A review of the technological developments for interlocking at level crossing. (April 2021)

• Record Type: Journal Article
• Authors: Fayyaz, Muhammad Asad Bilal; Alexoulis-Chrysovergis, Aris C; Southgate, Matthew J; Johnson, Christopher
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 4(2021)
• Page Start: 529
• Page End: 539
• Publication Date: 2021-04
• Subjects: Railway -- Level Crossing -- interlocking -- deep learning -- risk management; Railroads -- Periodicals; Personal rapid transit -- Periodicals; 625.1
• DOI: 10.1177/0954409720941726
• Languages: English
• ISSNs: 0954-4097

Abstract

A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union's Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach.