Modeling Environmental Operative Elements in Agent-Based Pedestrian Simulation

Models for pedestrian simulation are employed on a day-to-day basis for supporting the design and planning of the built environment in normal and evacuation situations. One of the aspects that are least investigated in the community, probably because it is considered closer to technology transfer than to research, is the modelling of operational elements of the simulated environment. The present paper briefly describes an agent-based approach to the representation of operative elements of the environment with particular attention to the mechanisms of interaction between these active objects and pedestrians

Adaptive pedestrian behaviour for the preservation of group cohesion

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). 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. 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. 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)

Lane Formation Beyond Intuition Towards an Automated Characterization of Lanes in Counter-flows

Pedestrian behavioural dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between unidirectional and bidirectional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. The collective behaviour of lane formation emerges in condition of variable density and due to a self-organisation dynamic, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-) automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through an inter-rater agreement test between the results achieved by a human coder and by the algorithm

Calibration and validation of a simulation model for predicting pedestrian fatalities at unsignalized crosswalks by means of statistical traffic data

This work presents a simulation model for unsignalized crosswalks which takes into account collisions between vehicles and pedestrians, thus allowing to assess the estimated yearly pedestrian fatality. In particular, we focus on a method to calibrate such a model combining measurable crosswalk characteristics, such as maximum speed limit or drivers' compliance, with statistical data for past accidents obtained from local municipality. In order to perform simulations under realistic conditions, we constructed a one-week scenario where pedestrian and vehicle traffic vary using specific patterns each hour of the week. The constructed traffic profile is based on openly available data and the suitability for the scenario considered (a crosswalk in Milan, Italy) is investigated showing that cultural/lifestyle elements determine the variation of weekly traffic. Simulations using the constructed one-week scenario were used to obtain the only non-measurable parameter which account for pedestrians' and drivers' distraction. In addition, we also focused on the presence of elderly pedestrians which have different physiological characteristics compared to adults or children and are becoming an important part of the population in several countries around the globe. The simulation model presented here and the method suggested for calibration may be employed in different contexts, thus allowing to build an important tool to be used not only for transportation efficiency/optimization but also for safety analysis. Keywords: Traffic simulation, Accident prevention, Unsignalized crosswalk, Vehicle-pedestrian interaction, Weekly traffic variatio