Experimental Study of Phase Transition in Pedestrian Flow

The transition between low and high density phases is a typical feature of systems with social interactions. This contribution focuses on simple evacuation design of one room with one entrance and one exit; four passing-through experiments were organized and evaluated by means of automatic image processing. The phase of the system, determined by travel time and occupancy, is evaluated with respect to the inflow, a controlled boundary condition. Critical values of inflow and outflow were described with respect to the transition from low density to congested state. Moreover, microscopic analysis of travel time is provided.Comment: To appear in proceedings of Pedestrian and Evacuation Dynamics 2014, Transportation Research Procedia, Elsevie

Two dimensional outflows for cellular automata with shuffle updates

In this paper, we explore the two-dimensional behavior of cellular automata with shuffle updates. As a test case, we consider the evacuation of a square room by pedestrians modeled by a cellular automaton model with a static floor field. Shuffle updates are characterized by a variable associated to each particle and called phase, that can be interpreted as the phase in the step cycle in the frame of pedestrian flows. Here we also introduce a dynamics for these phases, in order to modify the properties of the model. We investigate in particular the crossover between low- and high-density regimes that occurs when the density of pedestrians increases, the dependency of the outflow in the strength of the floor field, and the shape of the queue in front of the exit. Eventually we discuss the relevance of these results for pedestrians.Comment: 20 pages, 5 figures. v2: 16 pages, 5 figures; changed the title, abstract and structure of the paper. v3: minor change

Data Assimilation for Agent-Based Simulation of Smart Environment

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to provide inference about system state based on the incomplete, ambiguous and uncertain sensor data using a computer model. A typical data assimilation framework consists of a computer model, a series of sensors and a melding scheme. The purpose of this dissertation is to develop a data assimilation framework for agent-based simulation of smart environment. With the developed data assimilation framework, we demonstrate an application of building occupancy estimation which focuses on position estimation using the framework. We build an agent based model to simulate the occupants’ movement s in the building and use this model in the data assimilation framework. The melding scheme we use to incorporate sensor data into the built model is particle filter algorithm. It is a set of statistical method aiming at compute the posterior distribution of the underlying system using a set of samples. It has the benefit that it does not have any assumption about the target distribution and does not require the target system to be written in analytic form .To overcome the high dimensional state space problem as the number of agents increases, we develop a new resampling method named as the component set resampling and evaluate its effectiveness in data assimilation. We also developed a graph-based model for simulating building occupancy. The developed model will be used for carrying out building occupancy estimation with extremely large number of agents in the future

Role of opinion sharing on the emergency evacuation dynamics

Emergency evacuation is a critical research topic and any improvement to the existing evacuation models will help in improving the safety of the evacuees. Currently, there are evacuation models that have either an accurate movement model or a sophisticated decision model. Individuals in a crowd tend to share and propagate their opinion. This opinion sharing part is either implicitly modeled or entirely overlooked in most of the existing models. Thus, one of the overarching goal of this research is to the study the effect of opinion evolution through an evacuating crowd. First, the opinion evolution in a crowd was modeled mathematically. Next, the results from the analytical model were validated with a simulation model having a simple motion model. To improve the fidelity of the evacuation model, a more realistic movement and decision model were incorporated and the effect of opinion sharing on the evacuation dynamics was studied extensively. Further, individuals with strong inclination towards particular route were introduced and their effect on overall efficiency was studied. Current evacuation guidance algorithms focuses on efficient crowd evacuation. The method of guidance delivery is generally overlooked. This important gap in guidance delivery is addressed next. Additionally, a virtual reality based immersive experiment is designed to study factors affecting individuals\u27 decision making during emergency evacuation

Modelling and simulation of several interacting cellular automata

Cellular Automata are used for modelling and simulation of many systems. In some applications, the system is formed by a set of subsystems that can be modelled separately, but, in such cases, the existence of interactions between these subsystems requires additional modelling and computer programming. In this paper we propose a modelling methodology for the simulation of a set of Cellular Automata models that interact with each other. The modelling methodology is described, together with an insight on implementation details. Also, it is applied to a particular Cellular Automata model, the Sanpile Model, to illustrate its use and to obtain some example simulations

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities

The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such away that temporal dynamics is intrinsic to their representation and explanation. Someof the clearest examples involve local movement from conventional traffic modelingto disaster evacuation where congestion, crowding, panic, and related safety issue arekey features of such events. We propose that such events can be simulated using newvariants of pedestrian model, which embody ideas about how behavior emerges fromthe accumulated interactions between small-scale objects. We present a model inwhich the event space is first explored by agents using ?swarm intelligence?. Armedwith information about the space, agents then move in an unobstructed fashion to theevent. Congestion and problems over safety are then resolved through introducingcontrols in an iterative fashion and rerunning the model until a ?safe solution? isreached. The model has been developed to simulate the effect of changing the route ofthe Notting Hill Carnival, an annual event held in west central London over 2 days inAugust each year. One of the key issues in using such simulation is how the processof modeling interacts with those who manage and control the event. As such, thischanges the nature of the modeling problem from one where control and optimizationis external to the model to one where this is intrinsic to the simulation

Real-time Emergency Response through Performant IoT Architectures

International audienceThis paper describes the design of an Internet of Things (IoT) system for building evacuation. There are two main design decisions for such systems: i) specifying the platform on which the IoT intelligent components should be located; and ii) establishing the level of collaboration among the components. For safety-critical systems, such as evacuation, real-time performance and evacuation time are critical. The approach aims to minimize computational and evacuation delays and uses Queuing Network (QN) models. The approach was tested, by computer simulation, on a real exhibition venue in Alan Turing Building, Italy, that has 34 sets of IoT sensors and actuators. Experiments were performed that tested the effect of segmenting the physical space into different sized virtual cubes. Experiments were also conducted concerning the distribution of the software architecture. The results show that using centralized architectural pattern with a segmentation of the space into large cubes is the only practical solution