吉林大学学报(信息科学版) ›› 2026, Vol. 44 ›› Issue (1): 185-191.

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物联网技术下节假日公路交通拥堵状况预测算法

陈 艳   

  1. 1. 中国刑事警察学院 侦查系, 沈阳 110854; 2. 青海警官职业学院 公安系, 青海 西宁 810001
  • 收稿日期:2024-07-03 出版日期:2026-01-31 发布日期:2026-02-04
  • 作者简介:陈艳(1976— ), 女, 陕西武功人, 中国刑事警察学院副教授, 主要从事公安行政法学, 公安交通管理, 道路交通事故 处理研究, (Tel)86-13897115828(E-mail)chenyan19763@ 163. com
  • 基金资助:
    青海省自然科学基金资助项目(2023-ZJ-989Q)

Prediction Algorithm for Holiday Road Traffic Congestion under IoT Technology 

CHEN Yan    

  1. 1. Investigation Department, China Criminal Police Academy, Shenyang 110854, China; 2. Public Security Department, Qinghai Vocational College of Police Officers, Xining 810001, China
  • Received:2024-07-03 Online:2026-01-31 Published:2026-02-04

摘要: 针对在节假日期间公路交通压力增加, 直接应用采集的交通数据存在数据缺失和错误的缺陷, 进而影响 预测准确性的问题, 提出物联网技术下节假日公路交通拥堵状况预测算法。 该算法通过物联网技术, 实时采集 节假日公路交通数据, 并进行相关性分析以及组建相关路口集; 结合流量-占有率模型和径向基函数神经网络 模型, 修复交通流量, 并通过 K-means 聚类分析法对各个路段进行划分; 通过构建路网数据的压缩矩阵, 并 利用路网原始数据矩阵之间的映射关系, 获取节假日整个路网各个路段的交通预测结果。 实验结果表明, 所提 算法的效率系数(CE: Coefficient of Efficiency)取值趋近 1, 更贴近实际观测值。 证明所提算法在公路交通拥堵 状况预测方面具有良好的性能, 可以有效实现节假日公路交通拥堵状况预测。

关键词: 物联网技术, 节假日, 公路, 交通拥堵状况, 预测

Abstract: During holidays, the highway traffic pressure increases, and the traffic data collected directly is prone to the defects of data loss and error, which further affects the accuracy of prediction. Therefore, the prediction algorithm of holiday highway traffic congestion under the Internet of Things technology is proposed. The algorithm collects holiday road traffic data in real time through the Internet of Things technology, and carries out correlation analysis and sets up relevant intersection sets. Combining the traffic-occupancy model and radial basis function neural network model, the traffic flow is repaired, and each road section is divided by K-means clustering analysis. By constructing the compression matrix of road network data, the mapping relationship between the original data matrices of road network is realized, and the traffic prediction results of all sections of the whole road network during holidays are obtained. The experimental results show that the CE(Coefficient of Efficiency) of the proposed algorithm is significantly close to 1, which is closer to the actual observation value. It proves that the proposed algorithm has good performance in highway traffic congestion prediction and can effectively realize highway traffic congestion prediction on holidays. 

Key words: internet of things technology, holidays, highways, traffic congestion situation, forecast

中图分类号: 

  • TP399