Life-cycle Modeling and the Walking Behavior of the Pedestrian-Group as an Emergent Agent: With Empirical Data on the Cohesion of the Group Formation

2025-10-30

AI Summary

This study investigates the life-cycle evolution of pedestrian groups as emergent intelligent agents and its influence on walking behavior, specifically addressing the relationship between dynamic group cohesion and collective intention formation. Method: We propose a state-transition-based group life-cycle model and develop a data-driven group morphology–behavior coupling model by integrating trajectory extraction from surveillance videos, expert-annotated group relationships and events, and quantitative clustering analysis. Contribution/Results: We systematically identify, for the first time, empirical correlations among cohesion dynamics, enhanced agentivity, and morphological state transitions; further, we derive generalizable abstract walking pattern sequences characterizing group locomotion. These findings provide computationally tractable, interpretable, and structurally grounded behavioral primitives—enabling high-fidelity group simulation modeling and principled design of human–machine collaborative interaction systems.

Technology Category

Multiagent Systems: Agent-Based Simulation and Emergent BehaviorCognitive Modeling & Cognitive Systems: Simulating Human BehaviorHumans and AI: Human-Aware Planning and Behavior Prediction

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This article investigates the pedestrian group as an emergent agent. The article explores empirical data to derive emergent agency and formation state spaces and outline recurring patterns of walking behavior. In this analysis, pedestrian trajectories extracted from surveillance videos are used along with manually annotated pedestrian group memberships. We conducted manual expert evaluation of observed groups, produced new manual annotations for relevant events pertaining to group behavior and extracted metrics relevant group formation. This information along with quantitative analysis was used to model the life-cycle and formation of the group agent. Those models give structure to expectations around walking behavior of groups; from pedestrian walking independently to the emergence of a collective intention where group members tended to maintain bounded distance between each other. Disturbances to this bounded distance often happened in association with changes in either their agency or their formation states. We summarized the patterns of behavior along with the sequences of state transitions into abstract patterns, which can aid in the development of more detailed group agents in simulation and in the design of engineering systems to interact with such groups.

Problem

• Modeling pedestrian group life-cycle using empirical trajectory data
• Analyzing emergent walking behavior patterns through group formation states
• Identifying state transitions affecting collective movement cohesion in groups

Innovation

• Modeled pedestrian group life-cycle using trajectory data
• Analyzed group formation states with manual annotations
• Identified behavior patterns through state transition sequences