Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 15, 2023 - Issue 9
572
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Comparison of traffic accident injury severity prediction models with explainable machine learning

, , &
Pages 1043-1054 | Received 27 Apr 2022, Accepted 12 May 2023, Published online: 17 May 2023

References

  • Abdelwahab, H. T., and M. A. Abdel-Aty. 2001. “Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections.” Transportation Research Record 1746 (1): 6–13. doi:10.3141/1746-02.
  • Bucsuházy, K., E. Matuchová, R. Zůvala, P. Moravcová, M. Kostíková, and R. Mikulec. 2020. “Human Factors Contributing to the Road Traffic Accident Occurrence.” Transportation Research Procedia 45: 555–561. doi:10.1016/j.trpro.2020.03.057.
  • Chen, T., M. Wu, and C. Zhang. 2019. “Fatalıty Rate Analysıs of Major and Extraordınary Road Traffıc Accıdents Based on C4. 5 Decısıon Tree Algorıthm.” Part B, Applications: An İ̇nternational Journal of Research and Surveys 10 (7): 619–625.
  • Chen, C., G. Zhang, Z. Qian, R. A. Tarefder, and Z. Tian. 2016. “Investigating Driver Injury Severity Patterns in Roll Over Crashes Using Support Vector Machine Models.” Accident Analysis & Prevention 90: 128–139. doi:10.1016/j.aap.2016.02.011.
  • Cortes, C., and V. Vapnik. 1995. “Support Vector Machine.” Machine Learning 20 (3): 273–297. doi:10.1007/BF00994018.
  • Elassad, Z. E. A., H. Mousannif, and H. Al Moatassime. 2020. “Class-Imbalanced Crash Prediction Based on Real-Time Traffic and Weather Data: A Driving Simulator Study.” Traffic Injury Prevention 21 (3): 201–208. doi:10.1080/15389588.2020.1723794.
  • Farid, D. M., L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan. 2014. “Hybrid Decision Tree and Naïve Bayes Classifiers for Multi-Class Classification Tasks.” Expert Systems with Applications 41 (4): 1937–1946. doi:10.1016/j.eswa.2013.08.089.
  • Fenta, H. M., and D. L. Workie. 2014. “Analysis of Factors That Affect Road Traffic Accidents in Bahir Dar City, North Western Ethiopia.” Science Journal of Applied Mathematics and Statistics 2 (5): 91–96. doi:10.11648/j.sjams.20140205.11.
  • Fountas, G., A. Fonzone, N. Gharavi, and T. Rye. 2020. “The Joint Effect of Weather and Lighting Conditions on Injury Severities of Single-Vehicle Accidents.” Analytic Methods in Accident Research 27: 100124. doi:10.1016/j.amar.2020.100124.
  • González-Sánchez, G., E. Maeso-González, M. I. Olmo-Sánchez, M. Gutiérrez-Bedmar, A. Mariscal, and A. García-Rodríguez. 2018. “Road Traffic Injuries, Mobility and Gender. Patterns of Risk in Southern Europe.” Journal of Transport & Health 8: 35–43. doi:10.1016/j.jth.2017.11.147.
  • Hashmienejad, S. H. A., and S. M. H. Hasheminejad. 2017. “Traffic Accident Severity Prediction Using a Novel Multi-Objective Genetic Algorithm.” International Journal of Crashworthiness 22 (4): 425–440. doi:10.1080/13588265.2016.1275431.
  • Hyung, W. G., S. Kim, and J. K. Jo. 2019. “Improved Similarity Measure in Case-Based Reasoning: A Casestudy of Construction Cost Estimation.” Engineering, Construction & Architectural Management 27 (2): 561–578. doi:10.1108/ECAM-01-2019-0035.
  • Iqbal, A., Z. U. Rehman, S. Ali, K. Ullah, and U. Ghani. 2020. “Road Traffic Accident Analysis and Identification of Black Spot Locations on Highway.” Civil Engineering Journal 6 (12): 2448–2456. doi:10.28991/cej-2020-03091629.
  • Katanalp, B. Y., and E. Eren. 2020. “The Novel Approaches to Classify Cyclist Accident Injury-Severity: Hybrid Fuzzy Decision Mechanisms.” Accident Analysis & Prevention 144: 105590. doi:10.1016/j.aap.2020.105590.
  • Kelleher, J. D., B. Mac Namee, and A. D’Arcy. 2015. Fundamentals of Machine Learningforpredictivedataanalytics: Algorithms, Workedexamples, Andcasestudies (First). Cambridge: MIT Press.
  • Kumeda, B., F. Zhang, F. Zhou, S. Hussain, A. Almasri, and M. Assefa 2019, June. Classification of Road Traffic Accident Data Using Machine Learning Algorithms. In 2019 IEEE 11th international conference on communication software and networks (ICCSN), Chongqing, China, 682–687. IEEE.
  • Lee, J., T. Yoon, S. Kwon, and J. Lee. 2019. “Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study.” Applied Sciences 10 (1): 129. doi:10.3390/app10010129.
  • Lei, L., M. Xuelei, and W. Qiang. 2014. “Research on Emergency Decision Making for Railway Traffic Accidents Based on Case-Based Reasoning.” Journal of the China Railway Society 36 (11): 1–6.
  • Lundberg, S. M., and S. I. Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30: 4765–4774.
  • NHTSA. 2022. Traffic Safety Checks. January 14, 2023, from https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813406
  • Nitsche, P., P. Thomas, R. Stuetz, and R. Welsh. 2017. “Pre-Crashscenarios at Road Junctions: A Clustering Method for Car Crash Data.” Accident Analysis &prevention 107: 137–151. doi:10.1016/j.aap.2017.07.011.
