相关论文: An FPGA-Based Semi-Automated Traffic Control Syste…

In this work we study the effect of a traffic light system on the flow of a single lane road by proposing a traffic model based on a cellular automaton that also includes behavioral considerations. We focus on the macroscopic…

Existing traffic control systems only possess a local perspective over the multiple scales of traffic evolution, namely the intersection level, the corridor level, and the region level respectively. But luckily, despite its complex…

A typical urban signalized intersection poses significant modeling and control challenges in a mixed traffic environment consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs). In this paper, we address the…

For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic…

Today's intelligent traffic light control system is based on the current road traffic conditions for traffic regulation. However, these approaches cannot exploit the future traffic information in advance. In this paper, we propose GPlight,…

Intelligent traffic signal controllers, applying DQN algorithms to traffic light policy optimization, efficiently reduce traffic congestion by adjusting traffic signals to real-time traffic. Most propositions in the literature however…

Deep Reinforcement Learning (DRL) uses diverse, unstructured data and makes RL capable of learning complex policies in high dimensional environments. Intelligent Transportation System (ITS) based on Autonomous Vehicles (AVs) offers an…

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Congestion is intertwined with…

The goal of this work is to provide a viable solution based on reinforcement learning for traffic signal control problems. Although the state-of-the-art reinforcement learning approaches have yielded great success in a variety of domains,…

Autonomous vehicles (AVs) hold vast potential to enhance transportation systems by reducing congestion, improving safety, and lowering emissions. AV controls lead to emergent traffic phenomena; one such intriguing phenomenon is traffic…

Summary: Traffic light coordination is a complex problem. In this paper, we extend previous work on an abstract model of city traffic to allow for multiple street intersections. We test a self-organizing method in our model, showing that it…

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to…

Numerous studies have shown the effectiveness of intelligent transportation system techniques such as variable speed limit (VSL), lane change (LC) control, and ramp metering (RM) in freeway traffic flow control. The integration of these…

This paper presents the results of a new deep learning model for traffic signal control. In this model, a novel state space approach is proposed to capture the main attributes of the control environment and the underlying temporal traffic…

Congestion control algorithms are crucial in achieving high utilization while preventing overloading the network. Over the years, many different congestion control algorithms have been developed, each trying to improve in specific…

We build on our recent work on formalization of responsibility-sensitive safety (RSS) and present the first formal framework that enables mathematical proofs of the safety of control strategies in intersection scenarios. Intersection…

Optimal traffic-light settings are generally hard to obtain, certainly for actuated access control of an intersection. Typically, computationally expensive (microscopic) simulations or complicated optimization schemes are required to find…

We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is…

Control problems of mixed-autonomy traffic systems that consist of both human-driven vehicles (HV) and autonomous vehicles (AV), have gained increasing attention. This paper focuses on suppressing traffic oscillations in the mixed-autonomy…

Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically designed, with insufficient theoretical support. In this paper,…