Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model

Abstract

The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015–2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), resulting in thirteen injuries with $346,875 cost of vehicle damages. Consolidation programs help to close redundant crossings and thereby decrease the crash risks; however, it is difficult to find the best crossing in a neighborhood for closure. In our previous research working on HRGC consolidation models in 2019, from among four Machine Learning algorithms, eXtreme Gradient Boosting (XGboost) performed better in HRGC prediction models. In continuation of our previous studies on developing a HRGC prediction model, this research employed Text Mining Techniques, and Geospatial Analysis in addition to the XGboost Machine Learning algorithm. The aim was to develop a consolidation model that is customized for local implementation. The results indicated an overall accuracy of 88 % for the proposed model. The relative importance of the variables input to the model was also reported and offers an in-depth understanding of the model’s behavior. Considering the different correlation threshold, a sensitivity analysis was also performed on different aggregation gain values. Subsequently, it resulted in the development of a simplified model utilizing 14 variables, with aggregated gain values of 95 % and a correlation threshold of 0.5. Based on this model, 15 % of current highway-rail grade crossings should be closed.