Long-term Joint Scheduling for Urban Traffic

TL;DR

This paper introduces JLRLS, a reinforcement learning-based multi-modal traffic scheduling system that optimizes long-term urban transportation efficiency by considering layout changes and multiple transport modes.

Contribution

It proposes a novel multi-modal, long-term joint scheduling scheme using reinforcement learning, addressing previous limitations of short-term, single-mode approaches.

Findings

Improved traffic flow and reduced congestion in simulations.

Effective long-term planning considering layout adjustments.

Enhanced multi-modal transportation coordination.

Abstract

Recently, the traffic congestion in modern cities has become a growing worry for the residents. As presented in Baidu traffic report, the commuting stress index has reached surprising 1.973 in Beijing during rush hours, which results in longer trip time and increased vehicular queueing. Previous works have demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing systems and optimized bus transportation, the traffic efficiency could be significantly improved with little resource consumption. However, there are still two disadvantages that restrict their performance: (1) they only consider single scheduling in a short time, but ignoring the layout after first reposition, and (2) they only focus on the single transport. However, the multi-modal characteristics of urban public transportation are largely under-exploited. In this paper, we propose an efficient and economical multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal prediction, which adopts reinforcement learning to obtain optimal long-term and joint schedule. In JLRLS, we combines multiple transportation to conduct scheduling by their own characteristics, which potentially helps the system to reach the optimal performance. Our implementation of an example by PaddlePaddle is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling, with an explaining video at https://youtu.be/t5M2wVPhTyk.