Abstract

Road traffic congestion is a critical issue that impacts urban mobility, environmental sustainability, and economic productivity. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies, combined with spatiotemporal data, offers promising solutions for predicting and managing traffic congestion more effectively. This literature review explores the current state of research on AI/ML models for traffic congestion prediction, focusing on integrating spatiotemporal data, including traffic flow, weather conditions, road networks, and temporal patterns. It examines various predictive modeling techniques, such as supervised, deep, and reinforcement learning. The review also highlights the challenges, such as data quality, model generalization, and computational efficiency, while discussing the potential of hybrid approaches that combine multiple modeling strategies. Furthermore, it presents a summary of key findings, trends, and future directions for the development of intelligent traffic management systems.

References

• Smith, B. L., & Demetsky, M. J., (1994). Short-term traffic flow prediction: neural network approach. Transportation Research (pp. 98-104). Available online at http://onlinepubs.trb.org/Onlinepubs/trr/1994/1453/1453-011.pdf.
• Akhtar, M., & Moridpour, S. (2021). A Review of Traffic Congestion Prediction Using Artificial Intelligence. Journal of Advanced Transportation, 2021(1), 8878011. https://doi.org/10.1155/2021/8878011.
• Bouktif, S., Cheniki, A., Ali-Ouni, A., & El-Sayed, H. (2023). Deep reinforcement learning for traffic signal control with consistent state and reward design approach. https://doi.org/10.1016/j.knosys.2023.110440.
• Wang, J., Pradhan, M. R., & Gunasekaran, N. (2022). Machine learning-based human-robot interaction in ITS. Information Processing & Management Volume 59, Issue 1, January 2022, 102750. https://doi.org/10.1016/j.ipm.2021.102750.
• Joo, H., Ahmed, S. H., & Lim, Y. (2020). Traffic signal control for smart cities using reinforcement learning. Available online at https://doi.org/10.1016/j.comcom.2020.03.005.
• Kothai, G., Poovammal, E., Dhiman, G., Ramana, K., Sharma, A., AlZain, M. A., Gaba, G. S., & Masud, M. (2021). A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities. Wireless Communications and Mobile Computing, 2021(1), 5583874. https://doi.org/10.1155/2021/5583874.
• Slimani, N., Amghar, M., & Sbiti., N. (2022). Deep learning and time series analysis on traffic flow forecasting. Journal of Theoretical and Applied Information Technology, 100(5). https://www.jatit.org/volumes/Vol100No5/5Vol100No5.pdf.
• Iyer, L. S. (2021). AI-enabled applications towards intelligent transportation. Available online at https://doi.org/10.1016/j.treng.2021.100083.
• Samaei, S. R., (2024). A Comprehensive Algorithm for AI-Driven Transportation Improvements in Urban Areas. 13th International Conference on Advanced Research in Science, Engineering and Technology, Brussels, Belgium.At: https://civilica.com/doc/1930041/ Volume: 13.
• Ranjan, N., Bhandari, S., Zhao, H. P., Kim, H., & Khan, P (2020). City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN, in IEEE Access, vol. 8, pp. 81606-81620, 2020, doi: 10.1109/ACCESS.2020.2991462.
• Kang, L., Cheng, S., & Lei, B. (2021). Optimization Model and Algorithm of Urban Road Traffic Network Design Based on Decision Tree Quantification. In 2021 International Conference on Aviation Safety and Information Technology (ICASIT 2021). Association for Computing Machinery, New York, NY, USA, 696–699. https://doi.org/10.1145/3510858.3511365.
• Bokaba, T., Doorsamy, W., & Paul, B. S. (2022). A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion. Applied Sciences, 12(3), 1337. https://doi.org/10.3390/app12031337.
• Wang, P., Zhao, J., Gao, Y., Sotelo, M. A., & Li, Z. (2020) Lane Work-Schedule of Toll Station Based on Queuing Theory and PSO-LSTM Model, in IEEE Access, vol. 8, pp. 84434-84443, doi: 10.1109/ACCESS.2020.2992070.
• Liu, Y., Zhang, N., Luo, X., & Yang, M. (2021). Traffic Flow Forecasting Analysis based on Two Methods, J. Phys.: Conf. Ser. 1861 012042. doi:10.1088/1742-6596/1861/1/012042.
• Medina-Salgado, B., Sanchez-DelaCruz, E., Pozos-Parra, P., & Sierra, J. E. (2022). Urban traffic flow prediction techniques: A review. Sustainable Computing: Informatics and Systems, 35, 100739. https://doi.org/10.1016/j.suscom.2022.100739.
• Christalin, N. S., Mandal, T. K., & Prakash, G. L. (2022). A Novel Optimized LSTM Networks for Traffic Prediction in VANET. Journal of System and Management Sciences Vol. 12 No. 1, pp. 461-479 DOI:10.33168/JSMS.2022.0130.
• Mushtaq, A., Haq, I. U., Imtiaz, M. U., Khan, A., & Shafiq, O. (2021). Traffic Flow Management of Autonomous Vehicles Using Deep Reinforcement Learning and Smart Rerouting, in IEEE Access, vol. 9, pp. 51005-51019, doi: 10.1109/ACCESS.2021.3063463.
• Priyadharsini, N. K., & Chitra, D. (2021). A kernel support vector machine based anomaly detection using spatio-temporal motion pattern models in extremely crowded scenes. J Ambient Intell Human Comput 12, 5225–5234. https://doi.org/10.1007/s12652-020-02000-3.
• Sofuoglu, S. E., & Aviyente, S. (2022). GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data. Signal Processing, 192, 108370. https://doi.org/10.1016/j.sigpro.2021.108370.
• Liu, Y., Wang, X., Hou, W., Liu, H., & Wang, J. (2022). A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction. Knowledge-Based Systems, 255, 109760. https://doi.org/10.1016/j.knosys.2022.109760.
• Wang, K., Ma, C., Qiao, Y., Lu, X., Hao, W., & Dong, S. (2021). A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction. Physica A: Statistical Mechanics and its Applications, 583, 126293. https://doi.org/10.1016/j.physa.2021.126293.
• Li, Y., Chai, S., Ma, Z., & Wang, G. (2021). A Hybrid Deep Learning Framework for Long-Term Traffic Flow Prediction, in IEEE Access, vol. 9, pp. 11264-11271, 2021, doi: 10.1109/ACCESS.2021.3050836.
• Dadashova, B., Li, X., Turner, S., & Koeneman, P. (2021). Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators. Socioeconomic Planning Sciences, 75, 100877. https://doi.org/10.1016/j.seps.2020.100877.
• Wei, X., Ren, Y., Shen, L., & Shu, T. (2022). Exploring the spatiotemporal pattern of traffic congestion performance of large cities in China: A real-time data based investigation. Environmental Impact Assessment Review, 95, 106808. https://doi.org/10.1016/j.eiar.2022.106808.
• Nie, X., Peng, J., Wu, Y., Gupta, B., & El-Latif, A. A. A. (2022). Real-Time Traffic Speed Estimation for Smart Cities with Spatial Temporal Data: A Gated Graph Attention Network Approach. Big Data Research, 28, 100313. https://doi.org/10.1016/j.bdr.2022.100313.
• Wang, L., Yan, X., Liu, Y., Liu, X., & Chen, D. (2021). Grid Mapping for Road Network Abstraction and Traffic Congestion Identification Based on Probe Vehicle Data, Journal of Transportation Engineering, Part A: Systems, Volume 147 • Issue 5. https://doi.org/10.1061/JTEPBS.0000517.