Analysis of vehicle pedestrian crash severity using advanced machine learning techniques


  • Siyab Ul Arifeen Department of Civil Engineering, COMSATS University Islamabad, Abbottabad, Pakistan Author
  • Mujahid Ali Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Katowice Author
  • Elżbieta Macioszek Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Katowice Author



Machine learning, ANN, BNN, Vehicle-pedestrian crash


In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.


Abdel-Aty, M., Keller, J. (2005). Exploring the overall and specific crash severity levels at signalized intersections. Accident Analysis, 37, 417-425.

Aghaabbasi, M., Shekari, Z. A., Shah, M. Z., Olakunle, O., Armaghani, D. J., Moeinaddini, M. (2020). Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques. Transportation Research Part A: Policy, 136, 262-281.

Aghaabbasi, M., Ali, M., Jasiński, M., Leonowicz, Z., Novák, T. (2023). On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice. IEEE Access, 11, 19762-19774.

Al-Ghamdi, A. S. (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis, Prevention, 34, 729-741.

Al Mamlook, R. E., Abdulhameed, T. Z., Hasan, R., Al-Shaikhli, H. I., Mohammed, I., Tabatabai, S. (2020). Utilizing machine learning models to predict the car crash injury severity among elderly drivers. 2020 IEEE international conference on electro information technology (EIT). IEEE, 105-111.

Ali, M., Abbas, S., Salah, B., Akhter, J., Saleem, W., Haruna, S., Room, S., Abdulkadir, I. (2021). Investigating Optimal Confinement Behaviour of Low-Strength Concrete through Quantitative and Analytical Approaches. Materials, 14, 4675.

Ali, M., Hin Lai, S. (2023). Artificial intelligent techniques for prediction of rock strength and deformation properties – A review. Structures, 55, 1542-1555.

Ali, Y., Haque, M. M., Mannering, F. (2023). A Bayesian generalised extreme value model to estimate real-time pedestrian crash risks at signalised intersections using artificial intelligence-based video analytics. Analytic methods in accident research, 38, 100264.

Ali, M., Dharmowijoyo, D. B. E., De Azevedo, A. R. G., Fediuk, R., Ahmad, H., Salah, B. (2021). Time-Use and Spatio-Temporal Variables Influence on Physical Activity Intensity, Physical and Social Health of Travelers. Sustainability, 13, 12226.

Amoh-Gyimah, R., Aidoo, E. N., Akaateba, M. A., Appiah, S. K. (2017). The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana. International journal of injury control safety promotion, 24, 459-468.

Arifeen, S. U., Amin, M. N., Ahmad, W., Althoey, F., Ali, M., Alotaibi, B. S., Abuhussain, M. A. (2023). A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector. Construction and Building Materials, 407, 133485.

Astarita, V., Haghshenas, S. S., Guido, G., Vitale, A. (2023). Developing new hybrid grey wolf optimization-based artificial neural network for predicting road crash severity. Transportation Engineering, 12, 100164.

Aziz, H. A., Ukkusuri, S. V., Hasan, S. (2013). Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accident Analysis, Prevention, 50, 1298-1309.

Behnood, A., Mannering, F. L. (2016). An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models. Analytic Methods in Accident Research, 12, 1-17.

Chen, Y., Aghaabbasi, M., Ali, M., Anciferov, S., Sabitov, L., Chebotarev, S., Nabiullina, K., Sychev, E., Fediuk, R., Zainol, R. (2021). Hybrid Bayesian Network Models to Investigate the Impact of Built Environment Experience before Adulthood on Students’ Tolerable Travel Time to Campus: Towards Sustainable Commute Behavior. Sustainability, 14, 325.

Chen, Z. F. (2019). Modeling pedestrian injury severity in pedestrian-vehicle crashes in rural and urban areas: mixed logit model approach. Transportation research record 2673, 1023-1034.

Dai, D. (2012). Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment. Journal of Transport Geography, 24, 206-214.

