Traffic Flow Prediction Using Deep Learning Techniques in Urban Road Networks

[1] Lv, Y., Duan, Y., Kang, W., et al. (2014). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873. Lv Y. Duan Y. Kang W. ( 2014 ). Traffic flow prediction with big data: A deep learning approach . IEEE Transactions on Intelligent Transportation Systems , 16 ( 2 ), 865 – 873 . Search in Google Scholar

[2] Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17. Polson N. G. Sokolov V. O. ( 2017 ). Deep learning for short-term traffic flow prediction . Transportation Research Part C: Emerging Technologies , 79 , 1 – 17 . Search in Google Scholar

[3] Pamuła, T., & Żochowska, R. (2023). Estimation and prediction of the OD matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence, 117, 105550. Pamuła T. Żochowska R. ( 2023 ). Estimation and prediction of the OD matrix in uncongested urban road network based on traffic flows using deep learning . Engineering Applications of Artificial Intelligence , 117 , 105550 . Search in Google Scholar

[4] Yang, D., & Lv, L. (2023). A graph deep learning-based fast traffic flow prediction method in urban road networks. IEEE Access. Yang D. Lv L. ( 2023 ). A graph deep learning-based fast traffic flow prediction method in urban road networks . IEEE Access . Search in Google Scholar

[5] Medina-Salgado, B., Sánchez-DelaCruz, E., Pozos-Parra, P., et al. (2022). Urban traffic flow prediction techniques: A review. Sustainable Computing: Informatics and Systems, 35, 100739. Medina-Salgado B. Sánchez-DelaCruz E. Pozos-Parra P. ( 2022 ). Urban traffic flow prediction techniques: A review . Sustainable Computing: Informatics and Systems , 35 , 100739 . Search in Google Scholar

[6] Razali, N. A. M., Shamsaimon, N., Ishak, K. K., et al. (2021). Gap, techniques and evaluation: Traffic flow prediction using machine learning and deep learning. Journal of Big Data, 8, 1–25. Razali N. A. M. Shamsaimon N. Ishak K. K. ( 2021 ). Gap, techniques and evaluation: Traffic flow prediction using machine learning and deep learning . Journal of Big Data , 8 , 1 – 25 . Search in Google Scholar

[7] Bilotta, S., Collini, E., Nesi, P., et al. (2022). Short-term prediction of city traffic flow via convolutional deep learning. IEEE Access, 10, 113086–113099. Bilotta S. Collini E. Nesi P. ( 2022 ). Short-term prediction of city traffic flow via convolutional deep learning . IEEE Access , 10 , 113086 – 113099 . Search in Google Scholar

[8] Liu, Z., Li, Z., Wu, K., et al. (2018). Urban traffic prediction from mobility data using deep learning. IEEE Network, 32(4), 40–46. Liu Z. Li Z. Wu K. ( 2018 ). Urban traffic prediction from mobility data using deep learning . IEEE Network , 32 ( 4 ), 40 – 46 . Search in Google Scholar

[9] Chen, C., Liu, Z., Wan, S., et al. (2020). Traffic flow prediction based on deep learning in internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3776–3789. Chen C. Liu Z. Wan S. ( 2020 ). Traffic flow prediction based on deep learning in internet of vehicles . IEEE Transactions on Intelligent Transportation Systems , 22 ( 6 ), 3776 – 3789 . Search in Google Scholar

[10] Pamuła, T. (2018). Impact of data loss for prediction of traffic flow on an urban road using neural networks. IEEE Transactions on Intelligent Transportation Systems, 20(3), 1000–1009. Pamuła T. ( 2018 ). Impact of data loss for prediction of traffic flow on an urban road using neural networks . IEEE Transactions on Intelligent Transportation Systems , 20 ( 3 ), 1000 – 1009 . Search in Google Scholar

