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Modelling of Traffic Accident Severity in Jordan Using Machine Learning

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The quantity and range of research examining the severity of injuries in road accidents have expanded dramatically in recent years. This research investigates the relationships between injury severity levels and their causes. It develops new effective countermeasures that may contribute to reducing the severity of injuries caused by accidents using Machine learning - Random Forest (RF) and Logistic Regression- to model injury severity of traffic accidents in Jordan through R programming language. According to Gini's Improvement Index (GII), the most significant is Accident Primary Type (APT), which has a 16% improvement index. Still, according to the P-value, all variables were insignificant. Random forest of a tree-based classification and regression methodology applied to data with 500 trees has the advantage of diminishing overfitting without affecting prediction accuracy; the number of variables tried at each split is two, and the split between them is two. Results of RF models show that the mean age of drivers was 34 years, where the highest percentage among the causes of accidents was "violation of traffic rules" with 42%, while the "abrupt/sudden lane change" was the lowest at 11%. Multinomial Logistic (MNL) Regression results found that factors such as Age, Gender, Accident Max Speed (AMS), Weekday (W), Accident Primary Type (APT), Road Surface (RS), Weather Status (WS), and Manufacturing Year (MY) are all negatively related to crash severity at all levels (Property Damage Only (PDO), minor and major injury). In contrast, the rest of the factors, like Vehicle Registered Type (VRT), Road Lane Number (RLN), and Lighting Status (LS), are positively related at all levels. As for fatal accidents, the vehicle registered type has the highest odds ratio (36.943). Furthermore, the relative risk was substantially related to several lanes (one-lane, two-lane, three-lane) at (0.173, 5.441, 1.302), respectively.
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Traffic Crashes; Severity; Jordan; Machine Learning; Logistic Regression; Minor and Major Injuries; Property Damage Only; Random Forest

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National Highway Traffic Safety Administration (NHTSA), Traffic Safety Facts, 2022. Retrieved from:

World Health Organization (WHO), Global Status Report on Road Safety 2018. Retrieved from:

S. Chen, M. Kuhn, K. Prettner, and D. E Bloom, The Global Macroeconomic Burden of Road Injuries: Estimates and Projections for 166 Countries, The Lancet Planetary Health, Vol. 3(Issue 9): e390-e398, September 2019.

Jordan Traffic Safety Institute (2020), Traffic Accidents in Jordan, Annual Reports 2020.

N. Casado-Sanz, B. Guirao, and M. Attard, Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver's Perspective, Sustainability, Vol. 12(Issue 6): 2237, March 2020.

M. E. Hossain and M. U. Zaman, Analyzing The Factors Influencing Road Traffic Accident Severity: A Case Study of Khulna City, Plan Plus, Vol. 11(Issue 1), December 2021.

K. Wang, W. Zhang, L. Jin, Z. Feng, D. Zhu, H. Cong, and H. Yu, Diagnostic Analysis of Environmental Factors Affecting the Severity of Traffic Crashes: From The Perspective of Pedestrian-Vehicle and Vehicle-Vehicle Collisions, Traffic injury prevention, Vol. 23(Issue 1): 17-22. November 2021.

A. Jaber, J. Juhász, and B. Csonka, An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model, Sustainability, Vol. 13(Issue: 12): 6945, June 2021.

M. Guo, X. Zhao, Y. Yao, P. Yan, Y. Su, C. Bi, and D. Wu , A Study of Freeway Crash Risk Prediction and Interpretation Based on Risky Driving Behavior and Traffic Flow Data, Accident Analysis & Prevention, Vol. 160: 106328, September 2021.

M. Khattak, H. Backer, P. Winne, T. Brijs, and A. Pirdavani, Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression, International Conference on Transportation and Development 2022, 2022.

S. Erdoğan, M. Dereli, and H. Şenol, A GIS-Based Assessment of Long-Term Traffic Accidents Using Spatiotemporal and Empirical Bayes Analysis in Turkey, Applied Geomatics, Vol. 14(Issue 2): 147-162, February 2022.

Z. Ma, G. Mei, and S. Cuomo, An Analytic Framework Using Deep Learning for Prediction of Traffic Accident Injury Severity Based on Contributing Factors, Accident Analysis & Prevention, Vol. 160: 106322, September 2021.

U. Gazder, A. Ahmed, and U. Shahid, Predicting Severity of Accidents in Malaysia by Ordinal Logistic Regression Models, International Journal of Traffic and Transportation Management, Vol. 3(Issue 1): 11-16, March 2021.

U. Mayorathan and R. Malmarugan, Demographic Factors, Patterns, and Trends of Deaths Following Road Traffic Accidents In The Northern Sri Lanka, Jaffna Medical Journal, Vol. 34(Issue 1): 22-5, August 2022.

N. Alkofahi, T. Khedaywi, Trends and Modeling of Traffic Accidents in Jordan. International Journal of Engineering and Technology, Vol. 11(Issue 6): 1166-1181, December 2019-January 2020.

H. Khawaldah, N. Alzboun, and O. Dayafleh, Traffic Accidents in Jordan and Jordanian Youth's Perceptions of Their Consequences, Causes, and Reduction Methods, SSRN Electronic Journal: 1-20, August 2022.

M. Khasawneh, A. Al-Omari, B. Ganam, Forecasting Traffic Accidents in Jordan Using Regression Techniques. Jordan Journal of Civil Engineering, Vol. 12 (Issue4): 570- 579, September 2018.

