Browse Research
Lead Researcher: Naomi Dunn (Virgina Tech Transportation Institute)
Description: This study will utilize CTIL to validate two driver state monitoring (DSM) systems. DSM systems use sensors to determine driver states that are detrimental to safe operation, including distracted, drowsy, or generally inattentive. Active and retired locomotive engineers will be recruited to take part in two CTIL simulations, utilizing the same scenario and each of the two DSM systems. Study participants will be asked to operate over monotonous scenery and given minimal workload while CTIL collects performance data (e.g., ability to maintain speed, time to respond to speed changes, and detection of stimuli). Researchers will use the data to examine how well each of the DSM systems can identify instances of drowsiness and varying levels of alertness in the locomotive engineer during the simulation. Results from this study will inform the industry on DSM technology effectiveness.
Project Status: Work in-progress
Lead Researcher: Michael Cowen (Monterey Technologies, Inc.)
Description: This research proposes a total systems human factors design and development process (UCD-Rail) to create a more optimal cab console user interface that is user-centered as opposed to data-centered. As part of this work, researchers will develop a novel user interface/user experience according to UCD-Rail process. The proposed UI/UX should ensure that the right information and functionality are displayed at the right time so that the user can better manage, adapt to, or override possibly unsafe automated rail equipment control decisions. Researchers will utilize CTIL to develop, implement, and evaluate the demonstration.
Project Status: Work in-progress
Lead Researcher: Kelly Ozdemir (KEA Technologies, Inc.)
Description: This study utilized the CTIL to investigate the effects of various auditory alerts on experienced locomotive engineers. Researchers were primarily interested in whether habituation to in-cab audio alerts may occur as observed in other sectors. Study participants were asked to operate either a standard freight or passenger locomotive (dependent on what they are familiar with) for a prolonged period. The CTIL was programmed to have areas of both high and low workload within the track. Throughout the duration of the study, one to two novel tones were introduced randomly and researchers instructed operators to acknowledge this tone by pressing a button in the simulator. The goal of the study was to conduct a preliminary investigation on an aspect of locomotive operations that has not been fully researched with regard to performance, workload, and alerts.
Project Status: Completed
Researchers: Kelly Ozdemir (KEA Technologies, Inc.)
Description: Researchers from KEA Technologies, Inc. developed symbology for head-up displays (HUDs) to improve safety and optimize operations. The primary goal was to create a subset of symbols for both freight and passenger rail operations to incorporate in a prototype HUD system and test in a realistic environment. The evaluation utilized CTIL and an augmented reality system developed by partners at the Massachusetts Institute of Technology. This effort also identified symbols currently used by railroads and which symbols are preferred across the rail industry.
Status: Completed
Lead Researchers: Roshan Kalghatgi, Daniel Brown, Ben Jafek, Nathan Varney (Aurora Flight Sciences)
Description: Aurora Flight Sciences and MIT Man Vehicle Laboratory are exploring the use of an Artificial Intelligence (AI) system to compensate for loss of operator situational awareness by automatically detecting and reporting on the state of railway signs, signals and other objects in the path of locomotives. Over three phases, Aurora and MIT developed and validated the concept in the CTIL.
EXP-L informing a distracted operator of detected trackside information and prompting for confirmation (right)
Image: Trackside information detection results from a Deep Neural Network trained on imagery obtained from the CTIL (left). EXP-L informing a distracted operator of detected trackside information and prompting for confirmation (right).
Project Status: Completed
Lead Investigator: Kirk Mathews (GE Global Research)
Description: Researchers from GE Global Research developed and evaluated a new locomotive operating mode that provides a way for a locomotive engineer to direct train motion at a higher level of abstraction than traditional manual mode, but with more control and visibility into system operation than is presently offered by energy management system automatic modes. This is a first step toward a 'clean slate' redesign of locomotive cab and control systems to lower experience requirements and decrease workload, enabling locomotive engineers to perform higher-level tasks as well as spend more time looking out the window, which will increase safety and job satisfaction. This project provides new insights into control systems and the skills which are required of locomotive engineers. The hypothesis tested in CTIL was that such an operating mode will reduce the performance gap between expert and apprentice locomotive engineers in typical main-line scenarios.
