Near-Real-time Health Monitoring and Assessment of a Railway Track system

Project Abstract

Transportation plays a crucial role in shaping societal equity, and addressing disparities in accessibility and mobility is a pressing challenge faced by government and private agencies. Ensuring the safety and efficiency of railway track systems is of paramount importance in the dynamic landscape of modern transportation. The extensive rail network in the United States serves as a lifeline for the mobility of people and goods, underscoring the critical need for robust methods to evaluate, monitor, and predict the health of rail infrastructure. The primary aim of this research is to create an evaluation framework that enables the rigorous analysis of the equity implications of various transportation policies, especially within the context of the Washington DC area. By using advanced machine learning techniques to enable near-real-time assessment and early warning of railway track conditions, this research will seek to bridge the existing gap by developing an innovative evaluation framework that quantifies and assesses the equity impact of diverse transportation policy initiatives prior to implementation. This study focuses on the intersection of "equity" and "transformation" and as a result, aligns with the United States Department of Transportation's plan of ensuring transportation equity through assessment, investment, enhancement, and coordination.

Universities Involved

Howard University

Principle Investigators

Dr. Stephen Arhin

Funding Sources and Amounts

USDOT: $100,000

Start Date

September 1, 2023

Completion Date

September 1, 2024

Expected Research Outcomes & Impacts

The project's outcome will provide a validated method for early warning of railway track systems. This method will provide information to decision makers of rail systems in near-real time to take actions that can reduce delays and accidents by targeting speed advisories at specific locations.

Subject Areas

Railways, Safety, Machine Learning