Jamal Arshad, Umer Waleed
Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM BOX 5055, Dhahran 31261, Saudi Arabia.
Department of Construction Engineering and Management, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Int J Environ Res Public Health. 2020 Oct 14;17(20):7466. doi: 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.
更好地理解导致交通事故严重后果的情况是道路安全研究的一个重要目标。深入的碰撞伤害严重程度分析对于积极实施适当的缓解策略至关重要。本研究提出了一种改进的前馈神经网络（FFNN）模型，用于使用沙特阿拉伯王国（KSA）15条农村公路沿线收集的三年（2017 - 2019年）碰撞数据预测与个别碰撞相关的伤害严重程度。在研究期间共记录了12566起碰撞事故，预测变量的伤害严重程度结果为二元（致命或非致命伤害）。以反向传播（BP）作为训练算法、逻辑函数作为激活函数且隐藏层有六个隐藏神经元的FFNN架构产生了最佳的模型性能。使用不同的评估指标（如总体准确率、敏感性和特异性）对测试数据的模型预测结果进行了分析。预测结果表明了所提方法的充分性和稳健性能。还对优化后的神经网络进行了详细的敏感性分析，以显示不同预测变量对碰撞伤害严重程度结果的影响和相对影响力。敏感性分析结果表明，交通量、平均行驶速度、天气条件、现场损坏情况、道路和车辆类型以及行人参与等因素是最敏感的变量。本研究中应用的方法可用于碰撞数据的大数据分析，这可以作为政策制定者改善公路安全的快速有用工具。
