Research Areas
Pioneering projects mapping structural integrity and cyber-physical sensors.
Current Programs
Railway Infrastructure Risk Management
This project develops an implementable risk management model for assessing track safety risk, in support of the optimization of track inspection and maintenance. The current research focuses on broken rails, which are the leading derailment cause on U.S. freight railroads.
Advanced Train Control Technologies and Risk
The objective of this research is to evaluate the safety benefit and cost and operational impact of implementing positive train control (PTC) under various operating scenarios, such as restricted speed operations.
Intelligent Railroads, Safety and Security
The railway industry in stepping into the stage of the “Digital Railways,” which we call the Internet of Railway Things (IoRT). This project convened railroad experts and analysts to evaluate the safety and security risk profiles of emerging connected railroad technologies, and thus recommend implementable risk mitigation actions.
Railroad Big Data and Artificial Intelligence
This research develops advanced big data and artificial intelligence techniques to turn railroad big data into useful information that leads to risk-informed safety decisions. We also train the next generation of railroad experts skilled at data analytics.
Freight Railroads, Economics and Logistics
This project studies the economic efficiency of freight rail transportation and develops operations research methods to optimally allocate limited resources for maximizing the economic benefits of freight rail projects.
Evaluation of Raised Pavement Markers (RPMs)
This research evaluates the safety effects of raised pavement makers, and identifies their optimal implementation and promoting alternatives or modifications. The work is sponsored by New Jersey Department of Transportation.
Hazardous Materials Transportation Risk Analysis Tool
This project develops a GIS-based risk assessment tool for rail transport of flammable liquids. The tool estimates the average recurrence interval of a release incident, and the affected population. This work is sponsored by the Center for Advanced Infrastructure and Transportation (CAIT) at Rutgers University.