摘要
随着城市化的推进,城市的交通状况也是越发拥挤,如何对车流人流进行调度管理成为亟待解决的问题。通过设计一种高效智能的交通控制系统可以大大减轻交通堵塞的问题。然而,现阶段大多数路段的红绿灯仍是基于特定时间表进行的定时控制,这种控制方式在车流量较低时效果较好,但车流量较大或交通情况变化却无法满足需求。因此,文章设计了一种智能交通控制系统,该系统通过采集交通流量信息,按照BP神经网络控制算法测算路口车流量与通过时间的对应关系,通过系统自主学习训练完善算法模型,从而达到智能调节交通流量的目的。该系统设计了一种基于BP神经网络智能交通的控制系统模型,对各类交通工具通过时间进行预测。通过这种设计可以实现交通灯控制系统更加智能化,具有一定的经济效益和社会效益。
With the advancement of urbanization,the traffic situation in cities has become increasingly congested,and how to dispatch and manage the traffic flow has become an urgent problem to be solved.By designing an efficient and intelligent traffic control system,the problem of traffic congestion can be greatly reduced.However,at present,the traffic lights on most road sections are still timed based on specific schedules.This control method works well when the traffic flow is low,but it cannot meet the demand due to large traffic flow or changes in traffic conditions.Therefore,this article designs an intelligent traffic control system that collects traffic flow information,calculates the corresponding relationship between intersection traffic flow and passing time using the BP neural network control algorithm,and improves the algorithm model through independent learning and training of the system,thereby achieving the goal of intelligent adjustment of traffic flow.This system designs a control system model based on BP neural network for intelligent transportation,which predicts the passage time of various types of transportation vehicles.Through this design,the traffic light control system can be more intelligent,with certain economic and social benefits.
作者
孙中廷
SUN Zhong-ting(Jiangsu College of Safety Technology,Xuzhou 221011,China)
出处
《电脑与信息技术》
2023年第6期1-4,共4页
Computer and Information Technology
基金
新疆维吾尔自治区重点研发任务专项课题“煤火温度气体远程监测及态势研判关键技术研究”(项目编号:2022B03003-3)。
关键词
神经网络
智能交通系统
图像采集
图像算法
neural network
intelligent transportation system
Image acquisition
Image algorithm
