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Road tunnels are enclosed spaces that most occupants only experience while driving through them. In case of fire, however, occupants potentially need to evacuate on foot from a dangerous and unfamiliar environment. Clear and accurate guidance is important for an efficient and safe evacuation from tunnels. Common cues for evacuation guidance are a signage and audio messages that attract occupants to move on appropriate egress routes and avoid unsafe routes. This paper investigates how different types of visual and auditory signals influence occupants’ exit choices in a simulated tunnel evacuation. Common guidance cues were presented to participants in a mobile Head Mounted Display, and they were asked to choose between two possible exit doors in a simulated road tunnel. Two attracting cues (‘‘EXIT’’ signs, audio instructions), and two detracting cues (‘‘DO NOT ENTER’’ signs; traffic cones placed in front of an exit) were studied in three virtual reality (VR) experiments. In each experiment, the presence and direction of the cues were manipulated, and data from 20 participants were collected. Experiment 1 explored the effects of attracting cues, Experiment 2 detracting cues, and Experiment 3 the combination of attracting and detracting cues. Across all studies, participants tended to follow the guidance provided when there was only one cue. When several competing and even contradictory cues were present, participants were most likely to rely on audio instructions, followed by traffic cones and ‘‘DO NOT ENTER’’ signs, whereas ‘‘EXIT’’ signs were often disregarded. We conclude that participants tend to follow temporary cues that could carry current information, as opposed to permanently installed signage. Some corresponding suggestions are put forward on evacuation system design and strategic planning in a tunnel fire. 

Crossing flows of pedestrians result in collective motions containing self-organized lanes or stripes. Over a wide range of crossing angles, stripe orientation is observed to be perpendicular to the mean walking direction. Here, we test the behavioral components needed to reproduce the lanes and stripes in human data using an empirical, vision-based pedestrian model (Visual SCruM). We examine combinations of (i) steering toward a goal, (ii) collision avoidance, and (iii) alignment (both with and without visual occlusion). The minimal model sufficient to reproduce perpendicular stripes was the combination of a common goal and collision avoidance, although the addition of alignment with occlusion better approximated human heading adjustments. However, the model overestimated the variation in heading and underestimated the variation in speed, suggesting that recalibration of the collision avoidance component is needed. 

Previous research has suggested that some positions in human crowds are more influential than others. The present study aims to manipulate the influence networks in real human crowds by specifying the causal relationship among some pedestrians. We strategically placed covert or explicit leaders (confederates) in a group of walking pedestrians, instructed them to change walking direction (heading) on a signal, and tested their influence on collective motion. We reconstructed visual influence networks from video data and analyzed the effect of these leaders on the movements of other pedestrians. Our results suggest that both covert and explicit leaders in influential positions can steer and split a crowd, but explicit leaders change the network topology and are significantly more influential than their covert counterparts. The results have potential applications to directing emergency evacuations. 

Efficient emergency guidance in buildings is essential for the safe evacuation of occupants. However, occupants may be exposed to contradictory information from signage and other sources of information. This study presents a set of forced-choice VR experiments and a machine learning approach to investigate the effect of competing or conflicting guidance on exit choice in simulated scenarios. In the VR study, participants chose between two potential exits under time pressure in each trial. Attracting cues (“EXIT” signs, audio instructions) and repelling cues (“DO NOT ENTER” signs, traffic cones) were placed in front of the two exits, either individually or in combination. In total, 2,125 datapoints were recorded from 20 participants. To model exit choice, machine learning (random forest, RF) models were applied to predict and interpret the guidance on evacuation choices. The tuned-hyperparameters RF model proposed in this study showed above 75% accuracy to predict evacuation choices facing conflict cues and was superior to default RF and logistic regression models. Interestingly, repelling cues such as “DO NOT ENTER” signs had a stronger impact on exit choice than attracting cues like “EXIT” signs when people have to make choices. Overall, the study offers valuable data and insights into exit choices, revealing that negative cues are more influential than positive ones in emergencies. These findings can significantly inform the design and optimization of egress guidance systems. This bias towards negative information under pressure suggests that evacuation systems should prioritize clear and prominent negative cues to guide occupants effectively. 

