Tedjopurnomo, D. A. et al. A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans. Knowl. Data Eng. 34 (4), 1544–1561 (2020).
Santos, K., Dias, J. P. & Amado, C. A literature review of machine learning algorithms for crash injury severity prediction. J. Saf. Res. 80, 254–269 (2022).
WHO, W.H.O. Global status report on road safety 2023. [cited 13.12.2023; (2023). Available from: https://www.who.int/publications/i/item/9789240086517
Chen, S. et al. The global macroeconomic burden of road injuries: estimates and projections for 166 countries. Lancet Planet. Health. 3 (9), e390–e398 (2019).
(TUİK). T.İ.K. Karayolu Trafik Kaza İstatistikleri,. 2023 [cited 5.012.2023; Available from,. 2023 [cited 5.012.2023; Available from: (2022). https://data.tuik.gov.tr/Bulten/Index?p=Karayolu-Trafik-Kaza-Istatistikleri-2022-49513
Wijnen, W. & Stipdonk, H. Social costs of road crashes: an international analysis. Accid. Anal. Prev. 94, 97–106 (2016).
Shaik, M. E., Islam, M. M. & Hossain, Q. S. A review on neural network techniques for the prediction of road traffic accident severity. Asian Transp. Stud. 7, 100040 (2021).
Mannering, F. L., Shankar, V. & Bhat, C. R. Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods Accid. Res. 11, 1–16 (2016).
Ashraf, I. et al. Catastrophic factors involved in road accidents: underlying causes and descriptive analysis. PLoS One. 14 (10), e0223473 (2019).
Gatarić, D. et al. Predicting road traffic Accidents—Artificial neural network approach. Algorithms 16 (5), 257 (2023).
Halim, Z. et al. Artificial intelligence techniques for driving safety and vehicle crash prediction. Artif. Intell. Rev. 46, 351–387 (2016).
Wang, Y. & Zhang, W. Analysis of roadway and environmental factors affecting traffic crash severities. Transp. Res. Procedia. 25, 2119–2125 (2017).
Pourroostaei Ardakani, S. et al. Road Car accident prediction using a Machine-Learning-Enabled data analysis. Sustainability 15 (7), 5939 (2023).
Chaabani, H. et al. A neural network approach to visibility range Estimation under foggy weather conditions. Procedia Comput. Sci. 113, 466–471 (2017).
Alkheder, S., Taamneh, M. & Taamneh, S. Severity prediction of traffic accident using an artificial neural network. J. Forecast. 36 (1), 100–108 (2017).
Gu, Y., Qian, Z. S. & Chen, F. From Twitter to detector: Real-time traffic incident detection using social media data. Transp. Res. Part. C: Emerg. Technol. 67, 321–342 (2016).
Hamim, O. F. et al. A sociotechnical approach to accident analysis in a low-income setting: using accimaps to guide road safety recommendations in Bangladesh. Saf. Sci. 124, 104589 (2020).
Singh, G. et al. Deep neural network-based predictive modeling of road accidents. Neural Comput. Appl. 32, 12417–12426 (2020).
Shi, Q. & Abdel-Aty, M. Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transp. Res. Part. C: Emerg. Technol. 58, 380–394 (2015).
Mohanty, M. et al. Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN & logistic model using historical crash data. Int. J. Injury Control Saf. Promotion. 30 (2), 155–171 (2023).
Lee, J. et al. Model evaluation for forecasting traffic accident severity in rainy seasons using machine learning algorithms: Seoul City study. Appl. Sci. 10 (1), 129 (2019).
Lavrenz, S. M. et al. Time series modeling in traffic safety research. Accid. Anal. Prev. 117, 368–380 (2018).
Asadianfam, S., Shamsi, M., Rasouli, A. & Kenari Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers. Multimedia Tools Appl. 79 (33–34), 24645–24684 (2020).
Gutierrez-Osorio, C. & Pedraza, C. Modern data sources and techniques for analysis and forecast of road accidents: A review. J. Traffic Transp. Eng. (English edition). 7 (4), 432–446 (2020).
Parsa, A. B. et al. Toward Safer Highways, Application of XGBoost and SHAP for real-time Accident Detection and Feature Analysis136p. 105405 (Accident Analysis & Prevention, 2020).
Ma, Z., Mei, G. & Cuomo, S. An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors. Accid. Anal. Prev. 160, 106322 (2021).
Najafi Moghaddam Gilani, V. et al. Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models. Math. Probl. Eng. 2021, 1–11 (2021).
Formosa, N. et al. Predicting real-time traffic conflicts using deep learning. Accid. Anal. Prev. 136, 105429 (2020).
Li, K., Xu, H. & Liu, X. Analysis and visualization of accidents severity based on LightGBM-TPE. Solitons Fractals. 157, 111987 (2022). Chaos.
