The proposed activities will enable the FRA’s Office of Safety and Office of R&D to utilize the RLS as a platform for internal FRA training offered to both new and current employees. Offering training online will increase flexibility in obtaining the necessary skills required to effectively perform essential job functions, and save on travel expenses currently incurred for this type of training. In addition, the proposal increases the amount of openly available education modules by incorporating the Railroading 101 modules developed by the FRA, as well as newly recorded versions of the 2016 Railway Engineering Education Symposium (REES) modules. The proposal will also provide technical support in using the modules and continuing monitoring and support of the system throughout the project duration. Continue reading “Implementation of Online Training Modules to Rail Learning System”
According to the USDOT Federal Railroad Administration, highway-rail grade crossing and trespasser fatalities still account for 96 percent of all rail-related deaths. A review of accident causes reveals that one of the main accident factors is human driving behavior.
One of the potential approaches to improve drivers’ behavior at crossings is to systematically examine their actions and use the outcomes to develop alternative methods or approaches to decrease the probability of missing or ignoring warnings. The Naturalistic Driving Study (NDS) approach, using in-vehicle video and other sensors to directly observe drivers during normal driving activities, is a technique that shows promise toward improved understanding of driver actions in the crossings.
This two-year project is divided into two phases with an overall objective to investigate driver behavior at highway-rail grade crossings using two distinct, but complimentary techniques. Phase I will use data collected under the Strategic Highway Research Program (SHRP) Naturalistic Driving Study to look at how normal drivers react at crossings in every day driving situations. Phase II will use the understanding developed in the first phase to create scenarios that resemble environments similar to those found in the NDS for use in our driver simulator environment. The research will look for two basic results. First, we will develop and use the organized NDS crossing database to examine behavioral trends at the crossings. Second, we will compare driver behavior in the simulator with that found in the NDS data to determine the level of correlation between the two environments. Our hypothesis is that a strong correlation would allow us to outline how to use the simulator environment in the future to predict driver response to a variety of crossing parameters.