Agent-Based Psychometric Route Optimization: Personality-Driven Pedestrian Navigation and Infrastructure Design

Abstract

Current pedestrian routing assumes all pedestrians are identical and optimizes a single objective such as journey time, despite 6 decades of evidence showing personality consistently influences route preference. This paper develops a route, an agent-based framework that maps Big-Five-plus-sensation-seeking profiles to eight route attributes through a multinomial logit (MNL) cost function governed by an 8 × 6 personality, attribute interaction matrix (γ). The framework integrates a Mesa 3.x simulation on the Boğaziçi University campus map (740 nodes, 1,892 edges, SRTM elevation 4–135 m), a five-layer multi-modal pathfinder governed by a personality–mode matrix (δ), and a p-median facility-location optimiser that converts personality-segmented demand into infrastructure placement. Three converging lines of evidence support the approach. A 2,000-agent simulation produced significant alignment improvements on all nine pre-registered tests at p < .001 (Reserved archetype Cohen's d = 1.563), and was robust to terrain. A real Phase 1 screening survey (n = 26, 19 eligible) confirmed 16 of 23 hypothesised δ sign directions (70 %, binomial p = .047). A synthetic pipeline validation produced large subjective-measure effects (satisfaction d = 1.169, commercial viability d = 1.445) while objective deviation stayed flat. A multi-modal extension cut travel time by 43 %; the optimiser recommended 23 scooter docks, saving 2,397 person-hours per day at Gini = 0.007. Personality matters for routing, as does the technology that underlies it.