Zafian, T. et al. Using SHRP2 NDS Data To Examine Infrastructure and Other Factors Contributing To Older Driver Crashes during Left Turns at Signalized Intersections156. 106141 (Accident Analysis & Prevention, 2021).
Tran, D. et al. Real-time detection of distracted driving based on deep learning. IET Intel. Transport Syst. 12 (10), 1210–1219 (2018).
Guo, Y. et al. Deep learning for visual understanding: A review. Neurocomputing 187, 27–48 (2016).
LeCun, Y., Bengio, Y. & Hinton, G. Deep Learn. Nat., 521(7553): 436–444. (2015).
Zhang, Z. et al. A deep learning approach for detecting traffic accidents from social media data. Transp. Res. Part. C: Emerg. Technol. 86, 580–596 (2018).
Jiang, W. & Luo, J. Graph neural network for traffic forecasting: A survey. Expert Syst. Appl. 207, 117921 (2022).
Dong, C. et al. An improved deep learning model for traffic crash prediction. J. Adv. Transp. 2018, 1–13 (2018).
Shunshun, W., Changshun, Y. & Yong, S. A review of road traffic accident prediction methods. Am. J. Manage. Sci. Eng. 8 (3), 73–77 (2023).
Theofilatos, A., Chen, C. & Antoniou, C. Comparing machine learning and deep learning methods for real-time crash prediction. Transp. Res. Rec. 2673 (8), 169–178 (2019).
Ullah, Z. et al. Applications of artificial intelligence and machine learning in smart cities. Comput. Commun. 154, 313–323 (2020).
Ren, H. et al. A deep learning approach to the citywide traffic accident risk prediction. In:21st International Conference on Intelligent Transportation Systems (ITSC). 2018. IEEE. (2018).
Mannering, F. et al. Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods Accid. Res. 25, 100113 (2020).
Silva, P. B., Andrade, M. & Ferreira, S. Machine learning applied to road safety modeling: A systematic literature review. J. Traffic Transp. Eng. (English edition). 7 (6), 775–790 (2020).
Zantalis, F. et al. A review of machine learning and IoT in smart transportation. Fut. Internet. 11 (4), 94 (2019).
Chen, H. et al. Improved Naive Bayes classification algorithm for traffic risk management. EURASIP J. Adv. Signal Process. 2021 (1), 1–12 (2021).
Yu, B. et al. k-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J. Transp. Eng. 142 (6), 04016018 (2016).
Pakgohar, A. et al. The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach. Procedia Comput. Sci. 3, 764–769 (2011).
Moussa, G. S., Owais, M. & Dabbour, E. Variance-based Global Sensitivity Analysis for rear-end Crash Investigation Using Deep Learning165. 106514 (Accident analysis & prevention, 2020).
Owais, M., Alshehri, A., Gyani, J., Aljarbou, M. H. & Alsulamy, S. Prioritizing rear-end crash explanatory factors for injury severity level using deep learning and global sensitivity analysis. Expert Syst. Appl. 245, 123114 (2024).
Owais, M. & El Sayed, M. A. Red light crossing violations modelling using deep learning and variance-based sensitivity analysis. Expert Syst. Appl. 267, 126258 (2025).
Iranitalab, A. & Khattak, A. Comparison of four statistical and machine learning methods for crash severity prediction. Accid. Anal. Prev. 108, 27–36 (2017).
Abellán, J., López, G., De, J. & OñA Analysis of traffic accident severity using decision rules via decision trees. Expert Syst. Appl. 40 (15), 6047–6054 (2013).
Li, X. et al. Predicting motor vehicle crashes using support vector machine models. Accid. Anal. Prev. 40 (4), 1611–1618 (2008).
Aguero-Valverde, J. & Jovanis, P. P. Spatial analysis of fatal and injury crashes in Pennsylvania. Accid. Anal. Prev. 38 (3), 618–625 (2006).
Mussone, L., Ferrari, A. & Oneta, M. An analysis of urban collisions using an artificial intelligence model. Accid. Anal. Prev. 31 (6), 705–718 (1999).
Yuan, Z., Zhou, X. & Yang, T. Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. (2018).
Hermans, E., Wets, G. & Van den Bossche, F. Frequency and severity of Belgian road traffic accidents studied by state-space methods. J. Transp. Stat. 9 (1), 63 (2006).
Quddus, M. A. Time series count data models: an empirical application to traffic accidents. Accid. Anal. Prev. 40 (5), 1732–1741 (2008).
Li, P., Abdel-Aty, M. & Yuan, J. Real-time crash risk prediction on arterials based on LSTM-CNN. Accid. Anal. Prev. 135, 105371 (2020).
