An Intelligent and Autonomous Sight Distance Evaluation Framework for Sustainable Transportation

Vivek Singhal, Divya Anand, Hani Moaiteq Aljahdali, Nitin Goyal, Aman Singh and Seifedine Kadry
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Vivek Singhal: Independent Researcher, Meerut 250002, India
Divya Anand: Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
Hani Moaiteq Aljahdali: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 37848, Saudi Arabia
Nitin Goyal: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 144411, India
Aman Singh: Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
Seifedine Kadry: Faculty of Applied Computing and Technology, Noroff University College, 4608 Kristiansand, Norway

Sustainability, 2021, vol. 13, issue 16, 1-14

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.

Keywords: intelligent modeling; sustainable transportation; road vehicle–train collisions; sight distances; unmanned railway level crossings (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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