Abstract
Reliable forecasting of traffic flow requires efficient modeling of traffic data. Indeed, different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different methods to capture traffic networks’ complex underlying spatial-temporal relations. However, given the heterogeneity of traffic data, consistently capturing both spatial and temporal dependencies presents a significant challenge. Also, as more and more sophisticated methods are being proposed, models are increasingly becoming memory-heavy and, thus, unsuitable for low-powered devices. To this end, we propose Spatio-Temporal Lightweight Graph GRU, namely STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Specifically, our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks using memory-augmented attention and gating mechanisms in a continuously synchronized manner. Moreover, instead of employing separate temporal and spatial components, we show that our memory module and gated unit can successfully learn the spatial-temporal dependencies with reduced memory usage and fewer parameters. Extensive experimental results on three real-world public traffic datasets demonstrate that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Our code is available at https://github.com/Kishor-Bhaumik/STLGRU.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. Adv. Neural. Inf. Process. Syst. 33, 17766–17778 (2020)
Chen, C., et al.: Gated residual recurrent graph neural networks for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 485–492 (2019)
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. Transp. Res. Rec. 1748(1), 96–102 (2001)
Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3529–3536 (2020)
Chen, Y., Segovia, I., Gel, Y.R.: Z-gcnets: time zigzags at graph convolutional networks for time series forecasting. In: International Conference on Machine Learning, pp. 1684–1694. PMLR (2021)
Cheng, W., Shen, Y., Zhu, Y., Huang, L.: A neural attention model for urban air quality inference: Learning the weights of monitoring stations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Deb, T., Sadmanee, A., Bhaumik, K.K., Ali, A.A., Amin, M.A., Rahman, A.: Variational stacked local attention networks for diverse video captioning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 4070–4079 (2022)
Diao, Z., et al.: A hybrid model for short-term traffic volume prediction in massive transportation systems. IEEE Trans. Intell. Transp. Syst. 20(3), 935–946 (2018)
Fang, S., Zhang, Q., Meng, G., Xiang, S., Pan, C.: GSTNet: global spatial-temporal network for traffic flow prediction. In: IJCAI, pp. 2286–2293 (2019)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)
He, P., Jiang, G., Lam, S.K., Tang, D.: Travel-time prediction of bus journey with multiple bus trips. IEEE Trans. Intell. Transp. Syst. 20(11), 4192–4205 (2018)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)
Jiang, R., et al.: Spatio-temporal meta-graph learning for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8078–8086 (2023)
Lee, H., Jin, S., Chu, H., Lim, H., Ko, S.: Learning to remember patterns: pattern matching memory networks for traffic forecasting. arXiv preprint arXiv:2110.10380 (2021)
Lee, W.H., Tseng, S.S., Shieh, J.L., Chen, H.H.: Discovering traffic bottlenecks in an urban network by spatiotemporal data mining on location-based services. IEEE Trans. Intell. Transp. Syst. 12(4), 1047–1056 (2011)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Liebig, T., Piatkowski, N., Bockermann, C., Morik, K.: Dynamic route planning with real-time traffic predictions. Inf. Syst. 64, 258–265 (2017)
Lin, Z., Li, M., Zheng, Z., Cheng, Y., Yuan, C.: Self-attention convlstm for spatiotemporal prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11531–11538 (2020)
Mahmud, S., et al.: Human activity recognition from wearable sensor data using self-attention. arXiv preprint arXiv:2003.09018 (2020)
Niloy, F.F., Amin, M.A., Ali, A.A., Rahman, A.M.: Attention toward neighbors: a context aware framework for high resolution image segmentation. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2279–2283. IEEE (2021)
Niloy, F.F., Bhaumik, K.K., Woo, S.S.: CFL-net: image forgery localization using contrastive learning. arXiv preprint arXiv:2210.02182 (2022)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 914–921 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Yang, Q., Koutsopoulos, H.N., Ben-Akiva, M.E.: Simulation laboratory for evaluating dynamic traffic management systems. Transp. Res. Rec. 1710(1), 122–130 (2000)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)
Zhao, X., Fan, W., Liu, H., Tang, J.: Multi-type urban crime prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 4388–4396 (2022)
Acknowledgement
This work was partly supported by Institute for Information & communication Technology Planning & evaluation (IITP) grants funded by the Korean government MSIT: (No. 2022-0-01199, Graduate School of Convergence Security at Sungkyunkwan University), (No. 2022-0-01045, Self-directed Multi-Modal Intelligence for solving unknown, open domain problems), (No. 2022-0-00688, AI Platform to Fully Adapt and Reflect Privacy-Policy Changes), (No. 2021-0-02068, Artificial Intelligence Innovation Hub), (No. 2019-0-00421, AI Graduate School Support Program at Sungkyunkwan University), and (No. RS-2023-00230337, Advanced and Proactive AI Platform Research and Development Against Malicious Deepfakes). Lastly, this work was supported by Korea Internet & Security Agency (KISA) grant funded by the Korea government (PIPC) (No. RS-2023-00231200, Development of personal video information privacy protection technology capable of AI learning in an autonomous driving environment).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhaumik, K.K., Niloy, F.F., Mahmud, S., Woo, S.S. (2024). STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14650. Springer, Singapore. https://doi.org/10.1007/978-981-97-2266-2_23
Download citation
DOI: https://doi.org/10.1007/978-981-97-2266-2_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2265-5
Online ISBN: 978-981-97-2266-2
eBook Packages: Computer ScienceComputer Science (R0)