Metro transfer passageways are complex pedestrian systems where heterogeneity in pedestrian collision-avoidance behaviors, especially overtaking and evading, can disrupt crowd organization, and intensify local congestion. However, most existing models describe pedestrian heterogeneity using simplified behavioral rules or demographic attributes, with limited attention to how personality traits correlate with the underlying collision-avoidance decisions. This study develops an agent-based simulation framework, informed by questionnaire-derived personality classifications and behavioral parameters, to examine how personality heterogeneity influences pedestrian overtaking and evading behaviors. Based on Eysenck’s personality theory, questionnaire data on walking behaviors and safety perceptions were collected from Beijing metro users, and pedestrians were classified into three personality-informed behavioral types: Self-Directed, Flexible-Adaptor and Cautious-Altruist. The questionnaire results show that these types differ in overtaking thresholds, evading responses and perceived safety, and that personality traits are more strongly associated with these behavioral differences than gender or age. A dual-layer behavioral simulation framework was then developed by coupling personality-dependent state transitions with the social force model. The decision layer describes transitions among following, overtaking and evading states, while the movement layer reproduces microscopic motion in continuous space. Agent-based simulations of metro transfer passageways with different personality compositions show that personality composition correlates with local density accumulation, congestion evolution, interaction patterns and passageway efficiency, particularly near turning and merging regions. These findings suggest that personality-informed behavioral modeling can improve the representation of heterogeneous pedestrian interactions in high-density metro environments.
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Wei et al. (2026) studied this question.
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