Das, S., Le, M., Dai, B. (2020). Application of machine learning tools in classifying pedestrian crash types: A case study. Transportation Safety and Environment, 2, 106-119.

De Lavalette, B. C., Tijus, C., Poitrenaud, S., Leproux, C., Bergeron, J., Thouez, J.-P. (2009). Pedestrian crossing decision-making: A situational and behavioral approach. Safety science, 47, 1248-1253.

Ding, C., Chen, P., Jiao, J. (2018). Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. Accident Analysis, Prevention, 112, 116-126.

Faraz, M. I., Arifeen, S. U., Amin, M. N., Nafees, A., Althoey, F., Niaz, A. (2023). A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin. Elsevier, 937-948.

Gårder, P. E. (2004). The impact of speed and other variables on pedestrian safety in Maine. Accident Analysis, Prevention, 36, 533-542.

Guo, M., Yuan, Z., Janson, B., Peng, Y., Yang, Y., Wang, W. (2021). Older pedestrian traffic crashes severity analysis based on an emerging machine learning XGBoost. Sustainability, 13, 926.

Hafeez, F., Sheikh, U. U., Al-Shammari, S., Hamid, M., Khakwani, A. B. K., Arfeen, Z. A. (2023). Comparative analysis of influencing factors on pedestrian road accidents. Bulletin of Electrical Engineering and Informatics, 12, 257-267.

Hilakivi, I., Veilahti, J., Asplund, P., Sinivuo, J., Laitinen, L., Koskenvuo, K. (1989). A sixteen-factor personality test for predicting automobile driving accidents of young drivers. Accident Analysis, Prevention, 21, 413-418.

James, J. L., Kim, K. E. (1996). Restraint use by children involved in crashes in Hawaii, 1986–1991. Transportation research record, 1560, 8-12.

Jian, L., Lizhong, Y., Daoliang, Z. (2005). Simulation of bi-direction pedestrian movement in corridor. Physica A: Statistical Mechanics and its Applications, 354, 619-628.

Khuzan, T. S., Al-Jumaili, M. A. (2023). A review of studying the relationship of rural road accidents with geometric design. AIP Publishing.

Kim, D.-G., Washington, S., Oh, J. (2006). Modeling crash types: New insights into the effects of covariates on crashes at rural intersections. Journal of Transportation Engineering, 132, 282-292.

Kim, J.-K., Ulfarsson, G. F., Shankar, V. N., Mannering, F. L. (2010). A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accident Analysis, Prevention, 42, 1751-1758.

Koepsell, T., Mccloskey, L., Wolf, M., Moudon, A. V., Buchner, D., Kraus, J., Patterson, M. (2002). Crosswalk markings and the risk of pedestrian–motor vehicle collisions in older pedestrians. Jama, 288, 2136-2143.

Lee, S., Kim, S., Kim, J., Kim, D., Lee, D., Im, G., Yuk, H., Heo, T.-Y. (2023). Multiclass Classification by Various Machine Learning Algorithms and Interpretation of the Risk Factors of Pedestrian Accidents Using Explainable AI. Mathematical Problems in Engineering, 2023

Li, D., Ranjitkar, P., Zhao, Y., Yi, H., Rashidi, S. (2017). Analyzing pedestrian crash injury severity under different weather conditions. Traffic injury prevention, 18, 427-430

Li, Y., Ma, D., Zhu, M., Zeng, Z., Wang, Y. (2018). Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network. Accident Analysis, Prevention, 111, 354-363.

Liang, M., Li, S. (2022). Enhancing BNN structure learning of hybrid modeling strategy for free formulated mechanism complex systems. Journal of Process Control, 120, 44-67.

Liu, K., Yu, Q., Yuan, Z., Yang, Z., Shu, Y. (2021). A systematic analysis for maritime accidents causation in Chinese coastal waters using machine learning approaches. Ocean, Coastal Management, 213, 105859.

