Civil Engineering Faculty Publications and Presentations

Document Type

Conference Proceeding

Publication Date

6-14-2024

Abstract

Highway-rail grade crossings (HRGCs) play an essential role in ensuring the secure traversal of road users across railway tracks. However, despite their significance, they present safety challenges, particularly when trains go undetected, heightening the risk of potential collisions between the road user and train. This paper aims to explore the viability of employing vibration sensors for detection and characterization of an approaching train’s speed at HRGCs. The methodology involves analyzing rail vibrations and developing a time series predictive machine learning (ML) model. To accomplish this, a Finite Element (FE) model of a ballasted track railway is created in SAP2000, consisting of essential track components such as rails, sleepers, rail pads, ballast, and subgrade. The acceleration time history of the rails, induced by a moving single car, is recorded, and employed as input data for training and testing a Long Short-Term Memory (LSTM) network. The LSTM model predicts crucial parameters of the approaching train, including its location and speed. Successful prediction of these parameters is anticipated to enable the determination of the precise moment when the train reaches the HRGC, affording road users sufficient reaction time to safely traverse the railway track.

Comments

Copyright © 2024 by ASME

Publication Title

Proceedings of the 2024 Joint Rail Conference

DOI

10.1115/JRC2024-124517

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.