Waiting pedestrians in the social force model

Abstract

Microscopic simulation of pedestrian traffic is an important and increasingly popular method to evaluate the performance of existing or proposed infrastructure. The social force model is a common model in simulations, describing the dynamics of pedestrian crowds given the goals of the simulated pedestrians encoded as their preferred velocities.The main focus of the literature has so far been how to choose the preferred velocities to produce realistic dynamic route choices for pedestrians moving through congested infrastructure. However, limited attention has been given the problem of choosing the preferred velocity to produce other behaviors, such as waiting, commonly occurring at, e.g., public transport interchange stations.We hypothesize that: 1) the inclusion of waiting pedestrians in a simulated scenario will significantly affect the level of service for passing pedestrians, and 2) the details of the waiting model affect the predicted level of service, that is, it is important to choose an appropriate model of waiting.We show that the treatment of waiting pedestrians have a significant impact on simulations of pedestrian traffic. We do this by introducing a series of extensions to the social force model to produce waiting behavior, and provide predictions of the model extensions that highlight their differences. We also present a sensitivity analysis and provide sufficient criteria for stability.

Walking Ahead: The Headed Social Force Model

“…Relative velocities between pedestrians are instead considered in [22], while [23] uses pedestrians’ absolute velocities to govern the user head-on interactions. The relative positions and velocities provide also a way to account for the stop situation, which cannot be modeled by the original model [24, 25]. For example, [24] proposes three different SFM models for agents that are standing still.…”

“…The relative positions and velocities provide also a way to account for the stop situation, which cannot be modeled by the original model [24, 25]. For example, [24] proposes three different SFM models for agents that are standing still. The models describe the possibility of the agent to avoid incoming humans by coding a step forward/backward behavior, the ability to recover its desired position as well as changing it according to the environmental situation.…”

A Cost Function Approach to the Prediction of Passenger Distribution at the Subway Platform

“…The microscopic cellular automata model is a grid-based discrete model [19], which is more suitable for pedestrian dynamics in the complex environment because of its simplicity and efficiency [20,21]. The social force model is a continuous force-driven model, and its first application mainly focused on emergency evacuation from buildings [22,23]. The agent-based model usually uses the virtual agents to develop the social structure; it provides an innovative perspective to study pedestrian dynamics [24].…”

“…Wu and Ma introduced a new classification method of the crowdedness level at the platform, considering passenger flow characteristics and boarding services [37]. Johansson et al studied the waiting behaviors based on the SFM by introducing a series of extensions [22]. Basically, passenger distribution at the platform, resulting from the waiting area choice behavior, directly affects the congestion degree in carriages, the research on which can provide suggestions to optimize the layout of the platform facilities and thereby adjust passenger distributions.…”

An Evacuation Model for Passenger Ships That Includes the Influence of Obstacles in Cabins

“…However, because of the discrete nature of the cellular automata, this model cannot precisely evaluate the evacuation process. The social force model [14][15][16]] is a continuous model that assumes that individuals are subject to physical and social forces; this model can be extended to include various types of behaviors such as overtaking [17], waiting [18], counter-flow collision avoidance [19], and leader following [20]. However, this type of model is unsuited for large-scale computer simulations because the time required to calculate the interaction forces between agents increases in proportion to the square of the number of agents.…”