Distracting or informative? Examining signage for cyclists using eye-tracking

Abstract

There is great political motivation to improve conditions for cyclists to help solving the transport needs of the future. We used eye-tracking to collect data and analysed it using a novel machine learning approach. 40 cyclists in total were tasked with navigating a set route through the Oslo city centre. One group before the new infrastructure was in place and one group after. The analysis focused on developing a method that could be used to investigate how a new signage strategy impacted cyclists in Oslo. Improving signage could create safer traffic conditions for cyclists, while avoiding adding distracting elements. The algorithms developed were able to detect and categorize a variety of important objects. The signage system itself seemed to result in some route change among cyclists, but not all followed the suggested route. Qualitative analyses suggests that those who deviated cycled faster and looked less at signs, than those who chose the suggested route. The paper discusses strengths and weaknesses involved in this approach. While useful, one should be careful to conclude that gaze behaviour reflects the true inner consciousness of cyclists.