1. Lea, P.: Internet of Things for Architects: Architecting IoT Solutions by Implementing Sensors, Communication Infrastructure, Edge Computing, Analytics, and Security, p. 524. ISBN 978-1788470599
2. Huang, Y., Song, X., Zhang, S., Yu, J.J.Q.: Transfer learning in traffic prediction with graph neural networks. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, pp. 3732–3737 (2021). https://doi.org/10.1109/ITSC48978.2021.9564890
3. Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., Yin, B.: Deep learning on traffic prediction: methods, analysis and future directions. IEEE Trans. Intell. Transp. Syst., 1–17 (2021)
4. Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 21(11), 4883–4894 (2020)
5. Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)