Patterning Risk: An Innovative Task Design Method for Simulating Incidents in Transportation Studies

Abstract

The development of alternative tasks is pivotal in transportation safety research, particularly when resource constraints hinder the execution of incident simulations. Traditionally, many studies have relied on expert knowledge and subjective judgments to design such alternative tasks. However, a systematic methodology is lacking. This work proposes a novel task design approach by constructing a Task Knowledge Graph (TKG). The proposed approach leverages the Knowledge Graph to delineate the hierarchical and logical relationships of necessary operations of human operators in the incident scenarios. Such TKG patterns the risks to provide a theoretical foundation for designing alternative tasks. A case study is provided in this work to illustrate the effectiveness of the proposed approach. In the case study, a modified Stroop game was designed to pattern the task demand of pilots during engine shutdown incidents. The results showed that the Stroop game could evoke similar eye movements in the participants as the engine shutdown scenario replicated on a simulator. Overall, the proposed approach offers a feasible tool for designing alternative tasks to obtain human behaviour data when direct replication of research scenarios is impractical.

Keywords: Transportation safety, simulation experiment, incident analysis, knowledge graph, psychophysiological data

DOI: 10.54941/ahfe1005198