摘 要
随着物联网技术的快速发展，基于物联网技术的轨道交通信号灯联网优化研究成为众多学者关注的热点。本文针对轨道交通信号灯联网的不足，提出了一种基于车辆行驶信息的信号灯联网协同优化算法。该算法通过综合采集车辆位置、速度等信息，实现了车流量预测和信号灯动态调控。同时，基于现场实际数据，使用数据可视化展示技术对优化效果进行了分析和验证。通过模拟实验，本文算法表现出优秀的优化效果，提升了轨道交通信号灯联网的效率和安全性，并为今后轨道交通信号灯优化研究提供了借鉴。本论文的研究成果可以为城市轨道交通建设提供技术支持，降低能源消耗和车辆拥堵，进一步优化城市公共交通网络的运行效率。
关键词：物联网技术；轨道交通信号灯；车流量预测
Research on Network Optimization of rail transit signal lights based on Internet of Things technology
Abstract
With the rapid development of the Internet of Things technology, the research on the optimization of rail transit signal light network based on the Internet of Things technology has become the focus of many scholars. Aiming at the deficiency of rail transit signal lights networking, a collaborative optimization algorithm based on vehicle travel information is proposed in this paper. The algorithm can realize traffic flow prediction and dynamic control of signal light by collecting the information of vehicle position and speed. At the same time, based on the actual field data, the data visualization display technology is used to analyze and verify the optimization effect. Through simulation experiments, the algorithm in this paper shows excellent optimization effect, improves the efficiency and safety of rail transit signal light networking, and provides reference for future research on rail transit signal light optimization. The research results of this paper can provide technical support for urban rail transit construction, reduce energy consumption and vehicle congestion, and further optimize the operating efficiency of urban public transport network.
Key words: Internet of Things technology; Rail traffic light; Traffic flow prediction
目 录
1 前言 1
2 物联网技术概述 2
2.1 物联网技术的概念与发展历程 2
2.2 物联网技术在轨道交通领域中的应用 3
2.3 物联网技术的发展前景与趋势 3
3 轨道交通信号灯联网优化研究现状 5
3.1 基于物联网技术的轨道交通信号灯联网现有研究成果 5
3.2 车辆信号预测技术及其在信号优化中的应用 5
3.3 信号优化算法研究现状 6
4 轨道交通信号灯联网的现有问题分析 8
4.1 车辆行驶信息不精准、不全面问题 8
4.2 信号灯联网协同不良问题 8
4.3 信号灯优化策略不科学问题 9
5 轨道交通信号灯联网优化算法策略研究 10
4.1 轨道交通信号灯联网优化算法设计 10
4.2 车流量预测算法研究与数据预处理 11
4.3 数据可视化展示技术分析与实现 12
结论 14
参考文献 16
致谢 17
随着物联网技术的快速发展，基于物联网技术的轨道交通信号灯联网优化研究成为众多学者关注的热点。本文针对轨道交通信号灯联网的不足，提出了一种基于车辆行驶信息的信号灯联网协同优化算法。该算法通过综合采集车辆位置、速度等信息，实现了车流量预测和信号灯动态调控。同时，基于现场实际数据，使用数据可视化展示技术对优化效果进行了分析和验证。通过模拟实验，本文算法表现出优秀的优化效果，提升了轨道交通信号灯联网的效率和安全性，并为今后轨道交通信号灯优化研究提供了借鉴。本论文的研究成果可以为城市轨道交通建设提供技术支持，降低能源消耗和车辆拥堵，进一步优化城市公共交通网络的运行效率。
关键词：物联网技术；轨道交通信号灯；车流量预测
Research on Network Optimization of rail transit signal lights based on Internet of Things technology
Abstract
With the rapid development of the Internet of Things technology, the research on the optimization of rail transit signal light network based on the Internet of Things technology has become the focus of many scholars. Aiming at the deficiency of rail transit signal lights networking, a collaborative optimization algorithm based on vehicle travel information is proposed in this paper. The algorithm can realize traffic flow prediction and dynamic control of signal light by collecting the information of vehicle position and speed. At the same time, based on the actual field data, the data visualization display technology is used to analyze and verify the optimization effect. Through simulation experiments, the algorithm in this paper shows excellent optimization effect, improves the efficiency and safety of rail transit signal light networking, and provides reference for future research on rail transit signal light optimization. The research results of this paper can provide technical support for urban rail transit construction, reduce energy consumption and vehicle congestion, and further optimize the operating efficiency of urban public transport network.
Key words: Internet of Things technology; Rail traffic light; Traffic flow prediction
目 录
1 前言 1
2 物联网技术概述 2
2.1 物联网技术的概念与发展历程 2
2.2 物联网技术在轨道交通领域中的应用 3
2.3 物联网技术的发展前景与趋势 3
3 轨道交通信号灯联网优化研究现状 5
3.1 基于物联网技术的轨道交通信号灯联网现有研究成果 5
3.2 车辆信号预测技术及其在信号优化中的应用 5
3.3 信号优化算法研究现状 6
4 轨道交通信号灯联网的现有问题分析 8
4.1 车辆行驶信息不精准、不全面问题 8
4.2 信号灯联网协同不良问题 8
4.3 信号灯优化策略不科学问题 9
5 轨道交通信号灯联网优化算法策略研究 10
4.1 轨道交通信号灯联网优化算法设计 10
4.2 车流量预测算法研究与数据预处理 11
4.3 数据可视化展示技术分析与实现 12
结论 14
参考文献 16
致谢 17
