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1. Formation of lanes and stripes in crossing pedestrian flows using an empirical human model

   https://doi.org/10.1051/epjconf/202533404011  

   Feldmann, Sina; Veprek, Kyra; Warren, William H (January 2025, EPJ Web of Conferences: Traffic and Granular Flow 2024)

   Nicolas, A; Bain, N; Douin, A; Ramos, O; Furno, A (Ed.)

   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. 

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2. Visual model of locomotion reproduces lanes and stripes in crossing human crowds

   https://doi.org/10.1167/jov.25.9.2681  

   Veprek, Kyra; Feldmann, Sina; Warren, William (July 2025, Journal of Vision)

   The visual control of locomotion has been modeled for individual pedestrian behavior; however, this approach has not been applied to collective human behavior, where spontaneous pattern formation is often observed. We hypothesize that an empirical visual model of human locomotion will reproduce the emergent pattern of lanes and stripes observed in crossing flows, when two groups of pedestrians walk through each other at crosswalks or intersections. Mullick, et al. (2022) manipulated the crossing angle between two groups and found an invariant property: stripe orientation is perpendicular to the mean walking direction (i.e. 90˚ to the bisectrix of the crossing angle). Here we determine the combination of model components required to simulate human-like stripes: (i) steering to a goal (Fajen & Warren, 2003), (ii) collision avoidance with opponents (Bai, 2022; Veprek & Warren, VSS 2023), and (iii) alignment with neighbors (Dachner, et al., 2022), together called the SCruM (Self-organized Collective Motion) model. We performed multi-agent simulations of the data from Mullick et al. (2022), using fixed parameters and initial conditions from the dataset. There were two sets of participants (N=36, 38) with 18 or 19 per group. Crossing angle varied from 60˚ to 180˚ (30˚ intervals), with ~17 trials per condition. The minimal model necessary to reproduce stripe formation consists of the goal and collision avoidance components. Mean stripe orientation did not differ from 90˚ to the bisectrix (BF10 < 0.01, decisive). However, the SD of heading during crossing was significantly larger than the human data (p<0.001), whereas the SD of speed was significantly smaller (p<0.001). Thus, the ratio of heading/speed adjustments is lower than previously found, implying the need to reparameterize model components for walking in groups. In sum, steering to a goal and collision avoidance are sufficient to explain stripe formation in crossing flows, while alignment is unnecessary. 

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3. Autonomous Vehicle Visual Embodiment for Pedestrian Interactions in Crossing Scenarios: Virtual Drivers in AVs for Pedestrian Crossing

   https://doi.org/10.1145/3411763.3451626  

   Furuya, Hiroshi; Kim, Kangsoo; Bruder, Gerd; J. Wisniewski, Pamela; F. Welch, Gregory (May 2021, Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   This work presents a novel prototype autonomous vehicle (AV) human-machine interface (HMI) in virtual reality (VR) that utilizes a human-like visual embodiment in the driver’s seat of an AV to communicate AV intent to pedestrians in a crosswalk scenario. There is currently a gap in understanding the use of virtual humans in AV HMIs for pedestrian crossing despite the demonstrated efcacy of human-like interfaces in improving human-machine relationships. We conduct a 3x2 within-subjects experiment in VR using our prototype to assess the efects of a virtual human visual embodiment AV HMI on pedestrian crossing behavior and experience. In the experiment participants walk across a virtual crosswalk in front of an AV. How long they took to decide to cross and how long it took for them to reach the other side were collected, in addition to their subjective preferences and feelings of safety. Of 26 participants, 25 preferred the condition with the most anthropomorphic features. An intermediate condition where a human-like virtual driver was present but did not exhibit any behaviors was least preferred and also had a signifcant efect on time to decide. This work contributes the frst empirical work on using human-like visual embodiments for AV HMIs. 

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4. EFFECT OF PEDESTRIAN MOVEMENT ON THE SPREAD OF INFECTIOUS DISEASES DURING AIR

   Pierrot Derjany, Sirish Namilae (April 2017, Transportation Research Forum)

   This paper presents a formulation for an integrated model combining a social force based pedestrian movement including collision avoidance and a stochastic infection dynamics framework to evaluate the spread of infectious diseases in air transportation medium. We apply the multiscale model for two infectious diseases (1) Ebola and (2) SARS pathogens with different transmission mechanisms and compare the pattern of propagation during airplane boarding and deplaning at the airport gate. The objective of this analysis is to assess the influence of pedestrian movement on infection spread at an airport departure lounge. 

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5. Behavioral dynamics of pedestrians and crowds. International handbook of ecological psychology.

   Warren, WH (December 2025, Routledge)

   Romero, V; Segundo-Ortin, M; Wagman, J; Nonaka, T (Ed.)

   Where does the organization in behavior come from? Ecological psychology seeks to explain adaptive behavior as self-organized, emerging from the dynamics of agent-environment interactions, without assuming such organization a priori. This chapter offers an accessible introduction to the behavioral dynamics approach to modeling perception and action, applied to the case of human locomotion and crowd behavior. Most models of such behavior are ‘omniscient’, presuming accurate knowledge of the environmental state, but we find that information-based ‘visual’ models more closely capture human data. At the individual level, a locomotor trajectory emerges from an agent’s interactions with goals and obstacles, as attractors and repellers appear, shift, and bifurcate. At the dyad level, pedestrian interactions governed by visual control laws yield coordinated movements such as interception, collision avoidance, and following. At the collective level, these local interactions generate human ‘flocking’, crowd bifurcations, and patterns of lanes and stripes in crossing flows of pedestrians. By adopting an empirical, bottom-up, information-based approach, behavioral dynamics helps explain individual and collective behavior as resulting from a process of self-organized pattern formation, without appealing to internal plans or external commands. 

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