Resumo Durante a movimentação de pessoas em uma situação de abandono de um ambiente cons-truído, normalmente, se imagina que o escape irá ocorrer em fluxo unidirecional rumo às saídas mais próximas. Todavia, em situações de grande estresse e desorientação, o fluxo humano pode não ser direcionado. Além disso, mesmo numa situação de normalidade, um fluxo bidirecional mal concebido pode causar uma perigosa situação de congestionamento ou comportamento não adaptativo. Dois dos importantes fenômenos que podem acontecer na movimentação bidirecional são as formações dos rios de escoamento e do empacota-mento. Na movimentação humana, além dos aspectos físicos, devem também ser consi-derados os aspectos comportamentais e cognitivos das pessoas envolvidas, cuja natureza é basicamente qualitativa. Para que se possa realizar uma modelagem com fatores de natureza tão distinta, uma ferramenta de inteligência computacional passível de ser usada é a lógica fuzzy. Assim, este trabalho tem como objetivo modelar qualitativamente a movimentação humana em fluxos bidirecionais, observando a formação dos rios de escoamento humano e do empacotamento, por meio de uma modelagem celular associada à lógica fuzzy. Palavras-chave: Lógica Fuzzy. Movimentação Humana. Simulação. Evacuação de ambientes. ⇤ Submetido em 30/06/

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Similar to other animal groups, human crowds exhibit various collective patterns that emerge from self-organization. Recent studies have emphasized that individuals anticipate their neighbours' motions to seek their paths in dynamical pedestrian flow. This path-seeking behaviour results in deviation of pedestrians from their desired directions (i.e. the direct path to their destination). However, the strategies that individuals adopt for the behaviour and how the deviation of individual movements impact the emergent organization are poorly understood. We here show that the path-seeking behaviour is performed through a scale-free movement strategy called a Lévy walk, which might facilitate transition to the group-level behaviour. In an experiment of lane formation, a striking example of self-organized patterning in human crowds, we observed how flows of oppositely moving pedestrians spontaneously separate into several unidirectional lanes. We found that before (but not after) lane formation, pedestrians deviate from the desired direction by Lévy walk process, which is considered optimal when searching unpredictably distributed resources. Pedestrians balance a trade-off between seeking their direct paths and reaching their goals as quickly as possible; they may achieve their optimal paths through Lévy walk process, facilitating the emergent lane formation.

Human crowds provide paradigmatic examples of collective behavior emerging through self-organization. Understanding their dynamics is crucial to help manage mass events and daily pedestrian transportation. Although recent findings emphasized that pedestrians’ interactions are fundamentally anticipatory in nature, whether and how individual anticipation functionally benefits the group is not well understood. Here, we show the link between individual anticipation and emergent pattern formation through our experiments of lane formation, where unidirectional lanes are spontaneously formed in bidirectional pedestrian flows. Manipulating the anticipatory abilities of some of the pedestrians by distracting them visually delayed the collective pattern formation. Moreover, both the distracted pedestrians and the nondistracted ones had difficulties avoiding collisions while navigating. These results imply that avoidance maneuvers are normally a cooperative process and that mutual anticipation between pedestrians facilitates efficient pattern formation. Our findings may influence various fields, including traffic management, decision-making research, and swarm dynamics.

This paper presents a model to simulate unsignalized pedestrian crosswalks. Principal scope of the model is to develop a tool to be used by decision-makers to evaluate the necessity of introducing a new crosswalk and/or switching to a traffic light and estimate the potential benefits of such a measure in term of Level of Service. The model is based on empirical evidence gained during an observation of an unsignalized crosswalk in Milan. Pedestrian motion is simulated using a simple Cellular Automata model in which only static floor field is implemented. Vehicles use a continuous car following model inspired on Gipps equations in which driver's reaction time is considered. Pedestrian's decision-making process on crossing attempt and model parameters are directly obtained from the analysis of pedestrian-vehicle interactions observed in reality. The model developed employs small time steps, thus allowing the consideration of different pedestrian speeds (intrinsically allowing to consider elderly) and smoothly reproducing car-pedestrian interactions. In order to validate the model, delays (or waiting times) measured for both pedestrians and drivers were compared with simulated values. Results show a good agreement between empirically obtained time delay and values computed in the simulation.