Avoiding congestion and controlling traffic in urban scenarios is becoming nowadays of paramount importance due to the rapid growth of our cities' population and vehicles. The effective control of urban traffic as a means to mitigate congestion can be beneficial in an economic, environmental and health way. In this paper, a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles is presented. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. Taking advantage of the ability to specify optimization constraints in ASP and the off-the-shelf solver Clingo, it is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics (e.g., travel times or emissions). Finally, an analysis on real-world traffic data is performed, utilizing the state-of-the-art Urban Mobility Simulator (SUMO) to keep track of the state of the network, test the correctness of the solution and to prove the efficiency and capabilities of the presented solution.
Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios
University of Science and Technology of China（中国科学技术大学）；Suzhou Shuzhi Technology Group Co., Ltd.（苏州数智科技集团有限公司）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
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）
