基于PSO-LSTM的短时交通流量预测网站设计
Design of a Short Term Traffic Flow Prediction Website Based on PSO-LSTM
王宁 1成利敏 1甄景涛 1段晓霞1
作者信息
摘要
短时交通流量预测是智能交通系统中的重要环节,选用在短时交通流量预测方面表现出色的LSTM神经网络,并利用PSO算法优化LSTM神经网络模型.实验结果表明,与传统LSTM模型相比,所构建的PSO-LSTM模型对未来5分钟和10分钟两种短时交通流量预测,达到了更高的准确率.在此基础上,设计了一个交通流量预测网站更好地展示了预测结果,也方便用户随时查询.
Abstract
Short term traffic flow prediction is an important part of intelligent transportation systems.This article selects the LSTM neural network that performs well in short-term traffic flow prediction,and uses the PSO algorithm to optimize the LSTM neural network model.Experiments have shown that the PSO-LSTM model constructed in this paper achieves higher accuracy in predicting short-term traffic flow for the next 5 minutes and the next 10 minutes compared with traditional LSTM models.On this basis,in order to better display the prediction results and facilitate users to query at any time,the author spe-cifically designed a city traffic flow prediction website,which has certain practical application value.
关键词
智能交通系统/短时交通流量预测/LSTM神经网络/PSO算法/交通流量预测网站Key words
intelligent transportation system/short term traffic flow prediction/LSTM neural network/PSO algorithm/traf-fic flow prediction website引用本文复制引用
基金项目
廊坊市科学技术研究与发展计划自筹经费项目(第一批)(2020)(2020011009)
出版年
2024
NETL
NSTL
万方数据