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

arXiv: https://arxiv.org/abs/1905.09906 · HTML: https://mlsanity.com/paper/1905.09906/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 10 of 10)

- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms](https://mlsanity.com/paper/2105.08791/) (2021)
- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [Initial State Interventions for Deconfounded Imitation Learning](https://mlsanity.com/paper/2307.15980/) (2023)
- [A multi-functional simulation platform for on-demand ride service operations](https://mlsanity.com/paper/2303.12336/) (2023)
- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning](https://mlsanity.com/paper/2104.02661/) (2021)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)

## Abstract (arXiv)

Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, 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. Transportation network companies (TNC) or e-hailing (e.g., Didi, Uber) drivers exhibit different behaviors from traditional taxi drivers because e-hailing drivers do not need to actually search for passengers. Instead, they reposition themselves so that the matching platform can match a passenger. Accordingly, we incorporate e-hailing drivers' new features into our MDP model. The reward function used in the MDP model is uncovered by leveraging an inverse reinforcement learning technique. We then use 44,160 Didi drivers' 3-day trajectories to train the model. To validate the effectiveness of the model, a Monte Carlo simulation is conducted to simulate the performance of drivers under the guidance of the optimal policy, which is then compared with the performance of drivers following one baseline heuristic, namely, the local hotspot 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. The proposed MDP model captures the supply-demand ratio considering the fact that the number of drivers in this study is sufficiently large and thus the number of unmatched orders is assumed to be negligible. To better incorporate the competition among multiple drivers into the model, we have also devised and calibrated a dynamic adjustment strategy of the order matching probability.