Wandering crowd simulation based on space syntax theory

Space syntax has proven there appears to be a fundamental process that informs human and social usage of an environ- ment, and the effects of spatial configuration on movement patterns are consistent with a model of individual decision behavior. In- troducing space syntax to crowd simulation will enable space structure guide the random movement of the crowd with no specific targets. This paper proposes a simulation method of the wandering crowd, which calculates the crowd distribution corresponding to the space through space syntax and uses a hybrid path planning algorithm to dynamically navigate the crowd to conform to the dis- tribution. Experiments show the presented method can obtain reasonable and vision-realistic simulation results.

Simulation of Crowd Motion Based on Boids Flocking Behavior and Social Force Model

In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.

An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

Crowd evacuation simulation using virtual reality(VR)is significant for digital emergency response construction.However,existing evacuation simulation studies suffer from poor adaptation to complex environments,inefficient evacuations,and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation.To more realistically express the crowd evacuation results obtained under the influence offire environments and the subjective consciousness of pedestrians in subway stations,we designed a dynamic pedestrian evacuation path planning method under multiple constraints,analysed the influences of an‘environmental role’and a‘subjective initiative’on crowd evacuation,established an improved social force model(ISFM)-based crowd evacuation simulation method in VR,developed a prototype system and conducted experimental analyses.The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity.In simulation experiments without disaster scenarios,the improved model’s crowd evacuation efficiency improved by averages of 12.53%and 15.37%over the commercial Pathfinder software and the original social force model,respectively.The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subwayfires.

Crowd Simulation and Its Applications: Recent Advances

This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation,pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field.

Optimized evacuation route based on crowd simulation

An evacuation plan helps people move away from an area or a building. To assist rapid evacuation,we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis,we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed,and determines the routes in a hierarchical manner.This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results,with visualizations, demonstrate the feasibility of our evacuation route optimization method.

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

This paper describes a novel model known as the shadow obstacle model to generate a realistic comer-tuming behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a comer. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic comer-turning behavior by comparison with real data obtained from the experiments. Finally, we per- form parameter analysis to show the believability of our model through a series of experiments.

A modified universal pedestrian motion model: Revisiting pedestrian simulation with bottlenecks

Pedestrian flow through narrow exits is one the most important features of crowd dynamics and evacuation.This is a particularly important aspect of pedestrian simulation models in that the accuracy is highly dependent on their ability to produce realistic exit flow rates.We firstly identified the four parameters that are most critical for physical interactions of the social force model and then calibrated them against two well-controlled pedestrian experiments.With these calibrated parameters,we discussed the reasonable settings of sensitive parameters for different levels of pedestrian competitiveness.Then,we revisited the basic questions about the effect of the exit location,the bottleneck length,and the effect of obstacles on pedestrian egress.Our simulation results indicated that:(1)The effect of the exit location on the pedestrian egress efficiency is uncertain,and the evacuation efficiency is also related to the exit width and the level of urgency.(2)The"pass-way"after the exit also named as the bottleneck length has a negative impact on the evacuation performance only in the scenarios that the bottleneck length is not more than 2.0 meters.When the bottleneck length exceeds 2.0 meters,pedestrian outflow efficiency reaches an asymptotic.(3)Setting an obstacle near an exit is not leading to a longer pedestrian evacuation time,instead,it is effectively improving pedestrian evacuation.

Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios

To avoid crowd evacuation simulations depending on 2D environments and real data,we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning(DRL)and 3D physical environments(3DPEs).In 3DPEs,we construct simulation scenarios from the aspects of geometry,semantics and physics,which include the environment,the agents and their interactions,and provide training samples for DRL.In DRL,we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy.With a unified configuration,we conduct evacuation simulations.In scenarios with one exit,we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics(number,mass and height)on evacuation.In scenarios with two exits and a uniform(nonuniform)distribution of agents,we explore the impact of exit characteristics(width and relative position)and agent characteristics(height,initial location and distribution)on agent exit selection and evacuation.Overall,interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation.