Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction

作     者：Izadi, Mohammad Safayani, Mehran Mirzaei, Abdolreza 

作者机构：Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran 

出 版 物：《International Journal of Computers and Applications》 (Int J Comput Appl)

年 卷 期：2025年第47卷第1期

页      面：45-56页

核心收录：

学科分类：0401[教育学-教育学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：This research did not receive any specific grant from funding agencies in the public  commercial  or not-for-profit sectors 

主　　题：Cost functions 

摘      要：Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student s accuracy close to that of the teacher, even with the retention of only (Formula presented.) of network parameters. © 2024 Informa UK Limited, trading as Taylor & Francis Group.