-احسانی، سید مجید و آسوده، راضیه (۱۳۹۹). شناسایی نقاط حادثه خیز راههای برون شهری با استفاده از روش مدلسازی فازی. مجله راهور، ۴(۲)، 134-103.
-سادات حسینی، سید محمد؛ سلیمی کوچی، محمدباقر؛ جوزی دستجره، امیر و بیرانوند، محسن (۱۴۰۳). شناسایی و تحلیل نقاط حادثه خیز شهر همدان. مجله راهور، ۸(۱)، ۱۳۱-۱۵۲.
- غفاری، سارا و حسینعلی، فرهاد (۱۴۰۳). بررسی تأثیر عوامل هندسی بر شدت تصادفات در راههای برونشهری با استفاده از الگوریتمهای هوش مصنوعی و یادگیری عمیق. پژوهشنامه حمل و نقل، ۲۱(۳)، ۴۵-۶۸.
-مرکز پژوهشهای مجلس شورای اسلامی (۱۴۰۳). بسته سیاستی کاهش تصادفات رانندگی در سال ۱۴۰۳. تهران.
-سازمان پزشکی قانونی کشور (۱۴۰۲). گزارش آمار تلفات حوادث رانندگی کشور. تهران.
-AASHTO. (2010). Highway safety manual. AASHTO.
-Alagarsamy, S., et al. (2021). Prediction of road accidents using machine learning technique. IJARSCT, 6(1), 45-52.
-Anik, B. M. T. H., et al. (2023). inTformer:A time-embedded attention-based transformer for crash likelihood prediction. arXiv:2307.03854.
-Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159.
-Chang, L. Y., & Chen, W. C. (2005). Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research, 36(4), 365-375.
-Chaudhary, M., et al. (2023). Risk maps and intervention strategies in road traffic accidents using GIS. Transport Safety Review, 52, 21-38.
-Elvik, R. (2008). A survey of operational definitions of hazardous road locations. Accident Analysis & Prevention, 40(6),1830-1835.
-Gao, Y., et al. (2023). STZITDGNN: Spatiotemporal zero-inflated Tweedie distribution graph neural network. Transportation Research Part C, 148, 104002.
-Golestani, N., Moosavi, S. M. H., & Shabani, S. (2025). Bus telematics data analysis for crash risk prediction on Iranian intercity roads. PLoS ONE, 20(4), e0312345.
-Hauer, E. (1996). Identification of sites with promise. Transportation Research Record, 1542, 54-60.
-Hauer, E. (1997). Observational before-after studies in road safety. Pergamon.
-Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making. Springer.
- Kaffash Charandabi, N., Gholami, A., & Abdollahzadeh Bina, A. (2022). Road accident risk prediction using generalized regression neural network optimized with self-organizing map. Neural Computing and Applications, 34(11), 8511-8524.
-Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31-57.
-Lord, D., & Mannering, F. (2010).The statistical analysis of crash-frequency data. Transportation Research Part A, 44(5),291-305.
-Nippani, A., et al. (2023). Graph neural networks for road safety modeling. arXiv:2311.00164.
-Pan, Y., et al. (2018). Regularized deep belief network for traffic accident prediction. IEEE Transactions on Intelligent Transportation Systems, 19(12), 3890-3900.
-Rana, P., et al. (2024). Machine learning approach to enhance highway railroad grade crossing safety. Transportation Research Record, 2678(3), 245-260.
-Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
-Sajadi, P., et al. (2024). Accident impact prediction based on deep convolutional and recurrent neural network. arXiv:2411.07537.
-Shabani, S., et al. (2019). A risk based model for the switch derailment using Bayesian network. Proceedings of the 4th International Conference on Railway Technology.
-Shabani, S., et al. (2021). Developing a model for the risk of the rail vehicles collision using Bayesian network. Journal of Transportation Safety, 43(2), 145-162.
-Singh, G., et al. (2020). Deep neural network-based predictive modeling of road accidents. Procedia Computer Science, 167, 1672-1681.
-Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437.
-Theofilatos, A., et al. (2019). Comparing machine learning and deep learning methods for real-time crash prediction. Accident Analysis & Prevention, 132, 105278.
-Wan, R., et al. (2023). Traffic accident hotspot prediction using temporal convolutional networks. In ACM SIGSPATIAL 2023.
-Yang, S., & Abdel-Aty, M. (2025). Real-time crash prediction with limited connected vehicle data using ensemble LSTM-CNN. Transportation Research Part C, 162, 104112.
-Yu, L., et al. (2021). Deep spatiotemporal graph convolutional network for traffic accident prediction. Neurocomputing, 423, 135-147.
-Yuan, J., et al. (2019). Real-time crash risk prediction using LSTM recurrent neural network. Transportation Research Record, 2673(4), 314-326.
