Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Authors

  • Anas Saleh Alkarim Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia | Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Abdullah S. Al-Malaise Al-Ghamdi Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia | Information Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Saudi Arabia
  • Mahmoud Ragab Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia | Department of Mathematics, Faculty of Science, Al-Azhar University, Egypt
Volume: 14 | Issue: 2 | Pages: 13090-13094 | April 2024 | https://doi.org/10.48084/etasr.6767

Abstract

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.

Keywords:

intelligent transportation systems, smart traffic systems, traffic flow, prediction models, smart cities, bagging ensemble learning

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References

M. Angın and S. I. A. Ali, "Analysis of Factors Affecting Road Traffic Accidents in North Cyprus," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7938–7943, Dec. 2021.

N. K. Al-Shammari and S. M. H. Darwish, "In-depth Sampling Study of Charactersitics of Vehcile Crashes in Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4724–4728, Oct. 2019.

P. Sen, "Optimization of Traffic Flow Using Intelligent Transportation Systems," Mathematical Statistician and Engineering Applications, vol. 70, no. 1, pp. 720–727, Jan. 2021.

Y. A. Hanafy, M. Mashaly, and M. A. Abd El Ghany, "An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network," Electronics, vol. 10, no. 16, Jan. 2021, Art. no. 1875.

A. K. Kazi and S. M. Khan, "DyTE: An Effective Routing Protocol for VANET in Urban Scenarios," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6979–6985, Apr. 2021.

S. S. Sepasgozar and S. Pierre, "Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET," IEEE Access, vol. 10, pp. 8227–8242, 2022.

I. Moumen, J. Abouchabaka, and N. Rafalia, "Adaptive traffic lights based on traffic flow prediction using machine learning models," International Journal of Electrical and Computer Engineering, vol. 13, no. 5, Oct. 2023, Art. no. 5813.

Z. Wang, P. Sun, Y. Hu, and A. Boukerche, "A Novel Mixed Method of Machine Learning Based Models in Vehicular Traffic Flow Prediction," in Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, New York, NY, USA, Jul. 2022, pp. 95–101.

F. Sheriff, "ELMOPP: an application of graph theory and machine learning to traffic light coordination," Applied Computing and Informatics, Mar. 2021.

P. Sun, N. Aljeri, and A. Boukerche, "Machine Learning-Based Models for Real-time Traffic Flow Prediction in Vehicular Networks," IEEE Network, vol. 34, no. 3, pp. 178–185, Feb. 2020.

Z. Cheng, J. Lu, H. Zhou, Y. Zhang, and L. Zhang, "Short-Term Traffic Flow Prediction: An Integrated Method of Econometrics and Hybrid Deep Learning," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5231–5244, Jun. 2022.

R. K. C. Chan, J. M.-Y. Lim, and R. Parthiban, "A neural network approach for traffic prediction and routing with missing data imputation for intelligent transportation system," Expert Systems with Applications, vol. 171, Jun. 2021, Art. no. 114573.

A. Navarro-Espinoza et al., "Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms," Technologies, vol. 10, no. 1, Jan. 2022, Art. no. 5.

M. Ragab, H. A. Abdushkour, L. Maghrabi, D. Alsalman, A. G. Fayoumi, and A. A.-M. Al-Ghamdi, "Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System," Sustainability, vol. 15, no. 16, Aug. 2023, Art. no. 12601.

C. Axenie and S. Bortoli, "Road traffic prediction dataset." Zenodo, Oct. 07, 2020.

F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

M. Schonlau and R. Y. Zou, "The random forest algorithm for statistical learning," The Stata Journal, vol. 20, no. 1, pp. 3–29, Mar. 2020.

B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich, "A survey on long short-term memory networks for time series prediction," Procedia CIRP, vol. 99, pp. 650–655, Jan. 2021.

D. Maulud and A. M. Abdulazeez, "A Review on Linear Regression Comprehensive in Machine Learning," Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140–147, Dec. 2020.

E. Hillebrand, M. Lukas, and W. Wei, "Bagging weak predictors," International Journal of Forecasting, vol. 37, no. 1, pp. 237–254, Jan. 2021.

D. Chicco, M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, vol. 7, Jul. 2021, Art. no. e623.

J. A. Fadhil and Q. I. Sarhan, "Internet of Vehicles (IoV): A Survey of Challenges and Solutions," in 2020 21st International Arab Conference on Information Technology (ACIT), Giza, Egypt, Aug. 2020, pp. 1–10.

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How to Cite

[1]
A. S. Alkarim, A. S. Al-Malaise Al-Ghamdi, and M. Ragab, “Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13090–13094, Apr. 2024.

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