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

Jamal, Arshad and Umer, Waleed (2020) Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network. International Journal of Environmental Research and Public Health, 17 (20). p. 7466. ISSN 1661-7827

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Official URL: https://doi.org/10.3390/ijerph17207466


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.

Item Type: Article
Additional Information: Funding information: The authors would like to acknowledge the funding support provided by Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals for funding this work.
Uncontrolled Keywords: road safety, crash injury severity prediction, machine learning, neural networks, sensitivity analysis, Saudi Arabia
Subjects: H300 Mechanical Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 24 Mar 2022 11:24
Last Modified: 24 Mar 2022 11:30
URI: http://nrl.northumbria.ac.uk/id/eprint/48743

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