Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to adapt to dynamic traffic demand and provide limited decision interpretability. This study proposes an LLM-augmented traffic signal control framework that integrates LSTM-based short-term traffic state prediction, predictive phase selection, structured large language model reasoning, and safety-constrained action filtering. The LSTM module forecasts future queue length, waiting time, vehicle count, and lane occupancy based on recent intersection-level observations. A predictive controller then generates candidate signal actions, while the LLM module evaluates these actions using structured traffic-state inputs and produces congestion diagnoses, phase adjustment recommendations, and natural-language explanations. To ensure operational reliability, all LLM-generated recommendations are validated by a safety filter before execution. Simulation-based experiments in SUMO compare the proposed method with fixed-time control, rule-based control, and an LSTM-based predictive baseline under balanced demand, directional peak demand, and sudden surge scenarios. The results indicate that the proposed framework improves traffic efficiency, especially under dynamic and non-recurrent traffic conditions, while maintaining zero constraint violations after safety filtering. Overall, this study demonstrates that LLMs can enhance traffic signal control when used as constrained reasoning and decision-support modules rather than direct low-level controllers. Keywords: Intelligent Transportation Systems; Traffic Signal Control; Large Language Models; LSTM; Traffic State Prediction; Decision Support; Safety-Constrained Control; SUMO Simulation.
Open-TI: Open Traffic Intelligence with Augmented Language Model
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Sustainable Engineering and the Built Environment, Arizona State University（亚利桑那州立大学可持续工程与建筑环境学院）
Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；ICNLab, Shenzhen Graduate School, Peking University（北京大学深圳研究生院ICN实验室）
School of Computer Science and Technology（计算机科学与技术学院）；Taiyuan Normal University（太原师范学院）；Institute of Computer Science（计算机科学研究所）；University of Göttingen（哥廷根大学）；James Watt School of Engineering（詹姆斯·瓦特工程学院）；University of Glasgow（格拉斯哥大学）；Laboratory of Intelligent Home Appliances（智能家电实验室）；College of Science and Technology（科学与技术学院）；Ningbo University（宁波大学）
Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing
Aix-Marseille University（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；LIS（信息科学与系统实验室）；Université Libre de Bruxelles（布鲁塞尔自由大学）；WEL Research Institute（WEL 研究院）；University of Leeds（利兹大学）；School of Computer Science（计算机学院）
Indian Stock Market Prediction using Augmented Financial Intelligence ML
University College London（伦敦大学学院）；National Institute of Technology Patna（巴特那国立理工学院）；SRM University AP（SRM大学安得拉邦校区）；Amirkabir University of Technology, Tehran Polytechnic（阿米尔卡比尔理工大学（德黑兰理工学院））；IIT Kanpur（印度理工学院坎普尔分校）
Automatic driving lane change safety prediction model based on LSTM
Trine University（特莱恩大学）；Yantai University（烟台大学）；University of Electronic Science and Technology of China（电子科技大学）；Northern Arizona University（北亚利桑那大学）；University of Aberdeen（阿伯丁大学）
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shanghai AI Laboratory（上海人工智能实验室）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；SenseTime Group Limited（商汤科技）
针对强化学习交通信号控制需按交叉口结构重新训练的问题，提出 ADLight，通过统一智能体设计与 movement shuffle 数据增强训练通用模型，在 SUMO 中以约5%等待时间代价节省逾80%训练时间。
User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality
School of Telecommunications Engineering, Xidian University（电子科技大学电信工程学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）；School of Information Technology, Carleton University（卡尔顿大学信息科技学院）
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching
City University of Hong Kong（香港城市大学）；Jilin University（吉林大学）；Zhejiang University of Technology（浙江工业大学）；Southern University of Science and Technology（南方科技大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Alibaba Group（阿里巴巴集团）
