Acceso abierto

Intelligent Traffic Congestion Control Using Black Widow Optimization with Hybrid Deep Learning on Smart City Environment


Cite

Mall, P. K., Narayan, V., Pramanik, S., Srivastava, S., Faiz, M., Sriramulu, S., & Kumar, M. N. (2023). FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models. In Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities (pp. 76-95). IGI Global. Search in Google Scholar

Hameed, A., Violos, J., & Leivadeas, A. (2022). A deep learning approach for IoT traffic multi-classification in a smart-city scenario. IEEE Access, 10, 21193-21210. Search in Google Scholar

Ramana, K., Srivastava, G., Kumar, M. R., Gadekallu, T. R., Lin, J. C. W., Alazab, M., & Iwendi, C. (2023). A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas. IEEE Transactions on Intelligent Transportation Systems. Search in Google Scholar

Chen, G., & Zhang, J. (2022). Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities. Applied Soft Computing, 121, 108692. Search in Google Scholar

Khan, N. U., Shah, M. A., Maple, C., Ahmed, E., & Asghar, N. (2022). Traffic flow prediction: an intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble. Sustainability, 14(7), 4164. Search in Google Scholar

AlZoman, R. M., & Alenazi, M. J. (2021). A comparative study of traffic classification techniques for smart city networks. Sensors, 21(14), 4677. Search in Google Scholar

Zhou, S., Wei, C., Song, C., Pan, X., Chang, W., & Yang, L. (2022). Short-term traffic flow prediction of the smart city using 5G internet of vehicles based on edge computing. IEEE Transactions on Intelligent Transportation Systems. Search in Google Scholar

Gobezie, A., & Fufa, M. S. (2020). Machine learning and deep learning models for traffic flow prediction: A survey. Search in Google Scholar

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), 1-25. Search in Google Scholar

Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10(1), 5. Search in Google Scholar

Qi, T., Chen, L., Li, G., Li, Y., & Wang, C. (2023). FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network. Applied Soft Computing, 138, 110175. Search in Google Scholar

Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems, 139, 100-108. Search in Google Scholar

Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3), 417-426. Search in Google Scholar

Dai, F., Huang, P., Mo, Q., Xu, X., Bilal, M., & Song, H. (2022). ST-InNet: Deep Spatio-Temporal Inception Networks for Traffic Flow Prediction in Smart Cities. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19782-19794. Search in Google Scholar

Hassan, M., Kanwal, A., Jarrah, M., Pradhan, M., Hussain, A., & Mago, B. (2022, February). Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine. In 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-4). IEEE. Search in Google Scholar

Vijayalakshmi, B., Ramar, K., Jhanjhi, N. Z., Verma, S., Kaliappan, M., Vijayalakshmi, K., ... & Ghosh, U. (2021). An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. International Journal of Communication Systems, 34(3), e4609. Search in Google Scholar

Joseph, L. L., Goel, P., Jain, A., Rajyalakshmi, K., Gulati, K., & Singh, P. (2021, October). A novel hybrid deep learning algorithm for smart city traffic congestion predictions. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 561-565). IEEE Search in Google Scholar

Awan, F. M., Minerva, R., & Crespi, N. (2021). Using noise pollution data for traffic prediction in smart cities: experiments based on LSTM recurrent neural networks. IEEE Sensors Journal, 21(18), 20722-20729. Search in Google Scholar

Qaisar, S. M., Khan, S. I., Srinivasan, K., & Krichen, M. (2023). Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. Journal of King Saud University-Computer and Information Sciences, 35(1), 26-37. Search in Google Scholar

Henry, A., Gautam, S., Khanna, S., Rabie, K., Shongwe, T., Bhattacharya, P., Sharma, B., & Chowdhury, S. (2023). Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System. Sensors, 23(2), 890. Search in Google Scholar

Wang, H., Zhang, C., & Wu, H. (2023). Shear Capacity Prediction Model of Deep Beam Based on New Hybrid Intelligent Algorithm. Buildings, 13(6), 1395. Search in Google Scholar

https://www.kaggle.com/datasets/arashnic/road-trafic-dataset?select=region_traffic.csv Search in Google Scholar