X. Liu, D. Wu, G. K. Zewdie, L. Wijerante, C. I. Timms, A. Riley, E. Levetin, and D. J. Lary, Using Machine Learning to Estimate Atmospheric Ambrosia Pollen Concentrations in Tulsa, OK, Environ Health Insights, Vol. 11: 117863021769939, March 2017.

Y. Vorobeychik and M. Kantarcioglu, Adversarial Machine Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 12(Issue 3): 1-169, August. 2018.

A. Basuchoudhary, J. Bang, and T. Sen, Machine-Learning Techniques in Economics: New Tools for Predicting Economic Growth. Springer, 2017.

Schonlau, M., & Zou, R. Y, The Random Forest Algorithm for Statistical Learning. The Stata Journal, Vol. 20(Issue 1): 3-29, 2020.

C. Moore and A. Murphy, Random Forest (machine learning), Reference article,, June 2019.

G. ziarh, S. Shahis, T. Ismail, M. Asaduzzaman, A. Dewan, Correcting Bias of Satellite Rainfall Data Using Physical Empirical Model. Atmospheric Research, Vol. 251(Issue 1): 105430, April 2021.

A, Sharma, Random Forest vs. Decision Tree | Which Is Right for You?, Analytics Vidhya, March, 2023. Retrieved from:

Statista, Jordan: Total population from 2018 to 2028.

Department of statistics, 2019, Page 127&129.

Public Security Directorate, Jordan Traffic Institute, Traffic Accidents in Jordan 2013, 2014.

D. Hermawan, M. Fatihah, L. Kurniawati, and A. Helen, Comparative Study of J48 Decision Tree Classification Algorithm, Random Tree, and Random Forest on in-Vehicle Coupon Recommendation Data, 2021 International Conference on Artificial Intelligence and Big Data Analytics, Bandung, Indonesia: 1-6, 27-29 October 2021.

E. Kabir, S. Guikema, and B. Kane, Statistical Modeling of Tree Failures During Storms, Reliability Engineering & System Safety Vol. 177: 68-79, 2018.

Z. Xiaoyi, L, Pan, Z. Zijian, T. Denver, K. Amin, Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree, Reliability Engineering and System Safety, Elsevier, Vol. 200(Issue C), 2020.

K. Vehkalahti and B. Everitt, Multivariate Data and Multivariate Analysis, Multivariate Analysis for the Behavioral Sciences, CRC Press: 225-237, 2019.

G. Judge, W. Griffiths, R. Hill, H. Lutkepohl, and T. Lee, The Theory and Practice of Econometrics. 2nd Edition, Wiley, New York, 1991.

JF. Hair, RL. Tatham, RE. Anderson, Multivariate Data Analysis. 5th Edition, Prentice Hall, 1998.

N. Fries and P. Rydén, A Simulation Framework for Evaluating Statistical Methods for Quality Control in Manufacturing, November 2021.

A. Abdulhafedh, Incorporating the Multinomial Logistic Regression in Vehicle Crash Severity Modeling: A Detailed Overview. Journal of Transportation Technologies, Vol. 07(Issue 03): 279-303, July 2017.

T. Usman, L. Fu, L. Miranda-Moreno, Injury Severity Analysis: Comparison of Multilevel Logistic Regression Models and Effects of Collision Data Aggregation, Journal of Modern Transportation, Vol. 24 :73-87, February 2016.

K. Hubbert, M. Doustmohammadi, Multinomial Logit Analysis of Injury Severity in Crashes Involving Emotional Drivers. International Journal of Psychology and Behavioral Sciences, Vol. 9(Issue 4): 63-70, 2019.

Z. Chen, and W. Fan, A Multinomial Logit Model of Pedestrian-Vehicle Crash Severity in North Carolina. International Journal of Transportation Science and Technology, Vol. 8(Issue 1): 43-52, March 2019.

Y. Li and W. Fan, Modelling Severity of Pedestrian-Injury in Pedestrian-Vehicle Crashes with Latent Class Clustering and Partial Proportional Odds Model: A Case Study of North Carolina, Accident Analysis & Prevention, Vol. 131: 284-296, October 2019.

A. Kitali, S. Mokhtarimousavi, C. Kadeha, and P. Alluri, Severity Analysis of Crashes on Express Lane Facilities Using Support Vector Machine Model Trained by Firefly Algorithm, Traffic Injury Prevention, Vol. 22(Issue 1): 79-84, November 2020.

T. Hmaid and R. Imam, Development of Crash Prediction Models for Roadway Segments in Jordan, The Second Balq'a International Engineering Conference (BIEC 2019), Dead Sea, Jordan, December, 2019.

Shatnawi, N., Al-Omari, A., Alkhateeb, S., Prediction of Risk Factors Influencing Severity Level of Traffic Accidents Using Artificial Intelligence, (2023) International Review of Civil Engineering (IRECE), 14 (1), pp. 1-7.

Obeidat, M., Smadi, H., Abed Rabbo, S., The Factors Influencing the Prediction of Crash Frequency, Severity and Type: the Case of California Intersections, (2023) International Review of Civil Engineering (IRECE), 14 (1), pp. 8-15.

Hassouna, F., Tubaileh, M., Road Traffic Casualties in West Bank: Trends Analysis and Modeling, (2021) International Review of Civil Engineering (IRECE), 12 (2), pp. 101-107.

B. Al-Mistarehi, A. Alomari, R. Imam, and M. Mashaqba, Using Machine Learning Models to Forecast Severity Level of Traffic Crashes by R Studio and ArcGIS. Frontiers in built environment, Vol. 8(Issue 54), April 2022.


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