Advancing human performance in transportation systems: A SYDE graduate’s research journey
As a dedicated PhD researcher in Systems Design Engineering, Wendy Ding (Wen Ding), is pushing the boundaries of evaluating human performance and situation awareness in transportation systems. She is contributing significantly to human factors research aimed at optimizing safety and efficiency in surface transportation and aviation.
Wendy’s work focuses on human performance evaluation, situation awareness assessment, and the application of advanced data analytics on eye tracking data to improve human-system interactions in high-stakes environments. Her evaluations consider how individuals perform under varying conditions, emphasizing eye movement strategy, operational quality, and response times. This research is crucial for evaluating human performance and designing systems that support optimal performance in complex and dynamic settings.
In her investigation of situation awareness, Wendy employs methods such as the Situation Awareness Global Assessment Technique (SAGAT) and instructor evaluations. These tools measure how well operators perceive, comprehend, and anticipate elements in their environments, providing insights essential for informed decision-making during critical operations.
A distinctive aspect of Wendy’s research is her use of eye-tracking technology to understand operator attention and cognitive processes. Wendy identifies potential lapses in attention or information processing by analyzing metrics like fixation patterns, saccades, dwelling time, entry count, and entropy. These findings examined various aspects of operators’ attention allocation and eye movement behaviours, providing valuable insight into how operators with diverse backgrounds apply different gaze strategies. Furthermore, these conclusions can guide the design of interfaces and systems that reduce cognitive overload and enhance user performance.
As autonomous vehicle technology advances, Wendy explores the critical transitions between autonomous and manual control. Her work examines how operators adapt during these transitions, aiming to minimize response times and errors. Insights from her research guide the development of systems that ensure seamless handoffs and maintain safety in mixed-control scenarios.
Wendy also incorporates machine learning into her research to foster autonomous prediction of human performance. By forecasting operator behaviours and potential system failures, her models contribute to creating proactive safety mechanisms that enhance system reliability.
The Department of Systems Design Engineering is proud to show that Wendy’s research bridges challenges in transportation and aviation, where human-system interactions are becoming increasingly complex. By combining human factors methodologies with innovative technologies such as eye tracking and machine learning, Wendy is helping shape safer, more intuitive systems. Her work advances academic knowledge and has tangible implications for designing systems that prioritize human safety and performance in critical operational domains.