Collective motion in human crowds has been understood as a self-organizing phenomenon that is generated from local visual interactions between neighboring pedestrians. To analyze these interactions, we introduce an approach that estimates local influences in observational data on moving human crowds and represents them as spatially-embedded dynamic networks (visual influence networks). We analyzed data from a human “swarm” experiment (N= 10, 16, 20) in which participants were instructed to walk about the tracking area while staying together as a group. We reconstructed the network every 0.5 seconds using Time-Dependent Delayed Correlation (TDDC). Using novel network measures of local and global leadership ('direct influence' and 'branching influence'), we find that both measures strongly depend on an individual’s spatial position within the group, yielding similar but distinctive leadership gradients from the front to the back. There was also a strong linear relationship between individual influence and front-back position in the crowd. The results reveal that influence is concentrated in specific positions in a crowd, a fact that could be exploited by individuals seeking to lead collective crowd motion. 

For pedestrians moving without spatial constraints, extensive research has been devoted to develop methods of density estimation. In this paper we present a new approach based on Voronoi cells, offering a means to estimate density for individuals in small, unbounded pedestrian groups. A thorough evaluation of existing methods, encompassing both Lagrangian and Eulerian approaches employed in similar contexts, reveals notable limitations. Specifically, these methods turn out to be ill-defined for realistic density estimation along a pedestrian’s trajectory, exhibiting systematic biases and fluctuations that depend on the choice of parameters. There is thus a need for a parameter-independent method to eliminate this bias. We propose a modification of the widely used Voronoi-cell based density estimate to accommodate pedestrian groups, irrespective of their size. The advantages of this modified Voronoi method are that it is an instantaneous method that requires only knowledge of the pedestrians’ positions at a give time, does not depend on the choice of parameter values, gives us a realistic estimate of density in an individual’s neighborhood, and has appropriate physical meaning for both small and large human crowds in a wide variety of situations. We conclude with general remarks about the meaning of density measurements for small groups of pedestrians. 

For a group of pedestrians without any spatial boundaries, the methods of density estimation is a wide area of research. Besides, there is a specific difficulty when the density along one given pedestrian trajectory is needed in order to plot an 'individual-based' fundamental diagram. We illustrate why several methods become ill-defined in this case. We then turn to the widely used Voronoi-cell based density estimate. We show that for a typical situation of crossing flows of pedestrians, Voronoi method has to be adapted to the small sample size. We conclude with general remarks about the meaning of density measurements in such context. 

Patterns of crowd behavior are believed to result from local interactions between pedestrians. Many studies have investigated the local rules of interaction, such as steering, avoiding, and alignment, but how pedestrians control their walking speed when following another remains unsettled. Most pedestrian models assume the physical speed and distance of others as input. The present study compares such “omniscient” models with “visual” models based on optical variables.We experimentally tested eight speed control models from the pedestrian- and car-following literature. Walking participants were asked to follow a leader (a moving pole) in a virtual environment, while the leader’s speed was perturbed during the trial. In Experiment 1, the leader’s initial distance was varied. Each model was fit to the data and compared. The results showed that visual models based on optical expansion (θ˙) had the smallest root mean square error in speed across conditions, whereas other models exhibited increased error at longer distances. In Experiment 2, the leader’s size (pole diameter) was varied. A model based on the relative rate of expansion (θ˙/θ) performed better than the expansion rate model (θ˙), because it is less sensitive to leader size. Together, the results imply that pedestrians directly control their walking speed in one-dimensional following using relative rate of expansion, rather than the distal speed and distance of the leader. 

Conference Abstract 

While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly ‘‘resonate’’ to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this, we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.