Ensemble-Based Machine Learning Models for Real-Time Traffic Flow Prediction
Keywords:
Traffic Congestion Prediction, Traffic Congestion Prediction, Ensemble learning, Ensemble learning, Support Vector Classifier (SVC), Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), Multi-Layer Perceptron (MLP), CNN-LSTM, CNN-LSTM, Deep Learning, Deep LearningAbstract
Predicting traffic flow accurately and in real-time is critical in managing traffic and reducing congestion in urban areas. Traditional machine learning algorithms do not always perform well in accurately capturing and predicting the complex, nonlinear, and dynamic nature of real traffic observations and patterns. To improve this aspect of performance, this research offers an ensemble-based machine learning approach that consists of multiple base learners, which improves prediction accuracy and generalizability by combining machine learning models. The ensemble model includes the combined strength of a Multi-Layer Perceptron (MLP), a Support Vector Classifier (SVC), and a CNN-LSTM model that has the capability of addressing both spatial and temporal feature representation from video-based identification of traffic data. The context of the traffic flow prediction model is improved through the model's integration of real-time object detection of traffic frames, as well as incorporating the current weather conditions. Each base learner's predictions are optimally combined through a meta-learner Logistic Regression. The model performance is assessed through multiple evaluation criteria, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The experimental results demonstrated that the ensemble-based model surpassed traditional machine learning algorithms, such as Linear Regression, K-Nearest Neighbors, Random Forest, Decision Tree, and as well as Support Vector Regression. The ensemble model achieved upwards of 98% prediction accuracy, which was significantly better than any of the traditional machine learning algorithms tested for performance as well. The study demonstrates that ensemble-based learning techniques and multi-source feature integration can produce stable solutions for real-time traffic flow predictions to guide intelligent traffic systems development
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References
N. Cini and Z. Aydin, "A Deep Ensemble Approach for Long-Term Traffic Flow Prediction," Arabian Journal for Science and Engineering, vol. 49, Jan. 2024, doi: 10.1007/s13369-023-08672-1.
L. Qu, W. Li, W. Li, D. Ma, and Y. Wang, "Daily long-term traffic flow forecasting based on a deep neural network," Expert Systems with Applications, vol. 121, pp. 304–312, 2019.
K. Sattar, F. Chikh Oughali, K. Assi, N. Ratrout, A. Jamal, and S. M. Rahman, "Transparent deep machine learning framework for predicting traffic crash severity," Neural Computing and Applications, vol. 35, no. 2, pp. 1535–1547, 2023.
I. Lana, J. Del Ser, M. Velez, and E. I. Vlahogianni, "Road traffic forecasting: recent advances and new challenges," IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 2, pp. 93–109, 2018.
L. N. Do, N. Taherifar, and H. L. Vu, "Survey of neural network-based models for short-term traffic state prediction," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 1, p. 1285, 2019.
Z. Wang, X. Su, and Z. Ding, "Long-term traffic prediction based on LSTM encoder–decoder architecture," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 10, pp. 6561–6571, 2020.
T. Bogaerts, A. D. Masegosa, J. S. Angarita-Zapata, E. Onieva, and P. Hellinckx, "A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data," Transportation Research Part C: Emerging Technologies, vol. 112, pp. 62–77, 2020.
Y. Li, S. Chai, Z. Ma, and G. Wang, "A hybrid deep learning framework for long-term traffic flow prediction," IEEE Access, vol. 9, pp. 11264–11271, 2021.
L. N. Do, H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung, "An effective spatial-temporal attention-based neural network for traffic flow prediction," Transportation Research Part C: Emerging Technologies, vol. 108, pp. 12–28, 2019.
Y. Chen, H. Chen, P. Ye, Y. Lv, and F.-Y. Wang, "Acting as a decision maker: traffic-condition-aware ensemble learning for traffic flow prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3190–3200, 2020.
T. Bouraffa, E. K. Carlson, E. Wessman, A. Nouri, P. Lamart, and C. Berger, "Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves," in Proceedings of the 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024), 2024.
R. A. Khalil, Z. Safelnasr, N. Yemane, M. Kedir, A. Shafiqurrahman, and N. Saeed, "Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges," IEEE Open Journal of Vehicular Technology, vol. 5, Feb. 2024, doi: 10.1109/OJVT.2024.3369691.
M. Ganesan, S. Kandhasamy, B. Chokkalingam, and L. Mihet-Popa, "A Comprehensive Review on Deep Learning-Based Motion Planning and End-to-End Learning for Self-Driving Vehicles," IEEE Access, vol. X, Apr. 2024, doi: 10.1109/ACCESS.2024.3394869.
L. Falaschetti, L. Manoni, L. Palma, P. Pierleoni, and C. Turchetti, "Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture," IEEE Transactions on Intelligent Transportation Systems, vol. X, Aug. 2024, doi: 10.1109/TITS.2024.3447453.
M. Adl, R. Ahmed, C. Vidal, and A. Emadi, "Enhanced Vehicle Movement Counting at Intersections via a Self-Learning Fisheye Camera System," IEEE Access, vol. 12, May 2024, doi: 10.1109/ACCESS.2024.3408052.
