基于iTransformer的高速公路通行费精准预测模型  

A precise highway toll prediction model based on iTransformer

作　　者：王恒昆 谷金 宋之凡 王江锋[3] WANG Hengkun;GU Jin;SONG Zhifan;WANG Jiangfeng(Shandong Traffic Research Institute,Jinan 250002,China;Shandong Provincial Communications Planning and Design Institute Group Co.,Ltd.,Jinan 250002,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]山东省交通科学研究院,山东济南250002 [2]山东省交通规划设计院集团有限公司,山东济南250002 [3]北京交通大学交通运输学院,北京100044

出　　处：《山东科学》2025年第1期120-128,共9页Shandong Science

基　　金：国家重点基础研究发展计划(2022YFB4300404);河北省中央引导地方科技发展资金项目(236Z0802G)。

摘　　要：高速公路通行费受到节假日、突发事件等复杂因素影响,传统预测方法在处理这些问题时,往往无法充分考虑多维因素之间的复杂交互作用,导致预测精度难以达到理想水平。大型语言模型利用自注意力机制能够实现对复杂时空数据的拟合,并具有更强的特征学习能力,可有效解决高速公路通行费的精准预测问题。利用此特性提出了一种基于iTransformer的高速公路通行费精准预测模型,该预测模型将时间信息作为独立维度嵌入输入序列中,并倒置了自注意力机制与前馈网络的职责,使得预测模型能够更准确地捕捉时间序列的动态特征和多变量之间的相关性。实例分析结果显示,所提出的预测模型在普通场景下较SARIMA模型和LSTM模型平均预测精度分别提高23.47%和17.84%。特殊场景下具有更优预测效果,分别提升70.92%和45.64%。针对所提出预测模型进行敏感性分析,模型对前馈网络层数和编码器堆叠层数较为敏感,对注意力头数变化不敏感。该研究为解决复杂交通环境下的通行费预测问题提供了新的方法论支持,对提高高速公路通行费预测精度具有重要意义。The prediction of highway tolls is affected by complex factors such as holidays and unexpected events.Traditional prediction methods often fail to fully account for intricate interactions between these multiple factors,resulting in less-than-ideal prediction accuracy.By leveraging the self-attention mechanism,large language models can better fit complex spatiotemporal data and have enhanced feature learning capabilities,making them highly effective for precise highway toll prediction.Therefore,this study proposes a highway toll prediction model based on iTransformer.This model embeds temporal information as an independent dimension into the input sequence and reverses the roles of the self-attention mechanism and feed-forward network,thereby allowing the model to more accurately capture the dynamic features of time series and correlations between multiple variables.Case studies show that the proposed model improves the average prediction accuracy by 23.47%and 17.84%compared with the SARIMA and LSTM models,respectively,in regular scenarios.In irregular scenarios,the model demonstrates even better predictive performance,improving the accuracy by 70.92%and 45.64%,respectively.A sensitivity analysis of the proposed model indicates that it is highly sensitive to the number of feed-forward network layers and stacked encoder layers but is less sensitive to changes in the number of attention heads.Thus,this study provides a new methodological approach for addressing the challenges associated with toll prediction in complex traffic environments and has significant implications in terms of improving the accuracy of highway toll predictions.

关 键 词：大语言模型 预测模型 自注意力机制 通行费 

分 类 号：U491.122[交通运输工程—交通运输规划与管理]