Illustration: Relation among envisioned system elements and existing elements. The Robust Manual Mode allows the engineer to specify behaviors which then are interpreted by the automation to determine specific speed control decisions.
Project Status: Completed
Researchers: Victoria Chibuogu Nneji and Mary Cummings (Duke University)
Description: Researchers from Duke University studied railroad dispatcher workload in association with automation under various scenarios and task demands. The team developed a computational model to help examine, understand, and predict the effects of the introduction of technology and automation on dispatcher personnel workload. As railroads implement automation through technologies like Positive Train Control, the role of dispatchers could become more significant in rail traffic control and operational management. It may be important to factor in the effects of changes on the performance of dispatchers, as well as train crews, to maintain acceptable levels of safety in operations for individual trains and the broader networked rail system.
Project Status: Completed
Lead Researcher: Charles M. Oman (Massachusetts Institute of Technology)
Description: Researchers investigated whether a new type of head-up display (HUD) results in improved engineer/conductor situation awareness of critical events and automation transitions. The display system leveraged augmented reality, optical head tracking, and a wide field-of-view to project critical information alongside real-world objects seen through the windshield. Quantitative evaluation of the design was performed with human-in-the-loop experiments in CTIL.
Image: Illustration of the AR-HUD concept. Conformal symbology highlights and approaching signal while the static elements provide speed and location information. The signal symbols would move on the HUD as the physical signal gets closer to the train. Head trackers ensure proper registration of the conformal symbology when the engineer moves his head.
Project Status: Completed
Researchers: Angelia Sebok, Jordan Haggit and Marc Gacy (TiER 1 Performance Solutions)
Description: This study used the CTIL to investigate human error opportunities with automation in the locomotive cab. Sixteen crews of professional engineers and conductors participated in simulated runs along a 17-mile segment of track. They performed the runs multiple times, with and without using automation. The crews worked with either Positive Train Control (PTC) or Trip Optimizer (TO), depending on their operational experience. The scenarios include low and high workload conditions that differ in terms of the number of events that occur in the scenario. This research examined the potential for automation-induced human error and the effects that automation has on operator workload and train control behaviors. Based on the findings, design and training recommendations will be made in the final report.
This study is a systematic empirical evaluation that follows from a preliminary exploratory study performed in the CTIL in 2016, and presented at the 2017 Transportation Research Board (TRB) Annual Meeting.
Project Status: Completed
Researcher: Rachel Grice (FRA)
Description: FRA seeks to develop an understanding of how dispatch radio communications could potentially lead to human-performance degradation in the railroad engineer, and whether a Head-Up Display (HUD) combined with digital transmission of radio communications would be an alternative superior technology to communicate non-urgent information usually conveyed over dispatch radios. These questions were explored in an experiment using the CTIL.
Project Status: Completed
Lead Researcher: Angelia Sebok (TiER1 Performance Solutions)
Description: This presentation delivered at the 2017 Transportation Research Board (TRB) Annual Meeting discusses a study that used the CTIL to investigate human error opportunities associated with automation in the locomotive cab. In the study, operators were observed interacting with simulated Trip Optimizer (TO) and Positive Train Control (PTC) systems in a simulated train run. Three professional engineers participated in three simulated runs along a 17-mile segment of track. In the first (familiarization) scenario, the operators worked without automation. In the second scenario, the engineers used PTC or TO, depending on their experience with the technology. In a third scenario, the engineers worked with automation as they had in the second scenario, but three events were added: a work zone (presented on the automation and discussed in a pre-run briefing), a temporary speed restriction, and a stop and protect. The last two events were not displayed on the automation, and were communicated by a radio call from the dispatcher. The scenarios, while not a carefully controlled experimental study, provided the researchers with useful insights regarding human error potential.
Project Status: Completed
Lead researcher: James Brooks (General Electric Global Research)
Description: In 2016, GE Global Research teamed with MIT to build a hierarchical task model of the Trip Optimizer (TO) software, and used it to inform a potential future iteration of it. GE collaborated with several Class I freight railroads to bring in TO-experienced engineers to validate the model and test the next-generation TO concept using the CTIL. This research provides a methodology to design and evaluate new roles for humans and automation systems.
Project Status: Completed
Researchers: Matthew Isaacs, Megan France, and Gina Melnik (Volpe Center)
Description: In 2014, to directly address future human-machine interface concerns and to encourage out-of-the-box design approaches to the operation environment, FRA commissioned QinetiQ North America Technology Solutions Group to design and construct an experimental prototype crewstation in order to demonstrate an alternative crewstation concept and design approach.
The Volpe Center conducted a human factors evaluation of the experimental crewstation to find areas where it could provide or deny human factors benefit when compared to the Association of American Railroads (AAR) 105 control stand. This was done by performing an initial evaluation with human factors experts to define major areas of concern; a comparison of the experimental crewstation (and the existing AAR-105 control stand in the CTIL) to one of the most commonly-used human factors design standards, the Department of Defense Design Criteria Standard: Human Engineering (MIL-STD-1472G); an evaluation using the CTIL’s anthropometric modeling software to look at reachability, comfort, visibility and arm support; and, a usability test with experienced engineers in the simulator.
Project Status: Completed
Lead Researcher: Andrew Liu (Massachusetts Institute of Technology)
Description: This research project designed a locomotive Head-Up Display (HUD), a secondary display that helps the operator locate possible hazards in the surrounding environment while maintaining an awareness of the vehicle’s current state. HUDs are widely used in both military and commercial aviation and they are gradually being adopted in general aviation, but much less common in automobiles. In 2016, this project utilized the CTIL to examine the usefulness of HUDs for locomotives and concluded that HUDs could help engineers avoid accidents and improve their performance.
Image: The prototype HUD design. The symbology includes (1) a speed display, (2) in-cab signals, (3) a moving dynamic element highlighting external objects of interest, and (4) a message box for upcoming events.
Project Status: Completed
Lead Researcher: Jared Young (University of Massachusetts – Amherst)
Description: In 2015, UMass Amherst researchers used MIT's moving map display in the CTIL to examine how the distribution of operator glances changes with the use of moving map technologies, when compared to paper charts. This study was funded by the New England University Transportation Center (UTC).
Project Status: Completed
Researchers: Andrew Liu, Charles Oman, and Kathleen Voelbel (Massachusetts Institute of Technology)
Description: MIT developed a real-time moving map display to support engineer situation awareness. The design was informed by a cognitive task analysis, and was tested using rail engineers in the CTIL.
Project Status: Completed
Researchers: Gina Melnik, Hadar Safar, and Matthew Isaacs (Volpe Center)
Description: In this system demonstration, researchers used surrogate (novice) engineers to compare operator behavior in trains running with and without moving map technology. Because surrogate engineers were used for the experiment instead of experienced locomotive engineers, the results have limited applicability. Lessons learned and general best practices for designing and running future CTIL experiments are discussed in the report resulting from this research (see below for link).
Image: Moving map display designed by Alion Science and Technology and used in the study.
Project Status: Completed
Researchers: George Elsmore (Veolia Transdev), Raja Parasuraman (George Mason University)
Description: Veolia Transdev, together with George Mason University and Volpe, conducted a two-phase study aimed at understanding and then reducing engineer distraction. Researchers used knowledge of key distraction issues gained in the CTIL during the first phase to design a training program intended to reduce engineer distraction. This training module was tested in the CTIL during the second phase.
Veolia implemented its attention training program in 2013, which resulted in the National Transportation Safety Board (NTSB) closing an FRA Safety Recommendation with a classification of “acceptable action.” The recommendation (R-05-009) was to “Develop guidelines for locomotive engineer simulator training programs that go beyond developing basic skills and teach strategies for effectively managing multiple concurrent tasks and atypical situations.”
Project Status: Completed
Last updated: Thursday, October 24, 2024