Traffic Signal Phasing Problem-Solving Rationales of Professional Engineers Developed from Eye-Tracking and Clinical Interviews

Abstract

There is a lack of knowledge on the way transportation engineering practitioners engage with various Contextual Representations (CRs) to solve traffic engineering design problems. CRs such as equations, graphs, and tables could be perceived differently, even if they represent the same concept. The present study recognized left-turn treatment at signalized intersections as a prominent concept in traffic engineering practice and identified three associated CRs (a text-book equation, a graphical representation, and a stepwise flowchart) to design a phasing plan. Two data collection mechanisms were concurrently employed: 1) eye-tracking to analyze visual attention and document problem-solving approaches and 2) reflective clinical interviews to analyze ways of thinking and document problem-solving rationales. The problem-solving experiment was completed by twenty-four transportation engineering practitioners. Transportation engineering practitioners not only demonstrated preferences for different CRs, they also demonstrated different reasoning as to the selection of the same CR. Results of Multivariate Analysis of Variance showed that there was a statistically significant difference in visual attention based on CR. Additionally, in-vivo coding of participants’ interviews identified seven distinct rationales for CR selection. Findings from this study could be employed to modify transportation engineering curricula with optimized visual CRs.