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Research Articles

Investigating the effect of road condition and vacation on crash severity using machine learning algorithms

ORCID Icon, ORCID Icon, &
Pages 392-402 | Received 13 Dec 2022, Accepted 10 Apr 2023, Published online: 20 Apr 2023

References

  • Abdelwahab, H. T., & Abdel-Aty, M. A. (2001). Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 1746(1), 6–13. https://doi.org/10.3141/1746-02
  • Abdulhafedh, A. (2017). Road crash prediction models: Different statistical modeling approaches. Journal of Transportation Technologies, 07(02), 190–205. https://doi.org/10.4236/jtts.2017.72014
  • Abellán, J., López, G., & de Oña, J. (2013). Analysis of traffic accident severity using decision rules via decision trees. Expert Systems with Applications, 40(15), 6047–6054. https://doi.org/10.1016/j.eswa.2013.05.027
  • Ahmadi, A., Jahangiri, A., Berardi, V., & Machiani, S. G. (2020). Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods. Journal of Transportation Safety & Security, 12(4), 522–546. https://doi.org/10.1080/19439962.2018.1505793
  • Ahmed, M. M., Franke, R., Ksaibati, K., & Shinstine, D. S. (2018). Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models. Accident; Analysis and Prevention, 117, 106–113. https://doi.org/10.1016/j.aap.2018.04.011
  • Ali, M. (2020). PyCaret: An open source, low-code machine learning library in Python (Vol. 2).
  • Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting, 36(1), 100–108. https://doi.org/10.1002/for.2425
  • Al-Naami, M. Y., Arafah, M. A., & Al-Ibrahim, F. S. (2010). Trauma care systems in Saudi Arabia: An agenda for action. Annals of Saudi Medicine, 30(1), 50–58. https://doi.org/10.5144/0256-4947.59374
  • Alogaili, A., & Mannering, F. (2020). Unobserved heterogeneity and the effects of driver nationality on crash injury severities in Saudi Arabia. Accident; Analysis and Prevention, 144, 105618. https://doi.org/10.1016/j.aap.2020.105618
  • Amiri, A. M., Sadri, A., Nadimi, N., & Shams, M. (2020). A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed object crashes among elderly drivers. Accident; Analysis and Prevention, 138, 105468. https://doi.org/10.1016/j.aap.2020.105468
  • Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident; Analysis and Prevention, 41(3), 359–364. https://doi.org/10.1016/j.aap.2008.12.014
  • Arhin, S. A., & Gatiba, A. (2020). Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers. Transportation Safety and Environment, 2(2), 120–132. https://doi.org/10.1093/tse/tdaa012
  • Assemi, B., & Hickman, M. (2018). Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity. Transportation Research Part A: Policy and Practice, 113, 441–459. https://doi.org/10.1016/j.tra.2018.04.018
  • Babič, F., & Zuskáčová, K. (2016). Descriptive and predictive mining on road accidents data. In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI).
  • Budiawan, W., Saptadi, S., Tjioe, C. Phommachak., & T., Sriyanto. (2019). Traffic accident severity prediction using naive Bayes algorithm - A case study of Semarang toll road. IOP Conference Series: Materials Science and Engineering, 598(1), 012089. https://doi.org/10.1088/1757-899X/598/1/012089
  • Castro, Y., & Kim, Y. J. (2016). Data mining on road safety: Factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness, 21(2), 104–111. https://doi.org/10.1080/13588265.2015.1122278
  • Chang, L.-Y., & Chien, J.-T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17–22. https://doi.org/10.1016/j.ssci.2012.06.017
  • Chang, F., Xu, P., Zhou, H., Chan, A. H. S., & Huang, H. (2019). Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident; Analysis and Prevention, 131, 316–326. https://doi.org/10.1016/j.aap.2019.07.012
  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
  • Chen, F., Song, M., & Ma, X. (2019). Investigation on the injury severity of drivers in rear-end collisions between cars using a random parameters bivariate ordered probit model. International Journal of Environmental Research and Public Health, 16(14), 2632. https://doi.org/10.3390/ijerph16142632