Yeole, M., Jain, R. K. & Menon, R. Prediction of road accident using artificial neural network. Int. J. Eng. Trends Technol. 70 (3), 151–161 (2022).
Alqatawna, A., Álvarez, A. M. R. & García-Moreno, S. S. C. Comparison of multivariate regression models and artificial neural networks for prediction highway traffic accidents in spain: A case study. Transp. Res. Procedia. 58, 277–284 (2021).
García de Soto, B. et al. Predicting road traffic accidents using artificial neural network models. Infrastructure Asset Manage. 5 (4), 132–144 (2018).
Al-Masaeid, H. R. & Khaled, F. J. Performance of traffic accidents’ prediction models. Jordan J. Civil Eng., 17(1). (2023).
Dutta, B., Barman, M. P. & Patowary, A. Application of Arima model for forecasting road accident deaths in India. Int. J. Agricultural Stat. Sci. 16 (2), 607–615 (2020).
Getahun, K. A. Time series modeling of road traffic accidents in Amhara region. J. Big Data, 8(1). (2021).
Qian, Y. et al. Forecasting deaths of road traffic injuries in China using an artificial neural network. Traffic Inj. Prev. 21 (6), 407–412 (2020).
Deretić, N. et al. SARIMA modelling approach for forecasting of traffic accidents. Sustainability 14 (8), 4403 (2022).
Husin, W. Z. W. et al. Box-Jenkins and State Space Model in Forecasting Malaysia Road Accident Cases. in Journal of Physics: Conference Series. IOP Publishing. (2021).
Junus, N. W. M., Ismail, M. T. & Arsad, Z. Predicting Penang road accidents influences: time series regression versus structural time series. Indian J. Sci. Technol. 8 (30), 1017485 (2015).
Dutta, B., Barman, M. P. & Patowary, A. N. Exponential smoothing state space innovation model for forecasting road accident deaths in India. Thail. Stat. 20 (1), 26–35 (2022).
Antoniou, C. & Yannis, G. State-space based analysis and forecasting of macroscopic road safety trends in Greece. Accid. Anal. Prev. 60, 268–276 (2013).
Jiang, F., Yuen, K. K. R. & Lee, E. W. M. A long short-term memory-based framework for crash detection on freeways with traffic data of different Temporal resolutions. Accid. Anal. Prev. 141, 105520 (2020).
Sameen, M. I. & Pradhan, B. Severity prediction of traffic accidents with recurrent neural networks. Appl. Sci. 7 (6), 476 (2017).
Wen, X., Xie, Y., Jiang, L., Pu, Z. & Ge, T. Applications of machine learning methods in traffic crash severity modelling: current status and future directions. Transp. Reviews. 41 (6), 855–879 (2021).
Katambire, V. N., Musabe, R., Uwitonze, A. & Mukanyiligira, D. Forecasting the traffic flow by using ARIMA and LSTM models: case of Muhima junction. Forecasting 5 (4), 616–628 (2023).
Wang, C., Xie, Y., Huang, H. & Liu, P. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accid. Anal. Prev. 157, 106157 (2021).
Aoki, M. State Space Modeling of time Series (Springer Science & Business Media, 2013).
Zupan, J. Introduction to artificial neural network (ANN) methods: what they are and how to use them. Acta Chim. Slov. 41, 327–327 (1994).
Lai, Y. & Dzombak, D. A. Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather Forecast. 35 (3), 959–976 (2020).
Owais, M. Preprocessing and postprocessing analysis for hot-mix asphalt dynamic modulus experimental data. Constr. Build. Mater. 450, 138693 (2024).
Owais, M. Analysing Witczak 1-37A, Witczak 1-40D and modified hirsch models for asphalt dynamic modulus prediction using global sensitivity analysis. Int. J. Pavement Eng. 24 (1), 2268808 (2023).
Orenc, S., Acar, E. & Özerdem, M. S. The Electricity Price Prediction of Victoria City Based on Various Regression Algorithms. In: Global Energy Conference (GEC). IEEE. (2022).
Gönenç, A. et al. Artificial Intelligence Based Regression Models for Prediction of Smart Grid Stability. in 2022 Global Energy Conference (GEC). IEEE. (2022).
Ruzgar, S. & Acar, E. The statistical neural network-based regression approach for prediction of optical band gap of CuO. Indian J. Phys. 96 (12), 3547–3557 (2022).
Kasemset, C., Sae-Haew, N. & Sopadang, A. Multiple regression model for forecasting quantity of supply of off-season Longan. CMU J. Nat. Sci. 13 (3), 391–402 (2014).
Lewis, C. D. Industrial and Business Forecasting Methods: A Practical Guide To Exponential Smoothing and Curve Fitting (No Title), 1982).
