Technical Empathy in Operational Readiness: Train Driver and Train Controllers

Last Updated on November 13, 2023

Adaptive training and induction in dynamic scenarios is increasingly critical – from defence to construction to railway operations. The best are using CGI training – such as this example from Melbourne, Australia for Train Driver and Train Controller Training programmes.

The Rail Pilot-Train Controller Training Disconnect

Rail pilots and train controllers have critical roles in ensuring safe and efficient rail operations. However, these groups can have different perspectives on the network, which can lead to miscommunication and a disconnect in understanding. This issue has been highlighted in recent railway tragedies, where miscommunication has been identified as a contributing factor (NTSB, 2020).

See an example of our CGI Training ‘side-by-side’ view (Glenroy, Melbourne).

Commissioning event time pressures

In commissioning events of rail upgrades, there is huge time pressure to construct new infrastructure in complex contexts in restricted times and then seamlessly integrate with the operational network and upskill thousands of operators within days and weeks.

Practical Training has been the primary method of training rail pilots. But Practical Training can reduce commissioning timeframes to let thousands of drivers drive up and down the track – with impacts on rostering, overtime, staff and training costs in an already stretched workforce.

Traditionally, Train Controllers and other key staff may only get a PowerPoint or schematic map briefing and so may not fully comprehend the changes nor the challenges faced by rail pilots on the ground when incidents occur.

Change Management and Culture

Adopting new technologies and practices requires a shift in organizational culture and effective change management strategies. Kotter’s 8-Step Change Model (Kotter, 1996) and Lewin’s Change Management Model (Lewin, 1951) provide comprehensive frameworks for managing change and guiding organizations through the process.

By addressing potential resistance and fostering a culture of learning and adaptability, rail organisations can smoothly transition into utilising advanced railway training tools.

Integrated Train Drivers and Train Controller Training

Computer-Generated Imagery (CGI) training offers a powerful solution to bridge the gap between rail pilots and train controllers. Through digital twin technology, CGI training allows both groups to experience the rail network from each other’s perspectives, fostering empathy and collaboration.

Furthermore, CGI training is proven to be 70% cheaper than traditional practical training (Davies, 2021), while also reducing the risks and logistical challenges associated with old-school methods.

CGI training allows both rail pilots and train controllers to experience the rail upgrade, fostering an unprecedented level of understanding and empathy between groups.

Benefits of Adopting CGI Training

Organisations that embrace CGI training can expect numerous benefits, including:

  1. Improved alignment and collaboration between rail pilots and train controllers.
  2. Enhanced safety and reliability of rail operations.
  3. Reduction of training costs by up to 70% compared to practical training.
  4. Increased efficiency in onboarding and upskilling staff.

A deeper dive into the benefits of CGI eLearning methods can be found here.

Train driver with OLE safety envelope in rail driver training
Train driver with OLE safety envelope in rail driver training (read more about our dynamic kinematic envelope and safety clearance zone modelling here).

To deliver responsive CGI training, you need suppliers who can deliver in the narrow window of time available, to the fidelity needed with verifiable accuracy, fluid design inputs and mapped to national competencies as needed. CGI movies like “Avatar” at the cinemas take years to build.

This is another reason why CGI Digital Twins are such high value for organisations to extract training, design, engagement and other values quickly – sometimes on the same day!

Risks of not using Best Practice

With CGI training proven over and over again, organisations failing to adopt it as a core component of a workforce development strategy may face significant risks.

These risks include increased operational costs, higher accident rates, and potential legal liabilities for directors arising from a failure to use best practice training methods (Reason, 1997).

CGI industry training has been proven to upskill workforces, upshift mindsets, and foster a culture of collaboration better, faster and easier than traditional methods in multiple instances. Invest in next-generation CGI training to save costs and protect your directors, staff, and users.

See more on induction and competency-based training.


National Transportation Safety Board (NTSB). (2020). Railroad Accident Report: Collision of Two BNSF Railway Company Freight Trains. NTSB/RAR-20/01.

Davies, R. (2021). The impact of CGI training on cost and effectiveness in the rail industry. Journal of Rail and Rapid Transit, 235(5), 1031-1040.

Kotter, J. P. (1996). Leading change. Harvard Business Press.

Lewin, K. (1951). Field theory in social science: Selected theoretical papers. Harper & Row.

Dekker, S. (2011). Drift into failure: from hunting broken components to understanding complex systems. CRC Press.

Fletcher, T. D., Chua, W. L., & Chua, Y. P. (2018). Enhancing rail safety through virtual reality training: A review of the literature. Applied ergonomics, 70, 302-313.

Reason, J. (1997). Managing the risks of organizational accidents. Ashgate Publishing.

Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 13(2), 74-101.

Stanton, N. A., & Paul, G. (2017). Human factors methods: A practical guide for engineering and design. CRC Press.

Besseris, G. J. (2013). Data-driven training in organizations. Industrial and Commercial Training, 45(2), 73-79.

Chung, A., Lee, H., & Park, J. (2021). The effect of virtual reality-based training on work performance: A meta-analysis. Human Factors and Ergonomics in Manufacturing & Service Industries, 31(1), 46-59.