[11] Essien, A., Petrounias, I., Sampaio, P., et al. (2021). A deep-learning model for urban traffic flow prediction with traffic events mined from Twitter. World Wide Web, 24(4), 1345–1368. Essien A. Petrounias I. Sampaio P. ( 2021 ). A deep-learning model for urban traffic flow prediction with traffic events mined from Twitter . World Wide Web , 24 ( 4 ), 1345 – 1368 . Search in Google Scholar

[12] Wang, J., Chen, R., & He, Z. (2019). Traffic speed prediction for urban transportation network: A path-based deep learning approach. Transportation Research Part C: Emerging Technologies, 100, 372–385. Wang J. Chen R. He Z. ( 2019 ). Traffic speed prediction for urban transportation network: A path-based deep learning approach . Transportation Research Part C: Emerging Technologies , 100 , 372 – 385 . Search in Google Scholar

[13] Pei, J., Liu, W., Li, J., et al. (2024). A review of federated learning methods in heterogeneous scenarios. IEEE Transactions on Consumer Electronics. Pei J. Liu W. Li J. ( 2024 ). A review of federated learning methods in heterogeneous scenarios . IEEE Transactions on Consumer Electronics . Search in Google Scholar

[14] Xia, H., Xu, S., Pei, J., et al. (2023). Fedme 2: Memory evaluation & erase promoting federated unlearning in DTMN. IEEE Journal on Selected Areas in Communications. Xia H. Xu S. Pei J. ( 2023 ). Fedme 2: Memory evaluation & erase promoting federated unlearning in DTMN . IEEE Journal on Selected Areas in Communications . Search in Google Scholar

[15] Xie, P., Li, T., Liu, J., et al. (2020). Urban flow prediction from spatiotemporal data using machine learning: A survey. Information Fusion, 59, 1–12. Xie P. Li T. Liu J. ( 2020 ). Urban flow prediction from spatiotemporal data using machine learning: A survey . Information Fusion , 59 , 1 – 12 . Search in Google Scholar

[16] Han, L., & Huang, Y. S. (2020). Short-term traffic flow prediction of road network based on deep learning. IET Intelligent Transport Systems, 14(6), 495–503. Han L. Huang Y. S. ( 2020 ). Short-term traffic flow prediction of road network based on deep learning . IET Intelligent Transport Systems , 14 ( 6 ), 495 – 503 . Search in Google Scholar

[17] Abdullah, S. M., Periyasamy, M., Kamaludeen, N. A., et al. (2023). Optimizing traffic flow in smart cities: Soft GRU-based recurrent neural networks for enhanced congestion prediction using deep learning. Sustainability, 15(7), 5949. Abdullah S. M. Periyasamy M. Kamaludeen N. A. ( 2023 ). Optimizing traffic flow in smart cities: Soft GRU-based recurrent neural networks for enhanced congestion prediction using deep learning . Sustainability , 15 ( 7 ), 5949 . Search in Google Scholar

[18] Wu, Y., Tan, H., Qin, L., et al. (2018). A hybrid deep learning-based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166–180. Wu Y. Tan H. Qin L. ( 2018 ). A hybrid deep learning-based traffic flow prediction method and its understanding . Transportation Research Part C: Emerging Technologies , 90 , 166 – 180 . Search in Google Scholar

[19] Fouladgar, M., Parchami, M., Elmasri, R., et al. (2017). Scalable deep traffic flow neural networks for urban traffic congestion prediction. 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2251–2258. Fouladgar M. Parchami M. Elmasri R. ( 2017 ). Scalable deep traffic flow neural networks for urban traffic congestion prediction . 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE , 2251 – 2258 . Search in Google Scholar

[20] Huang, S., Liu, C., & Goda, K. (2023). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action. Chinese Journal of Geotechnical Engineering, 45(2), 336–344. Huang S. Liu C. Goda K. ( 2023 ). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action . Chinese Journal of Geotechnical Engineering , 45 ( 2 ), 336 – 344 . Search in Google Scholar