Open Access

ARTICLE

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

Bin Sun¹, Yinuo Wang¹, Tao Shen^1,*, Lu Zhang¹, Renkang Geng²

1 School of Electronic Engineering, University of Jinan, Jinan, 250022, China
2 Shandong High-Speed Info. Group Co. Ltd., Jinan, 250100, China

* Corresponding Author: Tao Shen. Email:

Computers, Materials & Continua 2025, 82(3), 4219-4236. https://doi.org/10.32604/cmc.2025.060567

Received 04 November 2024; Accepted 29 November 2024; Issue published 06 March 2025

Abstract

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.

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Keywords

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