  • Chen, C., Xiang, H., Qiu, T., Wang, C., Zhou, Y., & Chang, V. (2018). A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles. Journal of Parallel and Distributed Computing, 117, 192–204. https://doi.org/10.1016/j.jpdc.2017.08.014
  • Chen, C., Zhang, G., Huang, H., Wang, J., & Tarefder, R. A. (2016). Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model. Accident; Analysis and Prevention, 96, 79–87. https://doi.org/10.1016/j.aap.2016.06.015
  • Chen, C., Zhang, G., Tarefder, R., Ma, J., Wei, H., & Guan, H. (2015). A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accident; Analysis and Prevention, 80, 76–88. https://doi.org/10.1016/j.aap.2015.03.036
  • Dahim, M. A. H. (2018). Impact of vision 2030 on traffic safety in Saudi Arabia. International Journal of Pediatrics & Adolescent Medicine, 5(3), 103–109. https://doi.org/10.1016/j.ijpam.2018.08.002
  • Das, S., Dutta, A., & Sun, X. (2020). Patterns of rainy weather crashes: Applying rules mining. Journal of Transportation Safety & Security, 12(9), 1083–1105. https://doi.org/10.1080/19439962.2019.1572681
  • de Oña, J., López, G., & Abellán, J. (2013). Extracting decision rules from police accident reports through decision trees. Accident; Analysis and Prevention, 50, 1151–1160. https://doi.org/10.1016/j.aap.2012.09.006
  • Dong, N., Huang, H., & Zheng, L. (2015). Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects. Accident; Analysis and Prevention, 82, 192–198. https://doi.org/10.1016/j.aap.2015.05.018
  • Dong, B., Ma, X., & Chen, F. (2018). Analyzing the injury severity sustained by non-motorists at mid-blocks considering non-motorists’ pre-crash behavior. Transportation Research Record: Journal of the Transportation Research Board, 2672(38), 138–148. https://doi.org/10.1177/0361198118777354
  • Dong, C., Shao, C., Li, J., & Xiong, Z. (2018). An improved deep learning model for traffic crash prediction. Journal of Advanced Transportation, 2018, 1–13. https://doi.org/10.1155/2018/3869106
  • Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. The Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
  • Fabian Pedregosa, G. V., Gramfort, A., Michel, V., ¨ Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
  • Fiorentini, N., & Losa, M. (2020). Handling imbalanced data in road crash severity prediction by machine learning algorithms. Infrastructures, 5(7), 61. https://doi.org/10.3390/infrastructures5070061
  • Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463–484. https://doi.org/10.1109/TSMCC.2011.2161285
  • Global Health Observatory Data Repository. (2021). https://apps.who.int/gho/data/view.main.51310?lang=en.
  • Global Status Report on Road Safety. (2018).
  • Hasan, A. S., Kabir, M. A. B., Jalayer, M., & Das, S. (2022). Severity modeling of work zone crashes in New Jersey using machine learning models. Journal of Transportation Safety & Security, 1–32.https://doi.org/10.1080/19439962.2022.2098442
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer. Series in Statistics. Springer, p. 745.
  • Hosseinzadeh, A., Moeinaddini, A., & Ghasemzadeh, A. (2021). Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model. Journal of Safety Research, 77, 151–160. https://doi.org/10.1016/j.jsr.2021.02.012
  • Human Development Report. (2020).
  • Iranitalab, A., & Khattak, A. (2017). Comparison of four statistical and machine learning methods for crash severity prediction. Accident; Analysis and Prevention, 108, 27–36. https://doi.org/10.1016/j.aap.2017.08.008
  • Jamal, A., Zahid, M., Tauhidur Rahman, M., Al-Ahmadi, H. M., Almoshaogeh, M., Farooq, D., & Ahmad, M. (2021). Injury severity prediction of traffic crashes with ensemble machine learning techniques: A comparative study. International Journal of Injury Control and Safety Promotion, 28(4), 408–427.
  • Japkowicz, N. (2000). The class imbalance problem: Significance and strategies. In Proceedings of the 2000 International Conference on Artificial Intelligence ICAI.
