Article Sidebar

Download
PDF
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

Although traffic violations are among the most significant factors contributing to road accidents, little research has focused on predicting specific types of traffic violations in Qatar and the Gulf region, where the number of vehicles has increased significantly in recent years. This work addresses this gap by proposing a machine-learning framework to solve fine-grained, multi-class prediction of traffic violation types on a large-scale dataset from Qatar. The dataset is extremely imbalanced and has a high-dimensional categorical space, with 97 original violation classes. We created a preprocessing pipeline to address these issues by filtering the 97 original codes to nine well-represented violation types while retaining 99.6% of the records. The final model uses 18 spatiotemporal features, including our zone violation entropy measure, which captures violation diversity in each enforcement zone. Five classifiers are evaluated with a strict temporal train–validation–test split, training-based encoding (to avoid temporal leakage), and balanced class weighting. Random forest's macro F1 score is 0.6184 and balanced accuracy is 0.7064, outperforming the linear and neural baselines. SHAP analysis shows that location-derived features account for ~74% of the model's predictive importance, while temporal features account for ~25%. The results indicate that violation type is more strongly associated with location than with time, and they provide implications for targeted enforcement by location. The suggested framework provides a reproducible way to predict violation types in a multi-class setting. It shows that enforcement information alone can support predictive analysis without using road topology or sensor data.

Keywords

Traffic violation Multiclass classification Spatiotemporal analysis Random Forest Transportation systems Traffic safety

