摘要
利用2019年、2022年与2023年春节期间高速公路收费站出入口数据,文章采用LSTM和CNN神经网络组合模型分析了2024年春节期间陕西高速公路收费站的日出口流量。在春节假期结束后,将预测数据与实际出口流量进行比对,研究了春节期间的流量增长情况。结果表明,假期交通流量预测模型的误差控制在2%以内,对长假期间高速公路的交通安全具有积极意义。
Using historical toll station entrance and exit data from 2019,2022,and 2023 during the Spring Festival,this article uses a combination model of LSTM and CNN neural networks to analyze the daily exit traffic of toll stations during the 2024 Spring Festival.After the end of the Spring Festival holiday,the predicted data was compared with the actual export flow to study the traffic growth during the Spring Festival period in recent years.The results indicate that the error control of the holiday traffic flow prediction model is within 2%,which has a positive significance for the traffic safety of highways during the long holiday period.
作者
南争伟
NAN Zhengwei(Shaanxi Provincial Highway Bureau,Xi'an 710068,China)
出处
《计算机应用文摘》
2024年第11期126-128,共3页
Chinese Journal of Computer Application
关键词
长假
高速公路
交通流量
流量预测
long vacation
highway
traffic flow
traffic prediction