In evacuation models, individual interactions are typically assumed to follow the certain rules which are decided using prior knowledge. Our proposed improvement in evacuation modelling is inspired by the real-life experience that during crowd movements, individuals usually place their visual focus on the movements of their neighbours, even in the absence of social affiliations. That is, undeclared leader–follower (ULF) structures tend to emerge in crowd motion. In this study, to clarify the mechanism underlying individual interactions in crowd motion, a force-based model integrated with the ULF structure (LFM) was developed. An information-theoretic and model-free method, transfer entropy (TE), was applied to measure the ULF structure in evacuation crowds. Movement information (e.g., velocity and acceleration) was used as the time series for computing TE. The results showed that the LFM provides more realistic trajectories than does the classical social force model. In addition, the leader–follower structures were found to change over time, and an individual could act as a leader and a follower simultaneously during evacuation. ULF behaviour is strong at the early stage of evacuation and becomes weak when evacuees’ differences in movement states diminish. Moreover, the density map of leaders’ distributions presented a ‘V-like’ formation. Recognising the leader–follower relationship is central to understanding the mechanisms underlying the emergence of collective behaviour. The proposed approach is expected to aid in simulating realistic local interactions among evacuees and in providing a data-driven perspective for understanding crowd evacuation.

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker’s risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker’s assessment of critical factors, such as the number of evacuees close to the decision-maker ($NCDM$) and the number of evacuees close to an exit ($NCE$), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors.

Understanding exit choice behaviour is essential for optimising safety management strategies in building evacuations. Previous research focused on contextual attributes, such as spatial information, influencing exit choice, often using utility models based on monotonic functions of attributes. However, during emergencies, evacuees typically make rapid, less calculated decisions. The choice of context can significantly impact the evaluation of attributes, leading to preference reversals within the same choice set but under varying context conditions. This cognitive psychological phenomenon, known as context effects, encompasses the compromise effect, the similarity effect, and the attraction effect. While researchers have long recognised the pivotal role of context effects in human decision making, their incorporation into computer-aided evacuation management remains limited. To address this gap, we introduce context effects (CE) in a social force (SF) model, CE-SF. Evaluating CE-SF’s performance against the UF-SF model, which considers only the utility function (UF), we find that CE-SF better replicates exit choice behaviour across urgency levels, highlighting its potential to enhance evacuation strategies. Notably, our study identifies three distinct context effects during evacuations, emphasising their importance in advancing safety measures.