Data-driven framework for the adaptive exit selection problem in pedestrian flow: Visual information based heuristics approach

• Published in: Physica A Vol. 583; p. 126289
• Main Authors: Zhou, Zi-Xuan, Nakanishi, Wataru, Asakura, Yasuo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021
• Subjects: Data driven; Microscopic model; Obstacle detour; Path planning; Pedestrian dynamics; Visual information perception; Data driven; Visual information perception; Path planning; Microscopic model; Pedestrian dynamics; Obstacle detour
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2021.126289

Pedestrian behavior during evacuation has been formulated using various arbitrary microscopic methods to investigate the performance of crowd dynamics while their custom rules result in low visual realism in simulation due to the complexity of intrinsic decision logic of human. Statistical analysis is an effective way to reveal the motion pattern and path planning behavior of pedestrians whose main idea is to approach the trajectory and social attributes data of pedestrians extracted from evacuation drills as much as possible. In this study, we present a data-driven based microscopic pedestrian-simulation model with continuous-space representation to explore the potential of integrating empirical analysis into crowd simulation to enhance the authenticity of decision making. This method extracts the pedestrian’s decision mode and smoothly applies it in the crowd dynamics model. Instead of navigating agents by arbitrary regulations, the desired direction of pedestrians during the motion is arranged by machine learning (ML) algorithms. The path decision module trained with actual pedestrian data improves the compatibility of the model in the application of various spatial scenarios and no longer suffers from tedious parameter fine-tuning work. To completely describe the information precepted by pedestrians, a polygon segmentation module is developed to divide the visual field of pedestrians and identify the mutual visibility among them. This module filters out the information that can be perceived by pedestrians in real situations, thereby bridges the gap between statistical analysis and numerical simulation methods. We compare different ML approaches for route-choice behavior prediction and discuss the relative importance of its influencing variables under different scenarios. Inferring the perception of social interactions from disaggregate choice data, the scope of effectiveness of conformity behavior and crowd-aversion are also discussed. The simulation results are compared with experimental data, illustrating the model’s capability to accurately reproduce the observed flow motion in various scenarios with moderate modification in physical environment initialization.