基于GAT-LSTM模型的高速公路站点流量预测方法

doi:10.3969/j.issn.0255-8297.2024.03.014

应用科学学报 ›› 2024, Vol. 42 ›› Issue (3): 540-548.doi: 10.3969/j.issn.0255-8297.2024.03.014

• 计算机科学与应用 • 上一篇

基于GAT-LSTM模型的高速公路站点流量预测方法

刘圣卿¹, 马飞虎²

1. 江西省交通监控指挥中心, 江西南昌 330036;
2. 华东交通大学交通运输工程学院, 江西南昌 330013

收稿日期:2023-02-15 发布日期:2024-06-06
通信作者: 马飞虎，副教授，研究方向为智能交通。E-mail: mfh3@163.com E-mail:mfh3@163.com
基金资助:
国家重点研发计划项目（No. 2021YFE0105600）；国家自然科学基金（No. 51978263）；江西省自然科学重点基金（No. 20192ACBL20008）资助

Traffic Flow Prediction Method of Highway Toll Station Based on GAT-LSTM Model

LIU Shengqing¹, MA Feihu²

1. Jiangxi Traffic Monitoring Command Center, Nanchang 330036, Jiangxi, China;
2. College of Transportation Engineering, East China JiaoTong University, Nanchang 330013, Jiangxi, China

Received:2023-02-15 Published:2024-06-06

摘要/Abstract

摘要： 为了实现对高速公路站点流量的准确预测，提出了一种利用组合模型捕捉高速公路收费站流量时空特征以提高交通流量预测精度的方法，首先对高速公路收费数据进行挖掘，得到交通流量时空数据集；其次通过分析其时空特征，揭示高速公路收费站之间流量的时空演化规律及关联机制；最后结合深度学习模型预测高速公路交通流量。以江西省九江主线收费站为实验对象，收集了2021年5月1日至2021年12月31日的收费数据进行处理，将提取特征后的流量时空数据作为模型输入，得到出口流量分析预测结果。利用平均绝对误差、均方根误差和平均绝对百分比误差3项指标对模型预测效果进行评价，结果显示该模型能够利用时空特征有效提高流量的预测精度，与单一模型相比具有更好的预测性能。

关键词: 站点流量预测, 图注意力网络, 长短时记忆网络, 时空特性

Abstract: To achieve accurate prediction of highway station traffic flow, this paper proposes a research method that utilizes a combined model to capture the spatio-temporal characteristics of highway toll station traffic. The basic idea is to mine the toll data to obtain a spatio-temporal dataset of traffic flow, analyze its spatio-temporal characteristics, reveal the spatio-temporal evolution rules and correlation mechanisms between the traffic flow of highway toll stations. Subsequently, we combine these insights with deep learning models to predict highway traffic flow. As a case study, we focus on the main toll station in Jiujiang, Jiangxi Province, utilizing the toll data from May 1, 2021, to December 31, 2021. The extracted spatio-temporal traffic data serves as input for our model, yielding analysis and prediction results of outlet flow. The prediction performance of the model is evaluated through three indicators: mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results show that the proposed model effectively improves the prediction accuracy by utilizing spatio-temporal characteristics, outperforming single models in predictive capability.

Key words: toll station traffic prediction, graph attention networks, long short-term neural network, spatio-temporal characteristics

中图分类号:

U491.1

刘圣卿, 马飞虎. 基于GAT-LSTM模型的高速公路站点流量预测方法[J]. 应用科学学报, 2024, 42(3): 540-548.

LIU Shengqing, MA Feihu. Traffic Flow Prediction Method of Highway Toll Station Based on GAT-LSTM Model[J]. Journal of Applied Sciences, 2024, 42(3): 540-548.

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基于GAT-LSTM模型的高速公路站点流量预测方法

Traffic Flow Prediction Method of Highway Toll Station Based on GAT-LSTM Model

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