Liu, T., Liu, Y., Liu, J., Wang, L., Xu, L., Qiu, G., Gao, H. (2020). A Bayesian learning based scheme for online dynamic security assessment and preventive control. IEEE Transactions on Power Systems, 35, 4088-4099.

Lu, W., Liu, J., Fu, X., Yang, J., Jones, S. (2022). Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes. Accident Analysis, Prevention, 168, 106622.

Luke, R. (2023). Current and future trends in driver behaviour and traffic safety scholarship: an African research agenda. International journal of environmental research and public health, 20, 4290.

Ma, T., Aghaabbasi, M., Ali, M., Zainol, R., Jan, A., Mohamed, A. M., Mohamed, A. (2022). Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm. Sustainability, 14, 3395.

Mafi, S., Abdelrazig, Y., Doczy, R. (2018). Machine learning methods to analyze injury severity of drivers from different age and gender groups. Transportation research record, 2672, 171-183.

Mannering, F. L., Grodsky, L. L. (1995). Statistical analysis of motorcyclists' perceived accident risk. Accident Analysis, Prevention, 27, 21-31.

Marzban, C., Witt, A. (2001). A Bayesian neural network for severe-hail size prediction. Weather and Forecasting, 16, 600-610.;2.

Marzoug, R., Lakouari, N., Ez-Zahraouy, H., Téllez, B. C., Téllez, M. C., Villalobos, L. C. (2022). Modeling and simulation of car accidents at a signalized intersection using cellular automata. Physica A: Statistical Mechanics and its Applications, 589, 126599.

Maze, T. H., Agarwal, M., Burchett, G. (2006). Whether weather matters to traffic demand, traffic safety, and traffic operations and flow. Transportation research record, 1948, 170-176.

Mercier, C. R., Shelley, M. C., Rimkus, J. B., Mercier, J. M. (1997). Age and gender as predictors of injury severity in head-on highway vehicular collisions. Transportation Research Record, 1581, 37-46.

Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety science, 54, 27-37.

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, 1-11.

Nayeem, M. A., Hasan, A. S., Jalayer, M. (2023). Investigation of Young Pedestrian Crashes in School Districts of New Jersey Using Machine Learning Models. 250-264.

Olowosegun, A., Babajide, N., Akintola, A., Fountas, G., Fonzone, A. (2022). Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: The case of Scotland. Accident Analysis, Prevention, 169, 106610.

Pucher, J., Dijkstra, L. (2003). Promoting safe walking and cycling to improve public health: lessons from the Netherlands and Germany. American journal of public health, 93, 1509-1516.

Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E. E. (2021). Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model. Applied Sciences, 11, 11916.

Quddus, M. A., Noland, R. B., Chin, H. C. (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety research, 33, 445-462.

Rahimi, A., Azimi, G., Asgari, H., Jin, X, (2020). Injury severity of pedestrian and bicyclist crashes involving large trucks. International Conference on Transportation and Development. American Society of Civil Engineers Reston, VA, 110-122.

Saha, D., Dumbaugh, E. (2021). Use of a model-based gradient boosting framework to assess spatial and non-linear effects of variables on pedestrian crash frequency at macro-level. Journal of Transportation Safety, Security, 1-32.

Sattar, K., Chikh Oughali, F., Assi, K., Ratrout, N., Jamal, A., Masiur Rahman, S. (2023). Transparent deep machine learning framework for predicting traffic crash severity. Neural Computing and Applications, 35, 1535-1547.

Shankar, V., Mannering, F. (1996). An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. Journal of safety research, 27, 183-194.

Sze, N.-N., Wong, S. (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accident Analysis, Prevention, 39, 1267-1278.

Tao, W., Aghaabbasi, M., Ali, M., Almaliki, A. H., Zainol, R., Almaliki, A. A., Hussein, E. E. (2022). An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety. Sustainability, 14, 2436.

