User preferences in multi-objective routes: The role of gradient visualization and personality measures

Abstract

Traditional pedestrian navigation systems typically prioritize the shortest or fastest routes. However, modern urban environments require multi-objective navigation that incorporates factors such as route gradient, familiarity, and individual preferences. This study investigates how presenting gradient information–either in numeric or graphical formats–affects pedestrian route choices, and how individual psychological traits, particularly the Big Five and Sensation Seeking dimensions, influence these decisions. We conducted an online survey with 91 valid participants (from an initial pool of 315), each randomly assigned to one of three groups: Control (no gradient shown), Numeric (textual gradient), or Graphical (altitude charts). Participants selected their preferred route from six route pairs, each differing in slope and distance. These pairs were generated using a multi-objective planning algorithm that optimizes both attributes. Our findings reveal three key insights. First, numeric gradient presentation led to a modest shift away from shortest-route selections (mean: 3.89 versus 4.29 in the control group), particularly for longer cases. Second, graphical gradient representations did not significantly improve decision-making over numeric formats. Third, participants with higher Sensation Seeking scores showed a significantly stronger preference for longer but gentler routes (p < 0.01). These results highlight the role of individual tendencies in route selection, suggesting that personalized navigation systems could be improved by incorporating user-specific psychological profiles.