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

被引:0
作者
Sun, Bin [1 ]
Wang, Yinuo [1 ]
Shen, Tao [1 ]
Zhang, Lu [1 ]
Geng, Renkang [2 ]
机构
[1] Univ Jinan, Sch Elect Engn, Jinan 250022, Peoples R China
[2] Shandong High Speed Info Grp Co LTD, Jinan 250100, Peoples R China
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2025年 / 82卷 / 03期
关键词
Machine learning; deep learning; traffic time series prediction; forecasting; aggregation; INTELLIGENT TRANSPORTATION SYSTEMS; FLOW PREDICTION; SPECIAL-ISSUE; VEHICLES; INTERNET;
D O I
10.32604/cmc.2025.060567
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:4219 / 4236
页数:18
相关论文
共 41 条
[1]   Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data [J].
Abadi, Afshin ;
Rajabioun, Tooraj ;
Ioannou, Petros A. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (02) :653-662
[2]  
Al Sahili Z, 2023, Arxiv, DOI arXiv:2301.10569
[3]   Self-driving cars: A survey [J].
Badue, Claudine ;
Guidolini, Ranik ;
Carneiro, Raphael Vivacqua ;
Azevedo, Pedro ;
Cardoso, Vinicius B. ;
Forechi, Avelino ;
Jesus, Luan ;
Berriel, Rodrigo ;
Paixao, Thiago M. ;
Mutz, Filipe ;
Veronese, Lucas de Paula ;
Oliveira-Santos, Thiago ;
De Souza, Alberto F. .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 165
[4]   Special Issue on Internet of Things for Connected Automated Driving [J].
Cao, Dongpu ;
Li, Li ;
Marina, Clara ;
Chen, Long ;
Xing, Yang ;
Zhuang, Weihua .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (05) :3678-3680
[5]   Spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism [J].
Chen, Zhijun ;
Lu, Zhe ;
Chen, Qiushi ;
Zhong, Hongliang ;
Zhang, Yishi ;
Xue, Jie ;
Wu, Chaozhong .
INFORMATION SCIENCES, 2022, 611 :522-539
[6]   Societal Intelligence for Safer and Smarter Transportation [J].
Cheng, Xiang ;
Duan, Dongliang ;
Yang, Liuqing ;
Zheng, Nanning .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (11) :9109-9121
[7]   Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation [J].
Fukuda, Shota ;
Uchida, Hideaki ;
Fujii, Hideki ;
Yamada, Tomonori .
IET INTELLIGENT TRANSPORT SYSTEMS, 2020, 14 (08) :936-946
[8]   A Review of Motion Planning Techniques for Automated Vehicles [J].
Gonzalez, David ;
Perez, Joshue ;
Milanes, Vicente ;
Nashashibi, Fawzi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (04) :1135-1145
[9]   Recent advances in convolutional neural networks [J].
Gu, Jiuxiang ;
Wang, Zhenhua ;
Kuen, Jason ;
Ma, Lianyang ;
Shahroudy, Amir ;
Shuai, Bing ;
Liu, Ting ;
Wang, Xingxing ;
Wang, Gang ;
Cai, Jianfei ;
Chen, Tsuhan .
PATTERN RECOGNITION, 2018, 77 :354-377
[10]   Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables [J].
Hengl, Tomislav ;
Nussbaum, Madlene ;
Wright, Marvin N. ;
Heuvelink, Gerard B. M. ;
Graeler, Benedikt .
PEERJ, 2018, 6