Tay, R., Choi, J., Kattan, L., Khan, A. (2011). A multinomial logit model of pedestrian–vehicle crash severity. International journal of sustainable transportation, 5, 233-249.

Tang, P., Aghaabbasi, M., Ali, M., Jan, A., Mohamed, A. M., Mohamed, A. (2022). How Sustainable Is People Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships. Sustainability, 14, 3989.

Theofilatos, A., Yannis, G. (2014). A review of the effect of traffic and weather characteristics on road safety. Accident Analysis, Prevention, 72, 244-256.

Vilaça, M., Silva, N., Coelho, M. C. (2017). Statistical analysis of the occurrence and severity of crashes involving vulnerable road users. Transportation research procedia, 27, 1113-1120.

Wang, H., Yeung, D.-Y. (2016). Towards Bayesian deep learning: A framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering 28, 3395-3408.

Wang, K., Zhang, W., Jin, L., Feng, Z., Zhu, D., Cong, H., Yu, H. (2022). Diagnostic analysis of environmental factors affecting the severity of traffic crashes: From the perspective of pedestrian–vehicle and vehicle–vehicle collisions. Traffic injury prevention, 23, 17-22.

WAng, J., Mohammed, A. S., Macioszek, E., Ali, M., Ulrikh, D. V., Fang, Q. (2022). A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance. Buildings, 12, 919.

Xia, J. S., Khabaz, M. K., Patra, I., Khalid, I., Alvarez, J. R. N., Rahmanian, A., Eftekhari, S. A., Toghraie, D. (2023)a. Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling. ISA transactions, 132, 353-363.

Xia, Y., Chen, H., Zimmermann, R. (2023)b. A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions. Travel behaviour and society, 30, 118-134.

Xie, Y., Lord, D., Zhang, Y. (2007). Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis. Accident Analysis, Prevention, 39, 922-933.

Xing, F., Huang, H., Zhan, Z., Zhai, X., Ou, C., Sze, N. N., Hon, K. K. (2019). Hourly associations between weather factors and traffic crashes: Non-linear and lag effects. Analytic methods in accident research, 24, 100109.

Yan, D., Zhou, Q., Wang, J., Zhang, N. (2017). Bayesian regularisation neural network based on artificial intelligence optimisation. International Journal of Production Research, 55, 2266-2287.

Yang, L., Aghaabbasi, M., Ali, M., Jan, A., Bouallegue, B., Javed, M. F., Salem, N. M. (2022). Comparative analysis of the optimized KNN, SVM, and ensemble DT models using Bayesian optimization for predicting pedestrian fatalities: an advance towards realizing the sustainable safety of pedestrians. Sustainability, 14, 10467.

Yasmin, S., Eluru, N. (2013). Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accident Analysis, Prevention, 59, 506-521.

Zajac, S. S., John (2003). Factors influencing injury severity of motor vehicle–crossing pedestrian crashes in rural Connecticut. Accident Analysis, Prevention, 35, 369-379.

Zeng, X., Yang, Z., Zhang, L., Tang, X., Zeng, Z., Liu, Z. (2023). Safety verification of nonlinear systems with Bayesian neural network controllers. 15278-15286.

Zhao, B., Zuniga-Garcia, N., Xing, L., Kockelman, K. M. (2023). Predicting pedestrian crash occurrence and injury severity in texas using tree-based machine learning models. Transportation Planning and Technology, 1-22.

Zhou, Z.-P., Liu, Y.-S., Wang, W., Zhang, Y. (2013). Multinomial logit model of pedestrian crossing behaviors at signalized intersections. Discrete Dynamics in Nature and Society, 2013.

Zhu, S. (2022). Analyse vehicle–pedestrian crash severity at intersection with data mining techniques. International journal of crashworthiness, 27, 1374-1382.






Original articles

How to Cite

Ul Arifeen, S., Ali, M., & Macioszek, E. (2023). Analysis of vehicle pedestrian crash severity using advanced machine learning techniques. Archives of Transport, 68(4), 91-116.


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