Fundamentals of Pedestrian and Evacuation Dynamics

Fundamentals of Pedestrian and Evacuation Dynamics

Andreas Schadschneider (Universität zu Köln, Germany), Hubert Klüpfel (TraffGo HT GmbH, Germany), Tobias Kretz (PTV AG, Germany), Christian Rogsch (University of Wuppertal, Germany) and Armin Seyfried (Forschungszentrum Jülich GmbH, Germany)
Copyright: © 2009 |Pages: 31
DOI: 10.4018/978-1-60566-226-8.ch006
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Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors investigate the behaviour of pedestrians and human crowds, especially their physical movement. Their aim is to build a bridge between the multi-agent and pedestrian dynamics communities that facilitates the validation and calibration of modelling approaches which is essential for any application in sensitive areas like safety analysis. Understanding the dynamical properties of large crowds is of obvious practical importance. Emergency situations require efficient evacuation strategies to avoid casualties and reduce the number of injured persons. In many cases legal requirements have to be fulfilled, for example, for aircraft or cruise ships. For tests already in the planning stage reliable simulation models are required to avoid additional costs for changes in the construction. First, the empirically observed phenomena are described, emphasizing the challenges they pose for any modelling approach and their relevance for the validation and calibration. Then the authors review the basic modelling approaches used for the simulation of pedestrian dynamics in normal and emergency situations, focussing on cellular automata models. Their achievements as well as their limitations are discussed in view of the empirical results. Finally, two applications to safety analysis are briefly described.
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Understanding and predicting the dynamical properties of large human crowds is of obvious practical importance (Schadschneider et al., 2009). Especially emergency situations and disasters require efficient evacuation strategies to avoid casualties and reduce the number of injured persons. In many cases legal regulations have to be fulfilled, e.g. for aircraft or cruise ships. For tests already in the planning stage reliable simulation models are required to avoid additional costs for changes in the construction. But even, if changes in the construction are not possible, simulations can be very helpful for organizational issues like the design of evacuation routes, where full-scale tests are either too expensive or too dangerous.

Multi-Agent Simulation provides a general and powerful framework for understanding and predicting the behaviour of social systems. In this contribution, we describe its application to the dynamics of human crowds, especially their physical movement. The latter restriction allows us to focus on the operational and tactical levels of the agents’ decisions. ‘Operational’ in this context means the proper body motion, i.e. avoiding collisions and the movement within a short time-span (e.g. one second). ‘Tactical’ means that putting this in the well-established BDI-framework (beliefs, desires, intentions), only the intentions (like getting to the closest exit in the case of an evacuation) are explicitly modelled. Desires and beliefs are either neglected or modelled implicitly, e.g. by assuming that everyone wants to get out as fast as possible and representing orientation as following the gradient of a static floor field (for details, please refer to the following sections). Furthermore, the multi-agent paradigm is flexible enough to cover the model extensions that belong to the tactical and strategic realm.

Having said that, the fact that there are such distinct models as cellular automata and molecular dynamics-like simulations used in the field, gives strong hint to the need for a thorough understanding of basic model characteristics, their scope and limitations. This part can be addressed by investigating the models themselves without making reference to empirical data. This is useful but of course not sufficient. Therefore, we will cover the latter in this contribution, too.

In recent years several models of different sophistication have been developed. Macroscopic approaches use a coarse-grained description in terms of densities. In contrast in microscopic models, which are the focus of this review, different agents1 are distinguished. This allows to equip them with different properties reflecting demographics.

In this contribution we will try to give a compact introduction to the most important empirical results and theoretical approaches. All of these are relevant for most agent-based simulations of pedestrian dynamics. We will emphasize the importance of a close interplay of empirical observations and data with theoretical modelling approaches. We demonstrate how the realism of the model dynamics can be improved by taking into account qualitative and quantitative empirical observations. Such validation and finally calibration is extremely important, e.g. for the applications in safety analysis mentioned above.

In Sec.“Empirical Results” we will give an overview of the experimental observations. A variety of interesting dynamical properties and collective effects (fundamental diagram, behaviour at bottlenecks, lane formation in counterflow, flow oscillations etc.) have been found that provide information about the basic interactions and can be used as a kind of benchmark test for any modelling approach. Quantitative results are used to obtain the parameters specifying the interactions between the agents.

We then review in Sec.“Modelling of Pedestrian Dynamics” microscopic approaches to model pedestrian dynamics in normal and emergency situations. Our focus will be cellular automata based models, especially those related to the floor field model. Although the dynamics is often stochastic the cellular automata approach allows an intuitive specification of the motion of pedestrians. It can be easily extended to include not only interactions between different agents but also with the infrastructure, e.g. doors, stairs or walls. In more complex situations an extension to a multi-agent model is possible, e.g. by specifying origin-destination matrices etc.

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List of Reviewers
Table of Contents
Ana Bazzan, Franziska Klügl
Ana Bazzan, Franziska Klügl
Chapter 1
Takeshi Takama
This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both... Sample PDF
Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent
Chapter 2
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Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling... Sample PDF
A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns
Chapter 3
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, Kai Nagel
Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In... Sample PDF
MATSim-T: Architecture and Simulation Times
Chapter 4
Ulf Lotzmann
In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial... Sample PDF
TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications
Chapter 5
Paulo A.F. Ferreira, Edgar F. Esteves, Rosaldo J.F. Rossetti, Eugénio C. Oliveira
Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors... Sample PDF
Applying Situated Agents to Microscopic Traffic Modelling
Chapter 6
Andreas Schadschneider, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried
Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors... Sample PDF
Fundamentals of Pedestrian and Evacuation Dynamics
Chapter 7
Rex Oleson, D. J. Kaup, Thomas L. Clarke, Linda C. Malone, Ladislau Bölöni
The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in... Sample PDF
"Social Potential" Models for Modeling Traffic and Transportation
Chapter 8
Sabine Timpf
In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show... Sample PDF
Towards Simulating Cognitive Agents in Public Transport Systems
Chapter 9
Kurt Dresner, Peter Stone, Mark Van Middlesworth
Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this... Sample PDF
An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles
Chapter 10
Heiko Schepperle, Klemens Böhm
Current intersection-control systems lack one important feature: They are unaware of the different valuations of reduced waiting time of the... Sample PDF
Valuation-Aware Traffic Control: The Notion and the Issues
Chapter 11
Charles Desjardins, Julien Laumônier, Brahim Chaib-draa
This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the... Sample PDF
Learning Agents for Collaborative Driving
Chapter 12
Kagan Tumer, Zachary T. Welch, Adrian Agogino
Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they... Sample PDF
Traffic Congestion Management as a Learning Agent Coordination Problem
Chapter 13
Matteo Vasirani, Sascha Ossowski
The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context... Sample PDF
Exploring the Potential of Multiagent Learning for Autonomous Intersection Control
Chapter 14
Tomohisa Yamashita, Koichi Kurumatani
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New Approach to Smooth Traffic Flow with Route Information Sharing
Chapter 15
Denise de Oliveira, Ana L.C. Bazzan
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in... Sample PDF
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
Chapter 16
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The Merit of Agents in Freight Transport
Chapter 17
Lawrence Henesey, Jan A. Persson
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Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
Chapter 18
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