The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traffic management systems, particularly through adaptive traffic signal control, offers a promising solution. This paper explores the use of Reinforcement Learning (RL) to enhance traffic signal operations at intersections, aiming to reduce congestion without extensive sensor networks. We introduce two RL-based algorithms: a turn-based agent, which dynamically prioritizes traffic signals based on real-time queue lengths, and a time-based agent, which adjusts signal phase durations according to traffic conditions while following a fixed phase cycle. By representing the state as a scalar queue length, our approach simplifies the learning process and lowers deployment costs. The algorithms were tested in four distinct traffic scenarios using seven evaluation metrics to comprehensively assess performance. Simulation results demonstrate that both RL algorithms significantly outperform conventional traffic signal control systems, highlighting their potential to improve urban traffic flow efficiently.
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
Thales Air Systems（泰雷兹空中系统公司）；Thales Research & Technology（泰雷兹研究与技术公司）；INRIA-Saclay Ile-de-France（法国国家信息与自动化研究所萨克雷-法兰西岛分部）；Université Paris-Sud（巴黎第十一大学）
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach
随着计算机视觉的发展，交通视频数据已成为制约交通拥堵状况的关键因素。这项工作提出了一种独特的技术，交通视频分类使用颜色编码方案之前，在深卷积神经网络的交通数据。首先，将视频数据转换为图像数据集;然后，使用You Only Look Once算法执行车辆检测。采用颜色编码方案将图像数据集转换为二值图像数据集。这些二进制图像被馈送到深度卷积神经网络。使用UCSD数据集，我们获得了98.2%的分类准确率。
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
National Science Foundation（国家科学基金会）；U.S. Department of Transportation（美国交通部）；University Transportation Center (UTC)（大学交通中心）；Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)（交通网络安全高级研究与教育中心）；NVIDIA Academic Grant Program（NVIDIA学术资助计划）
