad network and the environment. This paper aims to employ a Markov DecisionProcess (MDP) to model idle e-hailing drivers' optimal sequential decisions inpassenger-seeking. Transportation network companies (TNC) or e-hailing (e.g.,Didi, Uber) drivers exhibit different behaviors from traditional taxi driversbecause e-hailing drivers do not need to actually search for passengers.Instead, they reposition themselves so that the matching platform can match apassenger. Accordingly, we incorporate e-hailing drivers' new features into ourMDP model. The reward function used in the MDP model is uncovered by leveragingan inverse reinforcement learning technique. We then use 44,160 Didi drivers'3-day trajectories to train the model. To validate the effectiveness of themodel, a Monte Carlo simulation is conducted to simulate the performance ofdrivers under the guidance of the optimal policy, which is then compared withthe performance of drivers following one baseline heuristic, namely, the localhotspot strategy. The results show that our model is able to achieve a 17.5%improvement over the local hotspot strategy in terms of the rate of return. Theproposed MDP model captures the supply-demand ratio considering the fact thatthe number of drivers in this study is sufficiently large and thus the numberof unmatched orders is assumed to be negligible. To better incorporate thecompetition among multiple drivers into the model, we have also devised andcalibrated a dynamic adjustment strategy of the order matching probability.

Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning