The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS. Recent advancements in Large Language Models (LLMs) such as GPT-4 highlight their broad general knowledge, reasoning capabilities, and commonsense priors across various domains. In this work, we conduct a large-scale user study involving 70 participants to investigate whether novices can leverage ChatGPT to solve complex mixed traffic control problems. Three environments are tested, including ring road, bottleneck, and intersection. We find ChatGPT has mixed results. For intersection and bottleneck, ChatGPT increases number of successful policies by 150% and 136% compared to solely beginner capabilities, with some of them even outperforming experts. However, ChatGPT does not provide consistent improvements across all scenarios.
The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven ones and the whole traffic system in different ways, for example by improving collision avoidance and reducing traffic waves. Many studies have been carried out to improve intersection management, a significant bottleneck in traffic, with intelligent traffic signals or exclusively automated vehicles. However, the problem of how to improve mixed tra…
Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians. The driving policy should make safe decisions to handle the dynamic traffic conditions and meet the requirements of on-board computation. However, most of the current researches focuses on simplified intersections considering only the surrounding vehicles and idealized traffic lights. This paper improves the integrated decision and control framework and develops a learning-based algorithm to deal with com…
We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks with through-traffic-only two-way and four-way intersections. Our method utilizes multi-agent policy decomposition which allows decentralized control based on local observations for an arbitrary number of controlled vehicles. We demonstrate that, even without reward shaping, reinforcement learning learns to coordinate the vehicles to exhibit traffic signal-like behaviors, achieving near-optimal throughput with 33-50% controlled vehicles. With the help of multi-task learnin…
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
Department of Civil and Coastal Engineering at University of Florida（佛罗里达大学土木与海岸工程系）；Department of Computer Science at University of Memphis（密苏里大学计算机科学系）；Min H. Kao Department of Electrical Engineering and Computer Science at University of Tennessee（田纳西大学电子工程与计算机科学系）
Stanford University（斯坦福大学）；UC Santa Barbara（加州大学圣巴巴拉分校）
Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model…
Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control
School of Transportation, Southeast University, Nanjing, China, 211189（东南大学交通学院）；Jiangsu Key Laboratory of Urban ITS, Nanjing, China, 210096（江苏省城市智能交通重点实验室）；Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, China, 210096（江苏省现代城市交通技术协同创新中心）；Department of Civil Engineering, Institute of Transport Studies, Monash University, Melbourne, Australia, VIC 3800（莫纳什大学土木工程系）；College of Transportation Engineering, Tongji University, Shanghai, PR China, 201804（同济大学交通工程学院）
