整合预测、信号自适应与消息通信，提升城市交通效率。
Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging
- 结合实时数据预测车流，动态调整信号灯时长。
- 仿真显示平均等待时间显著降低，能应对突发状况。
- 适合智能交通系统研究者与城市规划人员参考。
本文提出一种集成化城市交通优化方法，融合车辆流量预测、自适应信号控制与基于分布式消息的模块化架构。利用各类传感器的实时数据，系统可预判交通波动，并通过模拟退火与强化学习算法驱动的策略，动态调节信号灯相位时长，以最小化延误并改善交通流。该主动调控机制还能提升能源效率、减少污染物排放，并有效应对恶劣天气、交通事故或临时聚集等突发事件。在真实城市环境中的初步仿真表明，平均等待时间显著下降。未来工作将引入联网车辆数据，整合新型交通方式，并持续优化预测模型以应对日益复杂的都市出行挑战。
原文摘要 · Abstract (English)
This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time data from various sensors, the system anticipates traffic fluctuations and dynamically adjusts signal phase durations to minimize delays and improve traffic flow. This proactive adjustment, supported by algorithms inspired by simulated annealing and reinforcement learning, also enhances energy efficiency, reduces pollutant emissions, and responds effectively to unexpected events (adverse weather, accidents, or temporary gatherings). Preliminary simulations conducted in a realistic urban environment demonstrate a significant reduction in average waiting times. Future developments include incorporating data from connected vehicles, integrating new modes of transport, and continuously refining predictive models to address the growing challenges of urban mobility.
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