Article Details

How to Cite
Alshriem, M., & Yang, Y. (2026). Spatial–temporal machine learning for traffic violation type prediction: a case study in Qatar . Future Technology, 5(4), 270–283. Retrieved from https://fupubco.com/futech/article/view/1152
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Gopalakrishnan, S. (2012). A public health perspective of road traffic accidents. Journal of Family Medicine and Primary Care, 1(2), 144–150. https://doi.org/10.4103/2249-4863.104987
  2. Zhang, G., Yau, K. K., Zhang, X., & Li, Y. (2016). Traffic accidents involving fatigue driving and their extent of casualties. Accident Analysis & Prevention, 87, 34–42. https://doi.org/10.1016/j.aap.2015.10.033
  3. Wu, Y. W., & Hsu, T. P. (2021). Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data. Accident Analysis & Prevention, 150, 105910. https://doi.org/10.1016/j.aap.2020.105910
  4. Zahid, M., Chen, Y., Khan, S., Jamal, A., Ijaz, M., & Ahmed, T. (2020). Predicting risky and aggressive driving behavior among taxi drivers: Do spatio-temporal attributes matter? International Journal of Environmental Research and Public Health, 17(11), 3937. https://doi.org/10.3390/ijerph17113937
  5. Ding, C., Wang, D., Liu, C., Zhang, Y., & Yang, J. (2017). Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice, 100, 65–80. https://doi.org/10.1016/j.tra.2017.04.008
  6. Boateng, C., Yang, K., Ghoreishi, S. G. A., Jang, J., Jan, M. T., Conniff, J., Furht, B., Moshfeghi, S., Newman, D., Tappen, R., et al. (2023). Abnormal driving detection using GPS data. In Proceedings of the 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET) (pp. 210–215). IEEE. https://doi.org/10.1109/honet59747.2023.10374718
  7. Li, Y., Abdel-Aty, M., Yuan, J., Cheng, Z., & Lu, J. (2020). Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data. Accident Analysis & Prevention, 141, 105509. https://doi.org/10.1016/j.aap.2020.105509
  8. Cheng, Z., Zhang, L., Zhang, Y., Wang, S., & Huang, W. (2023). A systematic approach for evaluating spatiotemporal characteristics of traffic violations and crashes at road intersections: An empirical study. Transportmetrica A: Transport Science, 19(3), 2060368. https://doi.org/10.1080/23249935.2022.2060368
  9. Wang, H., & Liang, G. (2025). Association rules between urban road traffic accidents and violations considering temporal and spatial constraints: A case study of Beijing. Sustainability, 17(4), 1680. https://doi.org/10.3390/su17041680
  10. Abdullah, S. M., Periyasamy, M., Kamaludeen, N. A., Towfek, S., Marappan, R., Kidambi Raju, S., Alharbi, A. H., & Khafaga, D. S. (2023). Optimizing traffic flow in smart cities: Soft GRU-based recurrent neural networks for enhanced congestion prediction using deep learning. Sustainability, 15(7), 5949. https://doi.org/10.3390/su15075949
  11. Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., & Yin, B. (2022). Deep learning on traffic prediction: Methods, analysis, and future directions. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4927–4943. https://doi.org/10.1109/TITS.2021.3054840
  12. Zheng, H., Lin, F., Feng, X., & Chen, Y. (2021). A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6910–6920. https://doi.org/10.1109/TITS.2020.2997352
  13. Sayed, S. A., Abdel-Hamid, Y., & Hefny, H. A. (2025). Intelligent traffic flow prediction using deep learning techniques: A comparative study. SN Computer Science, 6(1), 60. https://doi.org/10.1007/s42979-024-03552-3
  14. Tarlochan, F., Ibrahim, M. I. M., & Gaben, B. (2022). Understanding traffic accidents among young drivers in Qatar. International Journal of Environmental Research and Public Health, 19(1), 514. https://doi.org/10.3390/ijerph19010514
  15. Shaaban, K. (2012). Comparative study of road traffic rules in Qatar compared to western countries. Procedia - Social and Behavioral Sciences, 48, 992–999. https://doi.org/10.1016/j.sbspro.2012.06.1076
  16. Akin, D., Sisiopiku, V. P., Alateah, A. H., Almonbhi, A. O., Al-Tholaia, M. M., & Al-Sodani, K. A. A. (2022). Identifying causes of traffic crashes associated with driver behavior using supervised machine learning methods: Case of Highway 15 in Saudi Arabia. Sustainability, 14(24), 16654. https://doi.org/10.3390/su142416654
  17. Almurayh, A., Bedaiwy, A., & Elsharkasy, A. (2024). Predicting the orientation of vehicle drivers towards the traffic and speed enforcement surveillance system. The Open Transportation Journal, 18. https://doi.org/10.2174/0126671212299393240603080010
  18. Maghelal, P., Li, Z., Alfarra, A., & Zhu, P. (2025). Analyzing determinants of traffic violations in a multi-cultural setting: Case of Abu Dhabi. Journal of Urban Management, 14(3), 675–689. https://doi.org/10.1016/j.jum.2025.01.006
  19. Alomari, A. H., Al-Mistarehi, B., Alnaasan, T. K., & Obeidat, M. S. (2023). Utilizing different machine learning techniques to examine speeding violations. Applied Sciences, 13(8), 5113. https://doi.org/10.3390/app13085113
  20. Wan, M., Wu, Q., Yan, L., Guo, J., Li, W., Lin, W., & Lu, S. (2023). Taxi drivers’ traffic violations detection using random forest algorithm: A case study in China. Traffic Injury Prevention, 24(4), 362–370. https://doi.org/10.1080/15389588.2023.2191286
  21. Hazaymeh, K., Almagbile, A., & Alomari, A. H. (2022). Spatiotemporal analysis of traffic accidents hotspots based on geospatial techniques. ISPRS International Journal of Geo-Information, 11(4), 260. https://doi.org/10.3390/ijgi11040260
  22. Zhou, Y., Wang, Y., Zhang, F., Zhou, H., Sun, K., & Yu, Y. (2023). GATR: A road network traffic violation prediction method based on graph attention network. International Journal of Environmental Research and Public Health, 20(4), 3432. https://doi.org/10.3390/ijerph20043432
  23. Wang, Y., Zhou, Y., & Zhang, F. (2025). LTVPGA: Distilled graph attention for lightweight traffic violation prediction. ISPRS International Journal of Geo-Information, 14(9), 332. https://doi.org/10.3390/ijgi14090332
  24. Al-Thani, M. G. (2025). Traffic accident predictive model for efficient resource allocation in Qatar: A novel transformer-based approach. Advances in Artificial Intelligence and Machine Learning, 5(2), 3975–3987. https://doi.org/10.54364/AAIML.2025.52224
  25. Alshriem, M., & Yang, Y. (2026). Prediction of Large-Scale Traffic Accident Severity in Qatar: A Binary Reformulation Approach for Extreme Class Imbalance with Interpretable AI. Future Transportation, 6(2), 88. https://doi.org/10.3390/futuretransp6020088
  26. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://doi.org/10.48550/arXiv.1705.07874
  27. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
  28. Majidi, M. Z., Wang, T., & Souleyrette, R. (2025). From prediction to prevention: Identifying actionable crash factors through ML and narrative-based sensitivity testing. Future Transportation, 5(4), 190. https://doi.org/10.3390/futuretransp5040190
  29. Yang, H., Yao, X. A., Roozkhosh, F., Liu, R., & Mai, G. (2025). From theory to deep learning: Understanding the impact of geographic context factors on traffic violations. Computers, Environment and Urban Systems, 119, 102268. https://doi.org/10.1016/j.compenvurbsys.2025.102268
  30. Sneha, R., & Ebenezer, P. (2022). Prediction of traffic violation using machine learning. International Journal of Creative Research Thoughts, 10(12), b706–b711. Available at: http://www.ijcrt.org/papers/IJCRT2212193.pdf
  31. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35, 507–520. https://doi.org/10.52202/068431-0037