Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning

• URL: http://arxiv.org/abs/2010.11738v1
• Date: Thu, 22 Oct 2020 13:55:26 GMT
• Title: Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning
• Authors: Shen Ren, Qianxiao Li, Liye Zhang, Zheng Qin and Bo Yang
• Abstract summary: We aim to optimise routing policies for a large fleet of vehicles for street-hailing services. A model-based dispatch algorithm, a model-free reinforcement learning based algorithm and a novel hybrid algorithm have been proposed.
• Score: 32.322091943124555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The future of mobility-as-a-Service (Maas)should embrace an integrated system of ride-hailing, street-hailing and ride-sharing with optimised intelligent vehicle routing in response to a real-time, stochastic demand pattern. We aim to optimise routing policies for a large fleet of vehicles for street-hailing services, given a stochastic demand pattern in small to medium-sized road networks. A model-based dispatch algorithm, a high performance model-free reinforcement learning based algorithm and a novel hybrid algorithm combining the benefits of both the top-down approach and the model-free reinforcement learning have been proposed to route the \emph{vacant} vehicles. We design our reinforcement learning based routing algorithm using proximal policy optimisation and combined intrinsic and extrinsic rewards to strike a balance between exploration and exploitation. Using a large-scale agent-based microscopic simulation platform to evaluate our proposed algorithms, our model-free reinforcement learning and hybrid algorithm show excellent performance on both artificial road network and community-based Singapore road network with empirical demands, and our hybrid algorithm can significantly accelerate the model-free learner in the process of learning.

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