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AI Automation Index
Analyze the automation vulnerability, task-level AI risk, salary, and demand projection for Railroad Conductors and Yardmasters. Run the upskilling simulator.
AI Vulnerability
Median Salary
Growth Demand
We parsed O*NET's 20 constituent tasks for this occupation and classified them by AI threat risk.
Automate your routine tasks and design your day around strategic advisory to future-proof this score.
Simulate Upskilling →Signal engineers to begin train runs, stop trains, or change speed, using telecommunications equipment or hand signals.
Semi-structured task combining routine and non-routine responsibilities.
Confer with engineers regarding train routes, timetables, and cargoes, and to discuss alternative routes when there are rail defects or obstructions.
Complex problem-solving, strategic leadership, or high-empathy task requiring human judgment.
Receive information regarding train or rail problems from dispatchers or from electronic monitoring devices.
Semi-structured cognitive task involving analysis, inspection, or monitoring.
Receive instructions from dispatchers regarding trains' routes, timetables, and cargoes.
Semi-structured task combining routine and non-routine responsibilities.
Direct and instruct workers engaged in yard activities, such as switching tracks, coupling and uncoupling cars, and routing inbound and outbound traffic.
Complex problem-solving, strategic leadership, or high-empathy task requiring human judgment.
Operate controls to activate track switches and traffic signals.
Semi-structured task combining routine and non-routine responsibilities.
Keep records of the contents and destination of each train car, and make sure that cars are added or removed at proper points on routes.
Routine cognitive or manual task with high vulnerability to automation.
Arrange for the removal of defective cars from trains at stations or stops.
Semi-structured task combining routine and non-routine responsibilities.
Direct engineers to move cars to fit planned train configurations, combining or separating cars to make up or break up trains.
Complex problem-solving, strategic leadership, or high-empathy task requiring human judgment.
Inspect each car periodically during runs.
Semi-structured cognitive task involving analysis, inspection, or monitoring.
Review schedules, switching orders, way bills, and shipping records to obtain cargo loading and unloading information and to plan work.
Routine cognitive or manual task with high vulnerability to automation.
Confirm routes and destination information for freight cars.
Semi-structured task combining routine and non-routine responsibilities.
Verify accuracy of timekeeping instruments with engineers to ensure trains depart on time.
Semi-structured task combining routine and non-routine responsibilities.
Document and prepare reports of accidents, unscheduled stops, or delays.
Semi-structured task combining routine and non-routine responsibilities.
Instruct workers to set warning signals in front and at rear of trains during emergency stops.
Semi-structured task combining routine and non-routine responsibilities.
Observe yard traffic to determine tracks available to accommodate inbound and outbound traffic.
Semi-structured task combining routine and non-routine responsibilities.
Supervise workers in the inspection and maintenance of mechanical equipment to ensure efficient and safe train operation.
Complex problem-solving, strategic leadership, or high-empathy task requiring human judgment.
Supervise and coordinate crew activities to transport freight and passengers and to provide boarding, porter, maid, and meal services to passengers.
Complex problem-solving, strategic leadership, or high-empathy task requiring human judgment.
Record departure and arrival times, messages, tickets and revenue collected, and passenger accommodations and destinations.
Routine cognitive or manual task with high vulnerability to automation.
Inspect freight cars for compliance with sealing procedures, and record car numbers and seal numbers.
Routine cognitive or manual task with high vulnerability to automation.
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O*NET Methodology
This page dynamically extracts data structures sponsored by the U.S. Department of Labor. Our AI classification applies cognitive task automation risk models to individual tasks.
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