Engineering Technology and Industrial Distribution Department, Texas A&M University, College Station, TX 77843, USA.
Sensors (Basel). 2023 May 24;23(11):5028. doi: 10.3390/s23115028.
In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestrian and vehicle information. Machine learning algorithms, including convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM), are demonstrated to predict traffic conditions and traffic light timings. To validate the proposed method, the Simulation of Urban Mobility (SUMO) platform was used to simulate the real-world intersection working. The simulation result indicates the dynamic traffic interval technique is more efficient and showcases a 12% to 27% reduction in the waiting time of vehicles and a 9% to 23% reduction in the waiting time of pedestrians at an intersection when compared to the fixed time and semi-dynamic traffic light control methods.
近年来，研究人员提出了智能交通信号灯控制系统，以改善交叉口的交通流量，但较少关注同时减少车辆和行人的延误。本研究提出了一种利用交通检测摄像机、机器学习算法和梯形逻辑程序的智能交通信号灯控制的网络物理系统。该方法采用了一种动态交通间隔技术，将交通分为低、中、高和非常高的流量。它根据实时交通数据，包括行人和车辆信息，调整交通信号灯间隔。包括卷积神经网络 (CNN)、人工神经网络 (ANN) 和支持向量机 (SVM) 在内的机器学习算法被用于预测交通状况和交通信号灯定时。为了验证所提出的方法，使用了城市交通模拟 (SUMO) 平台来模拟真实世界的交叉口工作。模拟结果表明，与固定时间和半动态交通信号灯控制方法相比，动态交通间隔技术在交叉口处的车辆等待时间减少了 12% 到 27%，行人等待时间减少了 9% 到 23%，更加高效。
