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Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Author

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  • Arshad Jamal

    (Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM BOX 5055, Dhahran 31261, Saudi Arabia)

  • Waleed Umer

    (Department of Construction Engineering and Management, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

Suggested Citation

  • Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7466-:d:427862
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    References listed on IDEAS

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    1. Ageli, Mohammed Moosa, 2013. "Road Traffic Accidents in Saudi Arabia: An ADRL Approach and Multivariate Granger Causality," MPRA Paper 47760, University Library of Munich, Germany.
    2. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Khalaf A. Al-Ofi & Hassan M. Al-Ahmadi, 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    3. Arshad Jamal & Muhammad Tauhidur Rahman & Hassan M. Al-Ahmadi & Irfan Ullah & Muhammad Zahid, 2020. "Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    4. Fanyu Meng & Pengpeng Xu & Cancan Song & Kun Gao & Zichu Zhou & Lili Yang, 2020. "Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach," IJERPH, MDPI, vol. 17(15), pages 1-16, August.
    5. Shengdi Chen & Shiwen Zhang & Yingying Xing & Jian Lu, 2020. "Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    6. Hasan A. Mohamed, 2015. "Estimation of Socio-Economic Cost of Road Accidents in Saudi Arabia: Willingness-To-Pay Approach (WTP)," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 5(3), pages 1-5.
    7. Seung-Hoon Park & Min-Kyung Bae, 2020. "Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    8. Mohammed Al-Turki & Arshad Jamal & Hassan M. Al-Ahmadi & Mohammed A. Al-Sughaiyer & Muhammad Zahid, 2020. "On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    9. Jungyeol Hong & Reuben Tamakloe & Dongjoo Park, 2019. "A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
    10. Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.
    11. Ageli, Mohammed Moosa, 2013. "Road Traffic Accidents in Saudi Arabia: An ARDL Approach and Multivariate Granger Causality," MPRA Paper 50616, University Library of Munich, Germany.
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    Cited by:

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    6. Tufail Ahmed & Mehdi Moeinaddini & Meshal Almoshaogeh & Arshad Jamal & Imran Nawaz & Fawaz Alharbi, 2021. "A New Pedestrian Crossing Level of Service (PCLOS) Method for Promoting Safe Pedestrian Crossing in Urban Areas," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
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