-Alajali, W., Zhou, W., Wen, S., & Wang, Y. (2018). Intersection traffic prediction using decision tree models. Symmetry, 10(9), 1–16. https://doi.org/10.3390/sym10090386
-Ata, A., Khan, M. A., Abbas, S., Khan, M. S., & Ahmad, G. (2021). Adaptive IoT Empowered Smart Road Traffic Congestion Control System Using Supervised Machine Learning Algorithm. The Computer Journal, 64(11), 1672–1679. doi.org/10.1093/comjnl/bxz129
-Bratsas, C., Koupidis, K., Salanova, J. M., Giannakopoulos, K., Kaloudis, A., & Aifadopoulou, G. (2020). A comparison of machine learning methods for the prediction of traffic speed in Urban places. Sustainability (Switzerland), 12(1), 1–15.
doi.org/10.3390/SU12010142
-Cai, P., Wang, Y., Lu, G., Chen, P., Ding, C., & Sun, J. (2016). A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies, 62(January), 21–34. doi.org/10.1016/j.trc.2015.11.002
-Fouladgar, M., Parchami, M., Elmasri, R., & Ghaderi, A. (2017). Scalable deep traffic flow neural networks for urban traffic congestion prediction. 2017 International Joint Conference on Neural Networks (IJCNN), 2251–2258. doi.org/10.1109/IJCNN.2017.7966128
-Gong, Y., Isom, T., Lu, P., Yang, X., & Wang, A. (2022). Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 0(0), 1–11. doi.org/10.1080/15472450.2022.2157212
-Medina-Salgado, B., Sánchez-DelaCruz, E., Pozos-Parra, P., & Sierra, J. E. (2022). Urban traffic flow prediction techniques: A review. Sustainable Computing: Informatics and Systems, 35(January).doi.org/10.1016/j.suscom.2022.100739
-Rasaizadi, A., & Seyedabrishami, S. (2022). Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow. Journal of Advanced Transportation.doi.org/10.1155/2022/3198636
-Razali, N. A. M., Shamsaimon, N., Ishak, K. K., Ramli, S., Amran, M. F. M., & Sukardi, S. (2021). Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning. Journal of Big Data, 8(1).
doi.org/10.1186/s40537-021-00542-7
-Shami, S., & Mamdoohi, A. R. (2022). An effectiveness analysis of Tehran peak-based traffic scheme, a travel behavior model. Journal of Transportation Research, 19(3), 149–164. doi.org/10.22034/tri.2021.286382.2909
-Shen, X., & Wei, S. (2020). Application of XGBoost for Hazardous Material Road Transport Accident Severity Analysis. IEEE Access, 8, 206806–206819.doi.org/10.1109/ACCESS.2020.3037922
-Tavakoli, E., & Hadji Hosseinlou, M. (2023). Short-Term Prediction of Traffic Speed using Recurrent Neural Networks (RNN). Quarterly Journal of Transportation Engineering, 15(1), 3369–3394. https://doi.org/10.22119/jte.2022.342446.2600
-Xie, P., Li, T., Liu, J., Du, S., Yang, X., & Zhang, J. (2020). Urban flow prediction from spatiotemporal data using machine learning: A survey. Information Fusion, 59, 1–12. doi.org/10.1016/j.inffus.2020.01.002
-Zhang, Z., Fu, D., Liu, F., Wang, J., Xiao, K., & Wolshon, B. (2023). COVID-19, traffic demand, and activity restriction in China: A national assessment. Travel Behaviour and Society, 31, 10–23. doi.org/10.1016/j.tbs.2022.11.001