Amit Roy
- Research Assistant, CCDS
PhD Student
Purdue University
Research Interests
Machine Learning
Conference Paper2021Cited by 4
Unified spatio-temporal modeling for traffic forecasting using graph neural network
Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali, M Ashraful Amin, AKM Mahbubur Rahman
2021 International Joint Conference on Neural Networks (IJCNN)
IEEE, pp. 1–8
Abstract
Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, most state-of-the-art approaches have designed spatial-only (e.g. Graph Neural Networks) and temporal-only (e.g. Recurrent Neural Networks) modules to separately extract spatial and temporal features. However, we argue that it is less effective to extract the complex spatiotemporal relationship with such factorized modules. Besides, most existing works predict the traffic intensity of a particular time interval only based on the traffic data of the previous one hour of that day. And thereby ignores the repetitive daily/weekly pattern that may exist in the last hour of data. Therefore, we propose a Unified Spatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that performs both spatial and temporal aggregation through direct information propagation across different timestamp nodes with the help of spectral graph convolution on a spatio-temporal graph. Furthermore, it captures historical daily patterns in previous days and current-day patterns in current-day traffic data. Finally, we validate our work's effectiveness through experimental analysis11Code is available at github.com/AmitRoy7781/USTGCN, which shows that our model USTGCN can outperform state-of-the-art performances in three popular benchmark datasets from the Performance Measurement System (PeMS). Moreover, the training time is reduced significantly with our proposed USTGCN model.
Keywords
TimestampComputer scienceGraphTemporal databaseData miningDeep learningArtificial intelligenceConvolution (computer science)Benchmark (surveying)Machine learningArtificial neural network
CCDS Authors
Kashob Kumar Roy
- Research Assistant, CCDS
PhD Student
University of Illinois Urbana-Champaign
Research Interests
Machine Learning
Amin Ahsan Ali, PhD
- Director, AI/ML Wing
Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Research Interests
Artificial Intelligence, Machine Learning
Prof. M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Professor
+1 more affiliationDepartment of Information Sciences and Technology
George Mason University, USA
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
AKM Mahbubur Rahman, PhD
- Director, DS & IPT Wings
Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
References
- 1.Chen, Tianyu, Wang, Zeyu, Li, Lin, Li, Ding, Li, Zongyang, Chang, Xiaoning, Bian, Pan, Liang, Guangtai, Wang, Qianxiang, Xie, Tao. (2025). Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories. Dagstuhl Research Online Publication Server[10.4230/lipics.ecoop.2025.6]
- 2.Michaël Defferrard. (2016). Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Gdl Seminar). Figshare[10.5281/zenodo.1318397]
- 3.Bing Yu, Haoteng Yin, Zhanxing Zhu. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. , 3634–3640[10.24963/ijcai.2018/505]
- 4.Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li. (2019). T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858[10.1109/tits.2019.2935152]
- 5.Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan. (2019). Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 922–929[10.1609/aaai.v33i01.3301922]
- 6.Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang. (2019). Graph WaveNet for Deep Spatial-Temporal Graph Modeling. , 1907–1913[10.24963/ijcai.2019/264]
- 7.Billy M. Williams, L A Hoel. (2003). Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering, 129(6), 664–672[10.1061/(asce)0733-947x(2003)129:6(664)]
- 8.Chih-Hsien Wu, Jan-Ming Ho, Dong-Hyun Lee. (2004). Travel-Time Prediction With Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems, 5(4), 276–281[10.1109/tits.2004.837813]
- 9.Iwao Okutani, Yorgos J. Stephanedes. (1984). Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B Methodological, 18(1), 1–11[10.1016/0191-2615(84)90002-x]
- 10.Chao Chen, Karl Petty, Alexander Skabardonis, Pravin Varaiya, Zhanfeng Jia. (2001). Freeway Performance Measurement System: Mining Loop Detector Data. Transportation Research Record Journal of the Transportation Research Board, 1748(1), 96–102[10.3141/1748-12]
- 11.Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong. (2020). LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. , 2355–2361[10.24963/ijcai.2020/326]
- 12.Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng. (2019). Gated Residual Recurrent Graph Neural Networks for Traffic Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 485–492[10.1609/aaai.v33i01.3301485]
- 13.Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. (2019). GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. , 2286–2293[10.24963/ijcai.2019/317]
- 14.Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Ki-Hwan Kim, Sungahn Ko, Jaegul Choo. (2020). ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed. , 1215–1224[10.1145/3340531.3411940]
- 15.Cheonbok Park, Chunggi Lee, Hyojin Bahng, Taeyun won, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo. (2019). STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting.. arXiv (Cornell University)[link]
- 16.William L. Hamilton, Rex Ying, Jure Leskovec. (2017). Inductive Representation Learning on Large Graphs. arXiv (Cornell University), 30, 1024–1034[10.48550/arxiv.1706.02216]
- 17.Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. (2017). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic\n Forecasting. arXiv (Cornell University)[10.48550/arxiv.1707.01926]
- 18.Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong. (2020). Spatial-Temporal Transformer Networks for Traffic Flow Forecasting. arXiv (Cornell University)[10.48550/arxiv.2001.02908]
Cited By
- 1.Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang, Xiaoyun Gong, Liang Zhao. (2024). Graph Neural Network for Spatiotemporal Data: Methods and Applications. SSRN Electronic Journal[10.2139/ssrn.4725185]
- 2.Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang. (2025). An urban road traffic flow prediction method based on multi-information fusion. Scientific Reports, 15(1), 5568[10.1038/s41598-025-88429-y]
- 3.Yuen Hoi Lau, Raymond Chi-Wing Wong. (2021). Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting. , 427–430[10.1145/3474717.3484207]
- 4.Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao. (2022). ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting. Journal of Advanced Transportation, 2022, 1–17[10.1155/2022/2806183]