Heuristic Feature Engineering for Enhancing Neural Network Performance in Spatiotemporal Traffic Prediction  

作　　者：Bin Sun Yinuo Wang Tao Shen Lu Zhang Renkang Geng 

机构地区：[1]School of Electronic Engineering,University of Jinan,Jinan,250022,China [2]ShandongHigh-Speed Info.Group Co.Ltd.,Jinan,250100,China

出　　处：《Computers, Materials & Continua》2025年第3期4219-4236,共18页计算机、材料和连续体(英文)

基　　金：supported by the Shandong Province Higher Education Young Innovative Talents Cultivation Programme Project:TJY2114;Jinan City-School Integration Development Strategy Project:JNSX2023015;the Natural Science Foundation of Shandong Province:ZR2021M F074.

摘　　要：Traffic datasets exhibit complex spatiotemporal characteristics,including significant fluctuations in traffic volume and intricate periodical patterns,which pose substantial challenges for the accurate forecasting and effective management of traffic conditions.Traditional forecasting models often struggle to adequately capture these complexities,leading to suboptimal predictive performance.While neural networks excel at modeling intricate and nonlinear data structures,they are also highly susceptible to overfitting,resulting in inefficient use of computational resources and decreased model generalization.This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks to enhance the identification and representation of relevant features from traffic data.We begin by evaluating the significance of various temporal characteristics using three distinct assessment strategies grounded in non-neural methodologies.These evaluated features are then aggregated through a weighted fusion mechanism to create heuristic features,which are subsequently integrated into neural network models for more accurate and robust traffic prediction.Experimental results derived from four real-world datasets,collected from diverse urban environments,show that the proposed method significantly improves the accuracy of long-term traffic forecasting without compromising performance.Additionally,the approach helps streamline neural network architectures,leading to a considerable reduction in computational overhead.By addressing both prediction accuracy and computational efficiency,this study not only presents an innovative and effective method for traffic condition forecasting but also offers valuable insights that can inform the future development of data-driven traffic management systems and transportation strategies.

关 键 词：Machine learning deep learning traffic time series prediction forecasting AGGREGATION 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]