Journal of Transportation Research

Journal of Transportation Research

Application of a Novel Hybrid Deep Learning CNN-LSTM Model for Predicting the Number of Speeding Violations Using Hourly Traffic Data

Document Type : Original Article

Author
Aliakbar Karamvand
Ph.D., Grad., Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
10.22034/tri.2025.539121.3366
Abstract
Background: Speeding violations are a primary cause of accidents on highways, leading to significant human, economic, and social consequences. While numerous studies have explored the prediction of traffic violations and accidents, few have focused on hourly violation counts using hybrid deep learning architectures. Methods: This study introduces a novel hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict the hourly number of speeding violations. The model was developed using hourly traffic data, including variables such as vehicle count, average speed, distance violations, and violation occurrence times. Following preprocessing, the dataset was divided into 70% training, 15% validation, and 15% testing sets. A 5-fold cross-validation approach was employed to assess model performance. Results: The cross-validation results demonstrated satisfactory performance in predicting speeding violations. During training, RMSE values ranged from 69.05 to 201.5, with the first fold showing anomalous performance; the remaining folds exhibited better results, with R² values between 0.68 and 0.85 and KGE values between 0.67 and 0.83. In the testing phase, average RMSE, R², and KGE were 89.4, 0.77, and 0.78, respectively, closely aligning with training phase averages, indicating robust model performance. In the validation phase, the model achieved an RMSE of 88.4, R² of 0.76, and KGE of 0.79. Conclusion: The proposed hybrid deep learning model effectively identifies high-risk locations for speeding violations with improved accuracy and speed, facilitating optimized resource allocation and enhanced road safety. Future studies are recommended to integrate weather condition data to further improve prediction accuracy.
Keywords
Deep Learning
Prediction
Speeding Violations
Convolutional Neural Network
Traffic Violations
Subjects
-Alomari, A. H., Al-Mistarehi, B. W., Alnaasan, T. K., & Obeidat, M. S. (2023). Utilizing Different Machine Learning Techniques to Examine Speeding Violations. Applied Sciences, 13(8), 5113. doi: 10.3390/app13085113
-Aloysius, N., & Geetha, M. (2017). A review on deep convolutional neural networks. 2017 International Conference on Communication and Signal Processing (ICCSP), 0588–0592. doi: 10.1109/ICCSP.2017.8286426
-Bhattacharya, S., & Raman, R. (2023). Speed Violation Detection and Enforcement with CNN and IoT Integration. 2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023, 1–5.
doi:10.1109/SMARTGENCON60755.2023.10441950
-Cai, Q., Abdel-Aty, M., Yuan, J., Lee, J., & Wu, Y. (2020). Real-time crash prediction on expressways using deep generative models. Transportation Research Part C: Emerging Technologies, 117, 102697.
 doi: 10.1016/j.trc.2020.102697
-Cypto, J., & Karthikeyan, P. (2022). Automatic detection system of speed violations in a traffic based on deep learning technique. Journal of Intelligent & Fuzzy Systems, 43(5), 6591–6606. doi: 10.3233/JIFS-220577
-Dai, F., Huang, P., Xu, X., Qi, L., & Khosravi, M. R. (2020). Spatio-temporal deep learning framework for traffic speed forecasting in IoT. IEEE Internet of Things Magazine, 3(4), 66–69. doi: 10.1109/IOTM.0001.2000031
-Deng, L. (2018). Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]. IEEE Signal Processing Magazine, 35(1), 180–177. doi: 10.1109/MSP.2017.2762725
-Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents:
A review. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 432–446. doi: 10.1016/j.jtte.2020.05.002
-Hemanth Kumar, C., & SMT.A.Kalpana. (2025). The Hierarchical Network-Based Method for Predicting Driver Traffic Violations. Journal of Engineering Sciences, 16(04), 170–173.doi: 10.36893/jes.2025.v16i04.028
-Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
doi: 10.1162/neco.1997.9.8.1735
-Karamvand, A., Hosseini, S. A., & Aziz, S. A. (2024). Enhancing Streamflow Simulations with Gated Recurrent Units Deep Learning Models in the Flood Prone Region with
Low-Convergence Streamflow
-Data. Physics and Chemistry of the Earth, Parts A/B/C, 103737.doi: 10.1016/j.pce.2024.103737
-Li, P., Abdel-Aty, M., & Yuan, J. (2020). Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis and Prevention, 135, 105371.
doi: 10.1016/j.aap.2019.105371
-Rasa-Ezadi, A., & Seyed-Abreshami, S. E. (2022). Traffic state prediction with machine learning algorithms for short-term and
mid-term prediction time horizons. Amirkabir Journal of Civil Engineering, 54(4),1503–1520.
doi: 10.22060/ceej.2021.19650.7219
-Wang, X., Liu, J., Qiu, T., Mu, C., Chen, C., & Zhou, P. (2020). A Real-Time Collision Prediction Mechanism With Deep Learning for Intelligent Transportation System. IEEE Transactions on Vehicular Technology, 69(9), 9497–9508.
doi: 10.1109/TVT.2020.3003933
-Wegayehu, E. B., & Muluneh, F. B. (2022). Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models. Advances in Meteorology, 2022.
doi: 10.1155/2022/1860460