  • Jeong, H., Jang, Y., Bowman, P. J., & Masoud, N. (2018). Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data. Accident; Analysis and Prevention, 120, 250–261. https://doi.org/10.1016/j.aap.2018.08.025
  • Jiang, L., Xie, Y., Wen, X., & Ren, T. (2022). Modeling highly imbalanced crash severity data by ensemble methods and global sensitivity analysis. Journal of Transportation Safety & Security, 14(4), 562–584. https://doi.org/10.1080/19439962.2020.1796863
  • Komol, M. M. R., Hasan, M. M., Elhenawy, M., Yasmin, S., Masoud, M., & Rakotonirainy, A. (2021). Crash severity analysis of vulnerable road users using machine learning. PloS One, 16(8), e0255828. https://doi.org/10.1371/journal.pone.0255828
  • Kunt, M. M., Aghayan, I., & Noii, N. (2012). Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), 353–366. https://doi.org/10.3846/16484142.2011.635465
  • Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident; Analysis and Prevention, 45, 478–486. https://doi.org/10.1016/j.aap.2011.08.016
  • Lin, C., Wu, D., Liu, H., Xia, X., & Bhattarai, N. (2020). Factor identification and prediction for teen driver crash severity using machine learning: A case study. Applied Sciences, 10(5), 1675. https://doi.org/10.3390/app10051675
  • Li, L., Shrestha, S., & Hu, G. (2017). Analysis of road traffic fatal accidents using data mining techniques [Paper presentation]. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). https://doi.org/10.1109/SERA.2017.7965753
  • Lu, P., Zheng, Z., Ren, Y., Zhou, X., Keramati, A., Tolliver, D., & Huang, Y. (2020). A gradient boosting crash prediction approach for highway-rail grade crossing crash analysis. Journal of Advanced Transportation, 2020, 1–10. https://doi.org/10.1155/2020/6751728
  • Ma, C., Hao, W., Xiang, W., & Yan, W. (2018). The impact of aggressive driving behavior on driver-injury severity at highway-rail grade crossings accidents. Journal of Advanced Transportation, 2018, 1–10.
  • Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1, 1–22. https://doi.org/10.1016/j.amar.2013.09.001
  • Mannering, F. L., Shankar, V., & Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16. https://doi.org/10.1016/j.amar.2016.04.001
  • Mansuri, F. A., Al-Zalabani, A. H., Zalat, M. M., & Qabshawi, R. I. (2015). Road safety and road traffic accidents in Saudi Arabia. Saudi Medical Journal, 36(4), 418–424. https://doi.org/10.15537/smj.2015.4.10003
  • Matthew, G., & Karlaftis, I. G. (2002). Effects of road geometry and traffic volumes on rural roadway accident rates. Accident; Analysis and Prevention, 34(3), 357–365. https://doi.org/10.1016/s0001-4575(01)00033-1
  • Mauro, R., Luca, M. D., & Dell’Acqua, G. (2013). Using a K-means clustering algorithm to examine patterns of vehicle crashes in before-after analysis. Modern Applied Science, 7(10) https://doi.org/10.5539/mas.v7n10p11
  • Mesa-Arango, R., Valencia-Alaix, V. G., Pineda-Mendez, R. A., & Eissa, T. (2018). Influence of socioeconomic conditions on crash injury severity for an urban area in a developing country. Transportation Research Record: Journal of the Transportation Research Board, 2672(31), 41–53. https://doi.org/10.1177/0361198118758684
  • Mohamed, A., & Abdel-Aty, H. T. A. (2004). Predicting injury severity levels in traffic crashes: A modeling comparison. Journal of Transportation Engineering, 130(2), 204–210. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:2(204)
  • Mokhtarimousavi, S., Anderson, J. C., Azizinamini, A., & Hadi, M. (2019). Improved support vector machine models for work zone crash injury severity prediction and analysis. Transportation Research Record: Journal of the Transportation Research Board, 2673(11), 680–692. https://doi.org/10.1177/0361198119845899
  • Mondal, A. R., Bhuiyan, M. A. E., & Yang, F. (2020). Advancement of weather-related crash prediction model using nonparametric machine learning algorithms. SN Applied Sciences, 2(8), 1372. https://doi.org/10.1007/s42452-020-03196-x
  • Moradkhani, F., Ebrahimkhani, S., & Sadeghi Begham, B. (2014). Road accident data analysis: A data mining approach. Indian Journal of Scientific Research, 3, 437–443.
  • Nguyen, H., Cai, C., & Chen, F. (2017). Automatic classification of traffic incident’s severity using machine learning approaches. IET Intelligent Transport Systems, 11(10), 615–623. https://doi.org/10.1049/iet-its.2017.0051
  • Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident; Analysis and Prevention, 136, 105405. https://doi.org/10.1016/j.aap.2019.105405
  • Pradhan, B., & Sameen, M. I. (2020). Predicting injury severity of road traffic accidents using a hybrid extreme gradient boosting and deep neural network approach. In Laser scanning systems in highway and safety assessment (pp. 119–127).
  • Prati, G., Pietrantoni, L., & Fraboni, F. (2017). Using data mining techniques to predict the severity of bicycle crashes. Accident; Analysis and Prevention, 101, 44–54. https://doi.org/10.1016/j.aap.2017.01.008
  • Road traffic injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries.
