They calibrate the model parameters with a behavioural-based approach that relies on observed motion behaviours. In this study, they focus on the implementation and calibration of an agent-based model that microscopically simulates the interactions between individuals and with the environment. In this context, the authors deem crucial to build a pedestrian dynamics simulation framework that they can completely control, from the modelling and calibration under different flow conditions to the implementation of a user-friendly tool that allows testing the model in a variety of case studies.
As a consequence, there has been growing interest in developing methodologies for analysing the walking transport mode.
Moreover, the quality and comfort level of available walking environments play a key role in the challenge of sustainable mobility. Pedestrian flow efficiency and safety are primary requirements in the effective configuration and management of urban gathering spaces, such as railway stations, stadiums, or shopping malls. We suggest that an attendee cluster can trigger jamming transitions not only by reducing the available walking space but also by increasing conflicts among pedestrians near the attraction. It is also observed that the extended jam phase can turn into freezing phase with a certain probability when pedestrian flux is high with strong social influence.
On the other hand, a different transition behavior appear in the unidirectional flow scenario, i.e., from free flow to localized jam, and then to extended jam phases. For bidirectional flow scenario, we observe a transition from free flow to freezing phases in which oppositely walking pedestrians reach a complete stop and block each other. By controlling pedestrian influx and the social influence parameter, we identify various pedestrian flow patterns. We formulate the joining probability as a function of social influence from others, reflecting that individual choice behavior is likely influenced by others. We numerically study jamming transitions in pedestrian flow interacting with an attraction, mostly based on the social force model for pedestrians who can join the attraction. Pedestrian facility management, for instance, for retail stores. The findings from this study can be understood in the context of the Identify under what conditions enhancing these variables would be moreĮffective. Strength of the social influence and the average length of stay enables us to Measuring the marginal benefits with respect to the Pedestrian behavior are summarized in a phase diagram by measuring the While others walk in their desired direction. If the social influence is not strong enough, an unsaturated phaseĪppears where one can observe that some pedestrians head for the attraction Saturated phase can be defined at which all the pedestrians have visited theĪttraction. When the social influence is strong along with a long length of stay, a Numerical simulations exhibit different patterns of pedestrian behaviorĭepending on the strength of the social influence and the average length of Step to incorporate the switching behavior, this study investigates collectiveĮffects of switching behavior for an attraction by developing a behavioral Their attention towards the attractions, namely switching behavior. They can be influenced by the attractions and some of them might shift Walking on the streets, pedestrians can be aware of attractions like shopping Other places but also for interacting with surrounding environment. Walking is a fundamental activity of our daily life not only for moving to