Purpose: A crowd of pedestrians is a complex system in which individuals exhibit conflicting behavioural mechanisms leading to self-organisation phenomena. Computer models for the simulation of crowds represent a consolidated type of application, employed on a day-to-day basis to support designers and decision makers. Most state of the art models, however, generally do not consider the explicit representation of pedestrians aggregations (groups) and their implications on the overall system dynamics. This work is aimed at discussing a research effort systematically exploring the potential implication of the presence of groups of pedestrians in different situations (e.g. changing density, spatial configurations of the environment). Methods: The paper describes an agent-based model encompassing both traditional individual motivations (i.e. tendency to stay away from other pedestrians while moving towards the goal) and an adaptive mechanism representing the influence of group presence in the simulated population. The mechanism is designed to preserve the cohesion of specific types of groups (e.g. families and friends) even in high density and turbulent situations. The model is tested in simplified scenarios to evaluate the implications of modelling choices and the presence of groups. Results: The model produces results in tune with available evidences from the literature, both from the perspective of pedestrian flows and space utilisation, in scenarios not comprising groups; when groups are present, the model is able to preserve their cohesion even in challenging situations (i.e. high density, presence of a counterflow), and it produces interesting results in high density situations that call for further observations and experiments to gather empirical data. Conclusions: The introduced adaptive model for group cohesion is effective in qualitatively reproducing group related phenomena and it stimulates further research efforts aimed at gathering empirical evidences, on one hand, and modelling efforts aimed at reproducing additional related phenomena (e.g. leader-follower movement patterns).

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.

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Several issues in transferring AI results in crowd modeling and simulation are due to the fact that control applications are aimed achieving optimal solutions, whereas simulations have to deal with the notions of plausibility and validity. The latter requires empirical evidences that, for some specific phenomena, are still scarce and hard to acquire. To face this issue, the present work presents an investigation on the route choice decisions of pedestrians, by producing empirical evidences with an experiment executed in a controlled setting. The experiment involves human participants facing a relatively simple choice among different paths (i.e. choose one of two available gateways leading to the same target area) in which, however, they face a trade-off situation between length of the trajectory to be covered and estimated travel time, considering the level of congestion in the different paths. The data achieved with the experiment are used to design and evaluate a general simulation model for pedestrian route choice. The proposed model firstly considers the fact that other pedestrians are generally perceived as repulsive and that choice of route is generally aimed at avoiding congestion (as for proxemics theory). On the other hand, we also introduce an additional mechanism due to the conjecture that the decision of a pedestrian to reconsider the adopted path is a locally perceivable event that is able to trigger a similar reconsideration by nearby pedestrians, that can imitate the former one. The model is experimented and evaluated in the experiment scenario, for calibration and validation, as well as in a larger scale environment, for exploring the implications of the modeling choices in a more complex situation.

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