  • NSC, (2022) Retrieved November 25, 2022, from https://injuryfacts.nsc.org/all-injuries/costs/societal-costs/data-details/
  • Obeng, K. 2011. “Gender Differences in Injury Severity Risks in Crashes at Signalized Intersections.” Accident Analysis & Prevention 43 (4): 1521–1531. doi:10.1016/j.aap.2011.03.004.
  • Pedregosa, F. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (Nov): 2825–2830.
  • Roscher, R., B. Bohn, M. F. Duarte, and J. Garcke. 2020. “Explainable Machine Learning for Scientific Insights and Discoveries.” IEEE Access 8: 42200–42216. doi:10.1109/ACCESS.2020.2976199.
  • Sameen, M. I., and B. Pradhan. 2017. “Severity Prediction of Traffic Accidents with Recurrent Neural Networks.” Applied Sciences 7 (6): 476. doi:10.3390/app7060476.
  • Sammut, C., and G. I. Webb, Eds. 2011. Encyclopedia of Machine Learning. New York, USA: Springer.
  • Santos, D., J. Saias, P. Quaresma, and V. B. Nogueira. 2021. “Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction.” Computers 10 (12): 157. doi:10.3390/computers10120157.
  • Sapri, F. E., N. S. Nordin, S. M. Hasan, W. F. W. Yaacob, and S. A. M. Nasir. 2017. “Decision Tree Model for Non-Fatal Road Accident Injury.” International Journal on Advanced Science, Engineering and Information Technology 7 (1): 63–70. doi:10.18517/ijaseit.7.1.1110.
  • Shamsashtiany, R., and M. Ameri. 2018. “Road Accidents Prediction with Multilayer Perceptron ANN-MLP Modelling Case Study: Roads of Qazvin, Zanjan and Hamadan.” Journal of Civil Engineering and Materials Application 2 (4): 181–192.
  • Shannon, C. E. 1948. “A Mathematical Theory of Communication.” Bell System Technical Journal 27 (3): 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x.
  • Sonmez, R., and F. Uysal. 2021. “Discussion of “Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-Of-The-Art Review” by Haytham H. Elmousalami.” Journal of Construction Engineering and Management 147 (6): 07021001. doi:10.1061/(ASCE)CO.1943-7862.0002048.
  • Taamneh, M., S. Alkheder, and S. Taamneh. 2017. “Data-Mining Techniques for Traffic Accident Modeling and Prediction in the United Arab Emirates.” Journal of Transportation Safety & Security 9 (2): 146–166. doi:10.1080/19439962.2016.1152338.
  • Tercan, E., E. Beşdok, and S. Tapkın. 2021. “Heuristic Modelling of Traffic Accident Characteristics.” Transportation Letters 13 (7): 522–530. doi:10.1080/19427867.2020.1734273.
  • Theofilatos, A., D. Graham, and G. Yannis. 2012. “Factors Affecting Accident Severity Inside and Outside Urban Areas in Greece.” Traffic Injury Prevention 13 (5): 458–467. doi:10.1080/15389588.2012.661110.
  • Valderrama-Zurián, J. C., D. Melero-Fuentes, F. J. Álvarez, and F. Herrera-Gómez. 2020. “World Wide Research Output Trends on Drinking and Driving from 1956 to 2015.” Accident Analysis & Prevention 135: 105364. doi:10.1016/j.aap.2019.105364.
  • Vapnik, V. N. 1999. “An Overview of Statistical Learning Theory.” IEEE Transactions on Neural Networks 10 (5): 988–999. doi:10.1109/72.788640.
  • Venkataraman, N., G. F. Ulfarsson, and V. N. Shankar. 2013. “Random Parameter Models of Interstate Crash Frequencies by Severity, Number of Vehicles Involved, Collision and Location Type.” Accident Analysis & Prevention 59: 309–318. doi:10.1016/j.aap.2013.06.021.
  • Wu, W., S. Jiang, R. Liu, W. Jin, and C. Ma. 2020. “Economic Development, Demographic Characteristics, Road Network and Traffic Accidents in Zhongshan, China: Gradient Boosting Decision Tree Model.” Transportmetrica A: Transport Science 16 (3): 359–387. doi:10.1080/23249935.2020.1711543.
  • Xiaoyun, Z., J. Xianlong, C. Xianghai, and H. Xinyi. 2011. “The Simulation and Optimization Integration Calculation Method and Application Validation for the Traffic Accident.” Advances in Engineering Software 42 (6): 387–397. doi:10.1016/j.advengsoft.2011.03.006.
  • Xi, J., Z. Zhao, W. Li, and Q. Wang. 2016. “A Traffic Accident Causation Analysis Method Based on AHP-Apriori.” Procedia Engineering 137: 680–687. doi:10.1016/j.proeng.2016.01.305.
  • Yau, K. K. 2004. “Risk Factorsaffectingtheseverity of Singlevehicletrafficaccidents in Hong Kong.” Accident Analysis & Prevention 36 (3): 333–340. doi:10.1016/S0001-4575(03)00012-5.
  • Zeng, Q., and H. Huang. 2014. “A Stable and Optimized Neural Network Model for Crash Injury Severity Prediction.” Accident Analysis & Prevention 73: 351–358. doi:10.1016/j.aap.2014.09.006.
  • Zohdy, I., and H. A. Rakha. 2012. “Framework for Intersection Decision Support in Adverse Weather Conditions: Use of Case-Based Reasoning Algorithm.” Transportation Research Record 2324 (1): 20–28. doi:10.3141/2324-03.
  • Zou, Q., S. Xie, Z. Lin, M. Wu, and Y. Ju. 2016. “Finding the Best Classification Threshold in Imbalanced Classification.” Big Data Research 5: 2–8. doi:10.1016/j.bdr.2015.12.001.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.