Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

TL;DR

This paper presents an environment-aware deep reinforcement learning approach for dynamic route optimization in urban vehicle services, significantly improving profitability and adaptability to unexpected events like COVID-19.

Contribution

It introduces a novel reinforcement learning framework with environment adaptability and a self-check mechanism for urban route optimization tasks.

Findings

Achieved over 98% improvement in taxi driver profitability.

Demonstrated robustness to environment changes such as COVID-19.

Outperformed state-of-the-art methods in comprehensive experiments.

Abstract

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).