报告题目：Real-Time Signal Control for Large Heterogeneous Traffic Networks

报告人：Prof. Rong Su

报告人单位：新加坡南洋理工大学

报告时间：2024年12月24日（周二）14:30-16:30

会议地点：电气工程与控制科学学院416报告厅（崇德楼D座416）

举办单位：电气工程与控制科学学院

报告人简介：RongSu，教授，博导，新加坡南洋理工大学电气与电子工程学院计算机控制与自动化硕士项目负责人、KTH-NTU 联合博士项目管理委员会 NTU负责人。研究方向包括多智能体系统、离散事件系统理论、基于模型的故障诊断、网络安全分析和综合、复杂网络的控制和优化，以及在柔性制造、智能交通、人机界面、电源管理和绿色建筑中的应用，拥有330多篇期刊和会议出版物、2部专著、18项已授予/申请的专利，曾获得多项最佳论文奖，包括IEEE/CAA Journal of Automatica Sinica 2021年Hsue-shen Tsien论文奖等。 目前，他担任IEEE Transactions on Cybernetics、Automatica (IFAC)、Journal of Discrete Event Dynamic Systems: Theory and Applications和Journal of Control and Decision的副主编。

报告摘要：Traffic congestion in urban areas significantly increases the commuting time for passengers and introduces unnecessary fuel burns and carbon emissions to the fragile urban ecosystem. Traffic lights, which is introduced to improve the order in traffic systems, may harm the traveling efficiency if the green times are not properly assigned for each approach. Sensors and controllers are implemented in modern intelligent transportation systems to generate traffic-responsive signal plans, which highly depends on the topological parameter estimation and traffic model based optimization. In this paper, to fulfill the requirements of constructing a V2X-enabled traffic light control scheme, a closed-loop traffic light scheduling strategy is proposed. A macroscopic model is introduced to depict the traffic movements in the network, which involves the traffic flow dynamics and the prediction of speed variations. A mixed integer linear model is elaborated to generate optimal traffic light plans. Topological parameters, such as turning ratios from each approach, are required to precisely depict the traffic movements. A learning-based parameter estimator is designed to on-line predict the turning ratios based on historical traffic data and traffic light assignments. Simulations show that the convergence is achieved under constant cyclic flow profiles, and our proposed closed-loop traffic light scheduling strategy could achieve significant reduction on key performance indices.

审核人：薄翠梅