Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods.