Ioakeim G. Georgoudas

An FPGA implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields

This paper studies the on-chip realisation of a dynamic model proposed to simulate crowd behaviour, originated from electrostatic-induced potential fields. It is based on cellular automata (CA), thus taking advantage of their inherent ability to represent sufficiently phenomena of arbitrary complexity and, additionally, to be simulated precisely by digital computers. The model combines electrostatic-induced potential fields to incorporate flexibility in the movement of pedestrians. It primarily calculates distances in an obstacle filled space based on the Euclidean metric. Furthermore, it adopts a computationally fast and efficient method to overcome trouble-inducing obstacles by shifting the moving mechanism to a potential field method based on Manhattan-distance. The hardware implementation of the model is based on FPGA logic. Initialisation of the dedicated processor takes place in collaboration with a detecting and tracking algorithm supported by cameras. The instant response of the processor provides the location of pedestrians around exits. Hardware implementation exploits the prominent feature of parallelism that CA structures inherently possess in contrast to the serial computers, thus accelerating the response of the model. Furthermore, FPGA implementation of the model is advantageous in terms of low-cost, high-speed, compactness and portability features. Finally, the processor could be used as a part of an embedded, real-time, decision support system, aiming at the efficient guidance of crowd in cases of mass egress.

An Anticipative Crowd Management System Preventing Clogging in Exits During Pedestrian Evacuation Processes

This paper presents an anticipative system which operates during pedestrian evacuation processes and prevents escape points from congestion. The processing framework of the system includes four discrete stages: a) the detection and tracking of pedestrians, b) the estimation of possible route for the very near future, indicating possible congestion in exits, c) the proposal of free and nearby escape alternatives, and d) the activation of guiding signals, sound and optical. Detection and tracking of pedestrians is based on an enhanced implementation of a system proposed by Viola, Jones, and Snow that incorporates both appearance and motion information in near real-time. At any moment, detected pedestrians can instantly be defined as the initial condition of the second stage of the system, i.e., the route estimation model. Route estimation is enabled by a dynamic model inspired by electrostatic-induced potential fields. The model combines electrostatic-induced potential fields to incorporate flexibility in the movement of pedestrians. It is based on Cellular Automata (CA), thus taking advantage of their inherent ability to represent effectively phenomena of arbitrary complexity. Presumable congestion during crowd egress, leads to the prompt activation of sound and optical signals that guide pedestrians towards alternative escaping points. Anticipative crowd management has not been thoroughly employed and this system aims at constituting an effective proposal.

Modelling earthquake activity features using cellular automata

Cellular automata (CA) are a powerful technique for modelling otherwise intractably complex systems. On the other hand, earthquake can be defined as a spatially extended dissipative dynamic system that naturally evolves into a critical state with no characteristic time or length scales. In this paper, a two-dimensional CA model capable of reproducing some prominent features of earthquake data is presented. The proposed model with continuous states and discrete time, comprises cell-charges and aims at simulating earthquake activity with the usage of potentials. Several measurements have been carried out at different critical states, leading to different paths to criticality, for various cascade (earthquake) sizes, various cell activities and different neighbourhood sizes. Most notably, the produced simulation results emulate the Gutenberg-Richter (GR) scaling law, in both quantitative and qualitative way. Furthermore, the CA model has been implemented with a user-friendly interface and the user can change several of its parameters, in order to study various hypotheses concerning the aforementioned earthquake activity features.

Real Data Evaluation of a Crowd Supervising System for Stadium Evacuation and Its Hardware Implementation

The aim of this paper is to develop an integrated electronic system that allows the dynamical management of congestion and provides the fast evaluation of dynamical circumstances. Thus, a cellular-automata-based model is proposed that estimates the movement of individuals. The presented system incorporates a process that allows the efficient camera-based initialization of the model, without any special prerequirements. The efficiency of the model has been thoroughly validated. Specifically, simulation-derived diagrams that depict the relationship of flow and speed of people as a function of crowd density have been compared with corresponding diagrams from the literature. Furthermore, the system has been evaluated with the use of real data. In particular, simulation results have been compared with real video recordings that depict the crowd evacuation process from a football stadium. Results prove that the proposed management system can estimate fast possible routes of people for the very near future, evaluating all possible exit alternatives. Finally, the proposed model has been implemented in hardware with a field-programmable gate array, enabling its incorporation into an integrated electronic system that estimates crowd movement and prevents congestion in exits almost in real time. The proposed electronic system is advantageous in terms of easy incorporation and portability as well as performance when compared with its analogous graphical-processing-unit implementation.

A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas

The movement of large numbers of people is important in many situations, such as the evacuation of a building in an emergency. In this paper, pedestrian dynamics during the evacuation of large areas is simulated using a computational intelligent technique, based on Cellular Automata. The characteristic feature of the proposed model is that the crowd consists of independent parts rather than treated as homogeneous mass. The crowd behaviour is artificially formatted by the response of each of these parts to the rule according to which each pedestrian reaches one of the possible exits. Furthermore, an efficient graphical user interface has been developed, in order to study various hypotheses concerning the pedestrians’ activity features. Collisions among pedestrians have been encountered while collective effects prominent at crowd behaviour have been also realised during simulation. Finally, the presence of fixed as well as user-defined moveable obstacles has been taken into account.

An Intelligent Cellular Automaton Model for Crowd Evacuation in Fire Spreading Conditions

In this paper, a two-dimensional Cellular Automaton (CA) model simulates the evacuation process of a crowd responding to fire spread. The crowd consists of individuals and its behaviour is modelled by the response of each individual to a rule that directs him/her to the nearest exit. Furthermore, fire spreading and movements of the crowd members while approaching the fire are successfully simulated. Empirical studies and socio-psychological concepts that attempt to explain how individuals act under fire threat have been considered. Characteristic features of crowd dynamics, such as incoherent pedestrian motion, blockings in front of exits and mass behaviour are successfully simulated. An efficient user-friendly interface has been equipped with parameters defining the arrangement of the area, crowd formation and fire features. Finally, the model is executed fast on typical PCs and can be used for planning evacuation strategies under fire threat or as part of a real-time decision support system.

A cellular automaton model for crowd evacuation and its auto-defined obstacle avoidance attribute

In this paper, a crowd evacuation model based on Cellular Automata (CA) is described. The model takes advantage of the inherent ability of CA to represent sufficiently phenomena of arbitrary complexity and to be simulated precisely by digital computers as well. Pedestrian movement depends on their distance from the closest exit, which is defined dynamically. The adoption of Manhattan distance as the reference metric provides calculation simplicity, computational speed and improves significantly computational performance. Moreover, the model applies an efficient method to overcome obstacles. The latter is based on the generation of a virtual field along obstacles. A pedestrian moves along the axis of the obstacle towards the direction that the field increases its values, leading her/him to avoid the obstacle effectively. Distinct features of crowd dynamics and measurements on different distributions of pedestrians have been used to evaluate the response of the model.

CA Crowd Modeling for a Retirement House Evacuation with Guidance

This paper studies the impact of guidance on evacuation processes. A Cellular Automata (CA) based model has been, therefore, elaborated in order to introduce group categorization and guidance attributes. The crowd is categorized according to motional skills and a special group is assigned leadership features. The presented scenario includes the evacuation of a retirement house with the help of the nursing staff. Simulation results prove the significance of proper guidance. The latter optimizes the response of the model by activating alternative routes that decrease congestion levels in front of exits.

Follow-the-Leader Cellular Automata Based Model Directing Crowd Movement

This paper describes a model that simulates crowd movement incorporating an efficient follow-the-leader technique based on cellular automata (CA). The scope of the method is to derive principal characteristics of collective motion of biological organisms, such as flocks, swarms or herds and to apply them to the simulation of crowd movement. Thus, the study focuses on the massive form of the movement of individuals, which is lastingly detected macroscopically, during urgent circumstances with the help of some form of guidance. Nevertheless, on a lower level, this formation derives from the application of simple local rules that are applied individually to every single member of the group. Hence, the adoption of CA-based formation has allowed the development of a micro-operating model with macro-features. Furthermore, the model takes advantage of the inherent ability of CA to represent sufficiently phenomena of arbitrary complexity. The response of the model has been evaluated through different simulation scenes that have been developed both in two and three dimensions.

A cellular automaton crowd tracking system for modelling evacuation processes

Crowd safety and comfort in highly congested places not only depend on the design and the function of the place, but also on the behaviour of each individual In this paper, an integrated evacuation system is described The proposed system comprises three stages The main stage includes an efficient computational tool based on Cellular Automata (CA) capable of simulating main features of pedestrian dynamics during the evacuation of large areas, supported by a multi-parameterised graphical-user interface (GUI) Moreover, an image-processing tracking algorithm is used for the calibration of the system providing all the necessary information about the number of individuals and their distribution in the under test area Finally, the VLSI implementation of the proposed model is straightforward due to the simplicity of the CA rule, thus leading to the design of a dedicated processor.