G. Yıldız, A. Ulu, B. Dizdaroğlu, and D. Yıldız, "Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition," IEEE Access, vol. 11, July 2023, doi: 10.1109/ACCESS.2023.3292955.
D. Valencia, E. Muñoz, and M. Muñoz-Añasc, "Impact of the Preprocessing Stage on the Performance of Offline Automatic Vehicle Counting Using YOLO," IEEE Latin America Transactions, vol. 22, no. 9, pp. 723, Sept. 2024.
G. S. Velan, B. Shanthini, and V. Cyril Raj, "Hybrid Real-Time Traffic Management System with Time Series Analysis Using Deep Learning Methods," Nanotechnology Perceptions, vol. 20, no. S7, pp. 117–132, 2024.
N. Cini and Z. Aydin, "A Deep Ensemble Approach for Long-Term Traffic Flow Prediction," Arabian Journal for Science and Engineering, vol. 49, pp. 12377–12392, Jan. 2024, doi: 10.1007/s13369-023-08672-1.
A. Chaudhuri, "Smart Traffic Management of Vehicles Using Faster R-CNN Based Deep Learning Method," Scientific Reports, vol. 14, no. :10357, 2024, doi: 10.1038/s41598-024-60596-4.
X. Liu, Y. Wang, D. Yu, and Z. Yuan, "YOLOv8-FDD: A Real-Time Vehicle Detection Method Based on Improved YOLOv8," IEEE Access, vol. 12, Sept. 2024, doi: 10.1109/ACCESS.2024.3453298.
M. Chen, S. Banitaan, and M. Maleki, "Enhancing Pedestrian Group Detection and Tracking Through Zone-Based Clustering," IEEE Access, vol. 11, pp. 132162–132179, 2023.
P. Bhartiya, M. Bhatele, and L. Gour, "Anomaly Detection and Short-Term Prediction of Traffic Flow by Using Ensemble Deep Learning Approach," International Journal of Research Publication and Reviews, vol. 4, no. 7, pp. 1951–1959, 2023
P. Bhartiya, M. Bhatele, and A. A. Waoo, "The Role of Deep Learning in Exploring Traffic Prediction Techniques," ShodhKosh: Journal of Visual and Performing Arts, vol. 5, no. 1, pp. 540–549, 2024, doi: 10.29121/shodhkosh.v5.i1.2024.1878.
P. Bhartiya, M. Bhatele, and A. A. Waoo, "A Machine Learning Approach for Predictive Analysis of Traffic Flow," *ShodhKosh: Journal of Visual and Performing Arts*, vol. 5, no. 5, pp. 422–430, 2024, doi: 10.29121/shodhkosh.v5.i5.2024.1892.
L. Gour and A. A. Waoo, "Challenges of Distributed Computing in the Context of Deep Learning," Journal of Emerging Technologies and Innovative Research (JETIR), vol. 6, no. 6, 2021.
J. K. Jain and A. A. Waoo, "An artificial neural network technique for prediction of cyber-attack using intrusion detection system," Journal of Artificial Intelligence, Machine Learning & Neural Networks, vol. 3, pp. 33–42, 2023.
R. Nayak, P. S. Patheja, and A. Waoo, "An enhanced approach for weather forecasting using neural network," in Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), Dec. 20–22, 2011, vol. 2, pp. 833–839, Springer India, 2012.
R. Nayak, P. S. Patheja, and A. A. Waoo, "An artificial neural network model for weather forecasting in Bhopal," in IEEE International Conference on Advances in Engineering, Science and Management (ICAESM-2012), 2012, pp. 747–749, IEEE.
L. Soni and A. Waoo, "A review of recent advances in methodologies for face detection," International Journal of Current Engineering and Technology, vol. 13, no. 02, pp. 86–92, 2023.
Bhartiya, P., Bhatele, M., & Waoo, A. A. (2025, June). Efficient Traffic Flow Prediction with CNN-Based Deep Learning Techniques. In International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025) (pp. 1153-1173). Atlantis Press.
Bhatele, M., Bhartiya, P., & Waoo, A. A. (2025). Integrating deep learning and ensemble methods for urban traffic forecasting. International Journal of Environmental Sciences, 11(8s), 565–582.
L. N. Soni, A. A. Waoo, (2025) "A Lightweight and Efficient Hybrid CNN Model for Face Detection" International Journal of Environmental Sciences, Vol. 11 No. 8s, 2025, Pp. 583-591.
L. N. Soni, A. A. Waoo, (2025) "Face Detection Under Low-Light and Low-Resolution Conditions Using Contrast-Limited Adaptive Histogram Equalization and a Modified Convolutional Neural Network" published in Journal of Neonatal Surgery, 14(32s), 2025 2620-2631.
Soni, L. N., & Waoo, A. A. (2025). A Lightweight and Efficient Hybrid CNN Model for Face Detection. International Journal of Environmental Sciences, 11(8s), 583-591
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