Volume 6 (1), June 2023, Pages 91-112

Sara Tavasolian1, Mehdi Afzali 2

1 Non-profit Higher Education Institutions Roozbeh Zanjan Branch, Zanjan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

2Islamic Azad University of Zanjan, Zanjan, Iran,  This email address is being protected from spambots. You need JavaScript enabled to view it.


In many developing countries, predicting traffic flow is one of the solutions to prevent congestion on highways and routes, and the intelligent transportation system is considered one of the solutions to problems related to transportation and traffic. Knowledge of the predicted situation for traffic flow is essential in traffic management and informing passengers. This research presents a short-term intelligent transportation traffic flow forecasting model, which first examines how traffic forecasting can improve the performance of intelligent transportation system applications. Then the method and basic concepts of traffic flow forecasting are introduced, and the two main categories of forecasting, statistical models and machine learning-based forecasting methods (supervised and unsupervised) are discussed. Finally, a method based on machine learning using a genetic algorithm is Presented. The prediction was used as a powerful method for the mathematical modeling of traffic data in the proposed genetic algorithm method to select important traffic data features and neural networks for classification. The simulation and results presented in this research show a 3 percent improvement in traffic flow prediction with the proposed method, which uses SVM as a classifier in the primary method, and the simulation of this method has output a value of 93.6, But the suggested method has an output of 96.6


Traffic Flow Prediction, Vanet Data, Artificial Neural Network, Genetic Algorithm.





Boukerche, A., Tao, Y., & Sun, P. (2020). Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks, 182, 107484.

Du, S., Li, T., Gong, X., & Horng, S. J. (2018). A hybrid method for traffic flow forecasting using multimodal deep learning. arXiv preprint arXiv:1803.02099.

Ghasempoor, Z., & Behzadi, S. (2021). Traffic Modeling and Prediction Using Basic Neural Network and Wavelet Neural Network Along with Traffic Optimization Using Genetic Algorithm, Particle Swarm, and Colonial Competition. Journal of Geomatics Science and Technology, 10(3), 147-163.

Hajian, S.R. (2015). A review of technology-based methods in identifying road traffic congestion in vehicular networks. The Second National Conference Of New Achievements in Electricity and Computers, Esfarain, 2015.

Han, D., Chen, J., & Sun, J. (2019). A parallel spatiotemporal deep learning network for highway traffic flow forecasting. International Journal of Distributed Sensor Networks, 15(2), 1550147719832792.

Knoblich, G., Butterfill, S., & Sebanz, N. (2011). Psychological research on joint action: theory and data. Psychology of learning and motivation, 54, 59-101.

Lakshmi, K., Visalakshi, N. K., Shanthi, S., & Parvathavarthini, S. (2017). Clustering Categorical Data using k-modes based on Cuckoo Search Optimization Algoriothm. Ictact journal on Soft Computing, 8(1).

Li, T., Ni, A., Zhang, C., Xiao, G., & Gao, L. (2020). Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features. IET Intelligent Transport Systems, 14(14), 1978-1986.

Li, X., Chen, F., Sun, D., & Tao, M. (2015). Predicting menopausal symptoms with artificial neural network. Expert Systems with Applications, 42(22), 8698-8706.

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.

Rizwan, P., Suresh, K., & Babu, M. R. (2016, October). Real-time smart traffic management system for smart cities by using Internet of Things and big data. In 2016 international conference on emerging technological trends (ICETT) (pp. 1-7). IEEE.

Sepasgozar, S. S., & Pierre, S. (2022). Network traffic prediction model considering road traffic parameters using artificial intelligence methods in VANET. IEEE Access, 10, 8227-8242.

Singh, G., Chakrabarty, N., & Gupta, K. (2014). Traffic congestion detection and management using vehicular ad-hoc networks (VANETs) in India. International Journal of Advanced Computer Technology (IJACT), 3, 24.

Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., & Gottschlich, J. (2018). Precision and recall for time series. Advances in neural information processing systems, 31.

Transportation and Economy Report (2021).

Zhang, Y., & Huang, G. (2018). Traffic flow prediction model based on deep belief network and genetic algorithm. IET Intelligent Transport Systems, 12(6), 533-541.