Artificial Intelligence Enabled Road Vehicle-Train Collision Risk Assessment Framework for Unmanned Railway Level Crossings
Vivek Singhala, Sushant Jainb, Divya Anandc, Aman Deep Singhc, Sahil Vermac, Kavita Kavitac, Joel J. P. C. Rodriguesd e, Noor Zaman Jhanjhif g, Uttam Ghoshh, Ohyun Joi, Celestine Iwendij
- aDepartment of Information Technology, JSS Academy of Technical Education, Noida, India
- bDepartment of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India (Retired)
- cDepartment of Computer Science and Engineering, Lovely Professional University, Phagwara, India
- dFederal University of Piauí (UFPI), Teresina, Brazil
- eInstituto de Telecomunicações, Lisboa, Portugal
- fSchool of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia
- gSchool of Computer Science and Engineering, Taylor's University, Subang Jaya, Malaysia
- hSchool of Computer Science and Engineering, Vanderbilt University, Nashville, USA
- iDepartment of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea
- jDepartment of Electronics, BCC of Central South University of Forestry and Technology, Changsha, China
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
The study focuses on the artificial intelligence empowered road vehicle- train collision risk prediction assessment, which may lead to the development of a road vehicle-train collision avoidance system for unmanned railway level crossings. The study delimits itself around the road vehicle-train collisions at unmanned railway level crossings on single line rail-road sections. The first objective of the study revolves around the rail-road collision risk evaluation by the development of road vehicle-train collision frequency and severity prediction model using Poisson and Gamma-log regression techniques respectively. Another study objective is the collision modification factor implementation on predicted risk factors, to reduce the road vehicle-train collision risk at the crossings. The collision risk has been predicted to be 3.52 times higher and 77% lower in one direction while in other directions it is 2.95 times higher and 80% lower than average risk at all unmanned railway level crossings. With collision modification factor application on higher risk contributing factors i.e. `crossing angle' and `train visibility, it predicts to reduce the road vehicle-train collision risk to 85% approximately.
TL;DRThe study focuses on the artificial intelligence empowered road vehicle- train collision risk prediction assessment, which may lead to the development of aroad vehicle-train collision avoidance system for unmanned railway level crossings on single line rail-road sections. (Semantic Scholar)