A modified social force model for crowd dynamics

Journal of Computers

This article proposes a new pedestrian model that precisely simulates the avoidance behavior so as to realize the pedestrians' walking action. For the design of the facility, the simulation is used as a means for predicting and analyzing the movement of pedestrians. However, most of the simulators focus on the flow of pedestrian but do not consider the movement of each pedestrian. As a result, conventional simulators are useful to simulate the phenomenon of macro level movement of pedestrian but are not useful to simulate the micro-level simulation. Therefore, we aim to implement the pedestrian flow simulator that enables us to observe each pedestrian behavior by using the Multi-Agent System. Research on pedestrian model began in the 1970s, but all models cannot simulate the behavior of the pedestrian individual well. Social Force Model, which is one of the typical pedestrian models, reproduces the behavior using the equation of motion by considering the mass point pedestrian. Accordingly, this model is effective in high-density state. By contrast, there is several problems in low-density state: the avoidance speed and trajectory. Hence, we propose a new model by introducing the concept of sub-goal and new force in order to solve this problem. Furthermore, the evaluation experiment shows that proposal model improves these problems.

Engineering Heritage Journal, 2017

Applied Mathematics & Information Sciences, 2013

Incorporating decision making capability into a microscopic crowd dynamic model is an essential factor for introducing a well-representative model for the pedestrian flow. In normal situations, this capability was presented by providing the simulated independent pedestrians in the Social Force Model an investigation ability of their walkways. However, predicting semi-blocked situations by the simulated intelligent pedestrians has not been treated in their investigation. In this paper, the authors describe the investigation capability of independent pedestrians in normal situations. In addition, the prediction aspect is introduced for the simulated independent pedestrians as an extension for the investigation capability to let the model appear more representative of what actually happens in reality. Simulations are performed to validate the work qualitatively by tracing the behavior of the simulated pedestrians and studying the impact of this behavior on their interactions with the grouped pedestrians. Finally, a comparison between the extended model and the original model with regards to the quantitative measurement (the efficiency of motion) is made.

Spring Simulation Conference (SpringSim 2020), 2020

The nature of the social relationship within a pedestrian group influences the group's structure and behavior and thus its entitativity (i.e. the perception of a group as a unit by other pedestrians). However, existing crowd models ignore the diversity of social relationships and have limitations in reproducing group avoidance behaviors. The proposed model is an adaptation of the social force model that addresses group social relationships. The approach is calibrated by comparing the distances and angles between members of the simulated groups with observations in real crowds. Results show that intra-group distances are a key factor in collision avoidance behavior. Simulation of collision avoidance shows that group members behavior fits better with empirical data than the original model and that individuals avoid splitting groups. By simply tuning the distribution of social relationships in the simulated crowd, the model can be used to reproduce crowd behaviors in several contexts.

Journal of Advanced Transportation

2011

The simulation of pedestrian dynamics is a consolidated area of application for agent-based based models; however generally the presence of groups and particular relationships among pedestrians is treated in a simplistic way. This work describes an innovative agent-based based approach encapsulating in the pedestrian's behavioural model effects representing both proxemics and a simplified account of influences related to the presence of groups in the crowd. The model is tested in a simple scenario to evaluate the effectiveness of mechanisms to preserve groups cohesion maintaining a plausible overall crowd dynamic.

Physica A: Statistical Mechanics and its Applications, 2007

Crowd scenarios have attracted attention from computer modellers, perhaps because of the impracticality of studying the phenomenon by traditional experimental methods. For example, Kirchner has proposed an agent-based crowd model inspired by fields of elementary particles [2], but chose not to incorporate crowd forces. We argue that crowd forces (and associated injuries) are an essential characteristic of crowds, and that their omission will negatively affect the model's ability to make predictions (e.g. time for a crowd to pass through an exit). To support this position we describe an evolution of Kirchner's model that includes a vector-based particle field to represent forces. We show qualitative and quantitative differences compared to Kirchner's model when force is included. The Swarm Force model demonstrates-by showing non-linear effects of force-the necessity of force in crowd models.

An analytical study is proposed in this paper to investigate pedestrian crowd dynamics from a multidisciplinary approach (i.e. traffic engineering and social science) focusing on the impact of environment physical features and social interaction on pedestrian movement dynamics in high-density situations on pedestrian movement dynamics in highdensity situations. Taking advantage of previous studies that highlighted the importance of turning movements of crowd during evacuations, we empirically investigated the impact of angled paths on orderly crowd egress flows. We also proposed to consider the social interaction among pedestrians, taking into account the presence of groups and their proxemics behavior while walking. Results of the flow rates level and walking speed of different scenarios studied in this work are presented (0°, 45°, 60° and 90° angle degrees). These showed that in high-density situations the walking speed of group members was lower compared to the singles within all scenarios studied, because of the need to stay close to own group members while walking. Likewise, the angle path with 60° degrees (compared to the scenario of corridor with 0° degrees) has a significant negative impact on both the flow rate and walking speed. These results could be of notable interest for all generic crowd models aiming at replicating crowd dynamics.

International journal of engineering research and technology, 2021

All Trips at some point need to be completed by walking. There are various models, which are used to simulate pedestrian observed flow with the real world. A literature study is performed in which we found that Social force model can be preferably used among other models like Cellular Automata model, Magnetic force model, Centrifugal force model. Social force model is one of the models which predicts a lot of observed phenomenon like lane formation, faster is slower, etc. The main aim of this work is to investigate whether the Social force model can deal with big data or not. The aim was to calibrate and validate the social force model with real-world data. If the Social Force model gets calibrated and validated with real-world data, then one can say that Social force model can be used to capture the variability and real-world pedestrian behavior. The pedestrian data is collected by drones. After the extraction of data, it was seen that it contains a lot of noise as speed was fluctuating too much, and while the observed video does not show such behavior. The noise errors in drone data may have occurred due to inaccuracies in the instrument, measurement error and bad weather. Therefore, data smoothening has been done with the help of filters which reject the value above a specific range of frequency. Data smoothening was essential to clean the data for further analysis. Five parameters are chosen i.e. A, B, Relaxation time, Lambda and Desired speed for the purposes calibration. These parameters were calibrated with the help of genetic algorithm by which minimizes the difference between values of observed distance and simulated distance. Initially, only one pedestrian is simulated while others move according to their observed trajectories. Later, the ten pedestrians were simulated simultaneously. Ideally, all the pedestrian should have been simulated together to minimize the error. However, as the genetic algorithm was taking lots of time to minimize the error, more than ten pedestrians was not considered for simulation simultaneously. Errors are noted in case of one pedestrian, two pedestrians and 5 pedestrian simulation together. Further, speed, flow and density variation were explored in the entire stretch over time. Further, the fundamental relation between these three parameters was also explored.

The simulation of pedestrian dynamics is a consolidated area of application for agent-based models: successful case studies can be found in the literature and off-the-shelf simulators are commonly employed by decision makers and consultancy companies. These models, however, generally do not consider the explicit representation of pedestrians aggregations (groups), the related occurring relationships and their dynamics. This work is aimed at discussing the relevance and significance of this research effort with respect to the need of empirical data about the 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 encapsulating in the pedestrian's behavioural specification effects representing both traditional individual motivations (i.e. tendency to stay away from other pedestrians while moving towards the goal) and a simplified account of influences related to the presence of groups in the crowd. The model is tested in a simple scenario to evaluate the implications of some modeling choices and the presence of groups in the simulated scenario. Moreover, the model is applied in a real world scenario characterized by the presence of organized groups as an instrument for crowd management. Results are discussed and compared to experimental observations and to data available in the literature.