Study Of The Behavioral Mechanism Of Self-organized Pedestrian Counter Flow

Pedestrian crowd movement, including single direction, bi-directional flow etc, may trigger serious crowd disasters such as trampling. People may be injured or even killed in these disasters. As a result, the effect of building facilities on the comfort and safety of people's movement becomes one of the important concerns of building designers and facility managers. Factors affecting the level of service relate closely to pedestrian flow pattern. Previous studies indicate that self-organized patterns emerging in pedestrian counter flows may affect the flow rate and velocity of crowds. However, the studies rarely discussed the inter-personal interaction in pedestrians.In this study, we first performed well-controlled experiments to capture the moving characteristics of pedestrians in a corridor. Pedestrians'moving trajectories were first extracted with digital image processing method and then mapped into real space coordinates by adopting a direct linear transformation approach. Moving characteristics of single pedestrian, interaction between pedestrian and the corridor as well as interaction between pair pedestrians were analyzed. It was found that when walking in the corridor, the average relaxation time of typical Chinese pedestrians was about 0.71s, and the maximum mean velocity of free walking was about 1.51m/s. Meanwhile, these pedestrians also kept a suitable distance to the wall to avoid potential collisions. When walking too close to the wall, the pedestrian had a tendency to walk away. This phenomenon was then expressed as an exponential decay force function. When one pedestrian tried to evade another standing still pedestrian in the corridor, the interaction between them showed a non-isotropic feature. The experimental results indicated that the participants preferred to walk with right preference more significantly. We further quantified the interaction among pedestrians, and found that the force from those who located on the right-forward direction did not change much while from those who located on the left-forward direction did vary with the increase of distance. Interaction among pedestrians in a single file uni-directional flow show that the moving pedestrian is affected by his direct predecessor most while is barely affected by others.Based on the experimental findings, two models were established, namely a metric distance based model and a k-Nearest-Neighbor (kNN) counterflow model, which could be used to investigate the fundamental interaction ruling pedestrian counter. flow. The basic update schemes of these two models were the same with a cellular automaton (CA) random walker model, which is entitled as basic model hereafter. Pedestrians moving in a long channel will evolve into left moving pedestrians and right moving pedestrians. These pedestrians interact with each other in different forms in different models. In the metric distance based model, the direction chosen behavior of an individual is influenced by all those who are in a small metric distance and come from the opposite direction; while in the kNN counterflow model, the direction chosen behavior of an individual is influenced by the distribution of a fixed number of the k-Nearest neighbors coming from the opposite direction. The self-organized lane formation was captured and factors affecting the number of lanes formed in the channel were investigated. Results implied that with varying the density, the lane formation pattern varies substantially in the case of metric distance based model while is nearly the same in the kNN counterflow model which matches field observations. This means that the kNN interaction plays a more fundamental role in the emergence of collective pedestrian phenomena. The relations among mean velocity, occupancy and total entrance density at the boundaries of the counter flow system were also studied. Reasons for the lane formation in the CA models were theoretically investigated on the basis of game theory. Reasons for the velocity enhancement and flow improvement were also discussed.The kNN counterflow model was further validated by comparing lane formation pattern and the fundamental diagram with real pedestrian counter flow. The results indicated that the kNN interaction enhances the mean velocity in the free flow phase by providing more efficient traffic condition, and is able to quantify features such as segregation and phase transition at high density of pedestrian traffic. Considering the facts such as the pedestrians'locations are out of alignment in reality, we further modified the kNN model into multi-grid kNN model to mimic pedestrian flow. Dynamics of the multi-grid kNN model were studied to detail traffic characteristics of pedestrian counter flow.With these insights in the behavioral mechanism of pedestrian counter flow, we illustrated the present study in the area of crowd control by an example of improving traffic situation for pedestrian counter flow in a long corridor in respect of a series of layout design. To facilitate application, we further embed the kNN counterflow model into a Geographic Information System (GIS) platform and try to derive fundamental diagram, as well as real-time level-of-service map so as to evaluate levels of services of pedestrian traffic facilities and efficiencies of different crowd control methods.