  • Saher System. (2022). Shorturl.at/bfCS6.
  • Sameen, M. I., & Pradhan, B. (2017). Severity prediction of traffic accidents with recurrent neural networks. Applied Sciences, 7(6), 476. https://doi.org/10.3390/app7060476
  • Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2020). Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data. Safety Science, 125, 104616. https://doi.org/10.1016/j.ssci.2020.104616
  • Secretariat, G. (2022). Traffic safety record for the year 2022. https://mcts.gov.sa.
  • Shao, X., Ma, X., Chen, F., Song, M., Pan, X., & You, K. (2020). A random parameters ordered probit analysis of injury severity in truck involved rear-end collisions. International Journal of Environmental Research and Public Health, 17(2), 395
  • Shi, Q., & Abdel-Aty, M. (2015). Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies, 58, 380–394. https://doi.org/10.1016/j.trc.2015.02.022
  • Tang, J., Liang, J., Han, C., Li, Z., & Huang, H. (2019). Crash injury severity analysis using a two-layer Stacking framework. Accident; Analysis and Prevention, 122, 226–238. https://doi.org/10.1016/j.aap.2018.10.016
  • Tang, J., Liu, F., Zou, Y., Zhang, W., & Wang, Y. (2017). An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2340–2350. https://doi.org/10.1109/TITS.2016.2643005
  • Tavakoli Kashani, A., Rabieyan, R., & Besharati, M. M. (2014). A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers. Journal of Safety Research, 51, 93–98. https://doi.org/10.1016/j.jsr.2014.09.004
  • Traffic Accidents: Their Heavy Costs. (2013). https://saudigazette.com.sa/article/58113.
  • Violations & Penalties. (2022). Shorturl.at/ckuQS.
  • Wahab, L., & Jiang, H. (2019). A comparative study on machine learning based algorithms for prediction of motorcycle crash severity. PloS One, 14(4), e0214966. https://doi.org/10.1371/journal.pone.0214966
  • Wahab, L., & Jiang, H. (2020). Severity prediction of motorcycle crashes with machine learning methods. International Journal of Crashworthiness, 25(5), 485–492. https://doi.org/10.1080/13588265.2019.1616885
  • Wang, J., Huang, H., Xu, P., Xie, S., & Wong, S. C. (2020). Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security, 12(6), 818–837. https://doi.org/10.1080/19439962.2018.1551257
  • Wang, S., Minku, L. L., & Yao, X. (2015). Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 27(5), 1356–1368. https://doi.org/10.1109/TKDE.2014.2345380
  • Waseem, M., Ahmed, A., & Saeed, T. U. (2019). Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident; Analysis and Prevention, 123, 12–19. https://doi.org/10.1016/j.aap.2018.10.022
  • Wen, X., Xie, Y., Jiang, L., Pu, Z., & Ge, T. (2021). Applications of machine learning methods in traffic crash severity modelling: Current status and future directions. Transport Reviews, 41(6), 855–879. https://doi.org/10.1080/01441647.2021.1954108
  • Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident; Analysis and Prevention, 159, 106261. https://doi.org/10.1016/j.aap.2021.106261
  • Yu, R., & Abdel-Aty, M. (2014). Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety Science, 63, 50–56. https://doi.org/10.1016/j.ssci.2013.10.012
  • Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident; Analysis and Prevention, 73, 351–358. https://doi.org/10.1016/j.aap.2014.09.006
  • Zhang, J., Li, Z., Pu, Z., & Xu, C. (2018). Comparing prediction performance for crash injury severity among various machine learning and statistical methods. IEEE Access, 6, 60079–60087. https://doi.org/10.1109/ACCESS.2018.2874979
  • Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., Cui, Z., & Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910. https://doi.org/10.1109/ACCESS.2019.2903319
  • Zhou, H., Yuan, C., Dong, N., Wong, S. C., & Xu, P. (2020). Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries. Journal of Safety Research, 74, 55–69. https://doi.org/10.1016/j.jsr.2020.04.003
  • Zichu, Z., Fanyu, M., Cancan, S., Richard, T., Zhongyin, G., Lili, Y., & Weili, W. (2021). Factors associated with consecutive and non-consecutive crashes on freeways: A two-level logistic modeling approach. Accident; Analysis and Prevention, 154, 106054. https://doi.org/10.1016/j.aap.2021.106054

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