Crowd Modeling and Simulation for Safer Building Design

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Optimal exit configuration of factory layout for a safer emergency evacuation using crowd simulation model and multi-objective artificial bee colony optimization

This work aims at providing a systematic method in producing a safer and optimal factory layout based on a crowd simulation model and the multi-objective artificial bee colony optimization technique. Apart, from ensuring the efficiency of manufacturing processes in planning a factory layout, it is also important that the safety aspect is taken into account. A factory is usually a closed working area consisting of machines, equipment, assembly lines as well as individual working space and other departments within the factory. In this environment, workers move around in the factory to perform different activities, and hence highly complex crowd behaviours that are influenced by the physical, social and psychological factors of the crowd might take place. Therefore, the layout of the factory must be carefully designed so that efficient movements of people can be obtained. Furthermore, during emergency situations that require efficient evacuation of workers from the factory building, a good factory layout will prevent or minimize the possibility of injuries during the evacuation process. This will reduce the evacuation egress time, which is the quantity used to evaluate the evacuation efficiency and the building's level of safety. One of the techniques to assess the evacuation efficiency of a particular space configuration is by using the crowd simulation model. Recent evidences suggest that the representation of crowd dynamics using a simulation model is useful, where experiments with real humans are too dangerous and not practical to be implemented. This work explains the method to provide optimal exit door configurations for a factory layout by analyzing the crowd evacuation time and the discomfort level, where the proposed optimum exit configurations will be compared with the original configuration for a better evacuation efficiency

Modelling human network behaviour using simulation and optimization tools: the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models.Peer Reviewe

Literature Review on Big Data Analytics Methods

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed

Path Planning for Robot and Pedestrian Simulations

The thesis is divided into two parts. The first part presents a new proposed method for solving the path planning problem to find an optimal collision-free path between the starting and the goal points in a static environment. Initially, the grid model of the robot's working environment is constructed. Next, each grid cell's potential value in the working environment is calculated based on the proposed potential function. This function guides the robot to move toward the desired goal location, it has the lowest value at the goal location, and the value increase as the robot moves further away. Next, a new method, called Boundary Node Method (BNM), is proposed to find the initial feasible path. In this method, the robot is simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves in the working environment toward the goal point with eight-boundary nodes based on the boundary nodes' characteristics. In the BNM method, the initial feasible path is generated from the sequence of the waypoints that the robot has to traverse as it moves toward the goal point without colliding with obstacles. The BNM method can generate the path safely and efficiently. However, the path is not optimal in terms of the total path length. An additional method, called Path Enhancement Method (PEM), is proposed to construct an optimal or near-optimal collision-free path. The generated path obtained by BNM and PEM may contain sharp turns. Therefore, the cubic spline interpolation is used to create a continuous smooth path that connects the starting point to the goal point. The performance of the proposed method is compared with the other path planning methods in terms of path length and computational time. Moreover, the multi-goal path planning problem is investigated to find the shortest collision-free path connecting a given set of goal points in the robot working environment. Furthermore, to verify the performance of the proposed method, several experimental tests have been performed on the e-puck robot with different obstacle configurations and various positions of goal points. The experimental results showed that the proposed method could construct the shortest collision-free path and direct the real physical robot to the final destination point. At the end of the first part of the thesis, we investigate the multi-goal path planning problem for the multi-robot system such that several robots reach each goal. In the second part of this thesis, we proposed a new method for simulating pedestrian crowd movement in a virtual environment. The first part of this thesis concerning the generation of the shortest collision-free path is used. In this method, we assumed that the crowd consists of multiple groups with a different number and various types of pedestrians. In this scenario, each group's intention is different for visiting several goal points with varying sequences of the visit. The proposed method uses the multi-group microscopic model to generate a real-time trajectory for each pedestrian navigating in the pedestrianized area of the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian' behaviours. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes, such as position, velocity, etc. Moreover, pedestrians optimize their path independently toward the desired goal points while avoiding obstacles and other pedestrians in the scene. At the end of this part of the thesis, a statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. The proposed method implemented for several simulation scenarios under a variety of conditions for a wide range of different parameters. The results showed that the proposed method is capable of describing pedestrian' behaviours in the virtual environment

CellEVAC: an adaptive guidance system for crowd evacuation through behavioral optimization

A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cellbased pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation?optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%).Ministerio de Economía y Competitivida

人・ユーザー中心の移動サービスと群集マネジメントのためのモデリング，シミュレーションと最適化

Tohoku University博士（情報科学）thesi