Assessment of the expert locomotive engineer's mental Model through expert-novice interactions

Today, many long-haul freight locomotives around the world are equipped with autothrottle systems that follow pre-computed and fuel-efficient speed plans. However, these systems cannot adapt to changes in operational constraints or engineers' train handling preferences, which results in engineers taking back manual control. To address issues created by this traded approach scheme, a new operational mode is envisioned that allows operators to shape automation behavior. Although high level goals have been enumerated by previous task analyses, there has been little research on how engineers actually drive routes, identify situations, and make train handling decisions. To fill this gap , five subject pairs drove a U.S. DOT/FRA freight locomotive research simulator along a 65 mile route, responding to signals, speed restrictions and dispatcher orders. Each subject pair consisted of one expert and one novice subject. One subject was seated at the controls and the other subject was seated in the conductor's position. The subject at the controls had limited access to information and relied on verbal communication with the other subject to safely manipulate the train controls. Subjects drove the route twice, once at each position. The research team developed a coding scheme based on cognitive linguistics research and prior work on freight driving strategies to categorize each interaction from the study. Analysis of this data suggested that experienced engineers know what decisions and actions should be taken when various situations are encountered along a route, but their train handling (e.g. braking) tactics vary. Next-generation autothrottle systems should leverage the engineer's ability to assess operational context and initiate actions. Additionally, these systems should allow the operator to make speed plan modifications at both the tactical and strategic level to accommodate the observed variation between engineers' control strategies.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Price, Rachel,S. M.(Rachel E.)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics.

Advisor dc:contributor.advisor

• Charles M. Oman.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127091

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127091