An Intelligent and Autonomous Sight Distance Evaluation Framework for Sustainable Transportation

Abstract

Railways are facing a serious problem of road vehicle–train collisions at unmanned railway level crossings. The purpose of the study is the development of a safe stopping sight distance and sight distance from road to rail track model with appropriate computation and analysis. The scope of the study lies in avoiding road vehicle–train collisions at unmanned railway level crossings. An intelligent and autonomous framework is being developed using supervised machine learning regression algorithms. Further, a sight distance from road to rail track model is being developed for road vehicles of 0.5 to 10 m length using the observed geometric characteristics of the route. The model prediction accuracy obtained better results in the development of a stopping sight distance model in comparison to other intelligent algorithms. The developed model suggested an increment of approximately 23% in the current safe stopping sight distance on all unmanned railway level crossings. Further, the feature analysis indicates the ‘approach road gradient’ to be the major contributing parameter for safe stopping sight distance determination. The accident prediction study finally indicates that, as the safe stopping sight distance is increased by following the developed model, it is predicted to decrease road vehicle–train collisions.