RONALD, NICOLE AMY Title: Agent-based ap

127 Pages·2007·2.97 MB·English

Agent-based Approaches To Pedestrian Modelling
Nicole Amy Ronald
Submitted in partial fulfillment of the requirements of the degree
Master of Engineering Science
in the Department of Computer Science and Software Engineering
of The University of Melbourne
February 2007
Produced on acid-free paper
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Abstract
Thisthesisinvestigatestheearlystagesofthesoftwaredevelopmentprocessforagent-basedmodels
of pedestrian behaviour. Planning for pedestrians is becoming more important as planners and
engineers become more aware of the sustainability and environmental aspects of transport and
infrastructure. It is also necessary for the planning and management of pedestrian areas and
events. Pedestrianbehaviour is more difficult to model than other transportmodes as it is not as
constrained and operates at a finer scale.
Many approaches have been developed for modelling pedestrian behaviour. The simplest in-
volve a single mathematical equation taking into account area and attractiveness of an area to
calculate the maximum capacity. More complicated mathematical models involving differential
equations have also been used.
Agent-basedmodellingisarecentdevelopmentinmodellingandsimulation. Thesesimulations
containagents whointeractwitheachotherandtheenvironment inwhichtheyaresituated. Their
similarity to human societies has led to their use for many social applications. Many modellers
are unsure of what agents are and how to develop models using them. In some cases, agents
may be useful. In other cases, the model outputs and realism may not offset the learning curve,
development time, and increased complexity of an agent-based model.
The thesis uses the overall framework of three nested processes – the planning process, the
modelling process, and the software development process – to explore pedestrian modelling.
Aframeworktoassistinselectinganappropriateapproachformodellingpedestrianbehaviour
is developed, using the domain and requirements developed by the client in the planning process
as inputs. Given the range of approaches and research- or industry-based models now available,
this framework is necessary to increase confidence in the pedestrian modelling undertaken for
the planning process. This framework forms the first contribution of this thesis and assists in
understanding who is involved in the modelling process and the different approaches involved.
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Thedevelopmentoftwoprototypesofagent-basedpedestrianbehaviourisdescribed. Thefirst
prototype uses the popular agent-based cellular automata (CA) approach to model movement in
a corridor. The second prototype uses intelligent agents using the belief-desire-intention (BDI)
architecture. The twoprototypesare the secondcontributionofthis thesis andprovideaninsight
into the software development process required for agent-based models and the applicability of
these models to pedestrian behaviour.
A set of evaluation criteria is developed, based on software engineering quality principles, to
evaluate the two prototypes. The CA prototype is particularly suited to modelling movement
because of the explicit spatial representation. However, the lack of scalability causes problems
for larger simulations and high-level behaviour, such as planning, requires additional non-CA
modules. The BDI prototype was more difficult to design and implement, as the spatial location
of the agents must be defined by the user. It is useful for modelling human behaviour, but lack
of knowledge about how pedestrians move at a low level means that it is limited to high-level
movement and planning.
Declaration
I certify that
1. other than as specified in the preface, this thesis comprises my original work;
2. due acknowledgment has been made in the text to all other material used;
3. the thesis consists of approximately 30,000 words, exclusive of tables, maps, bibliographies
and footnotes.
Nicole Ronald
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Acknowledgements
I am grateful to the Transportgroupin the Melbourne office of Sinclair Knight Merz for allowing
me to undertake this degree and work more flexible hours at the beginning of this project. The
involvement in the pedestrian modelling for the Melbourne 2006 Commonwealth Games was a
notable inspiration for this project – thanks to James Larratt and Craig McPherson (amongst
others) for winning the tender.
I am also grateful to both my supervisors. Leon Sterling was responsible for setting me up
with some teaching experience and making suggestions that only made sense three months later.
Michael Kirley and his never-ending questions were appreciated despite the grumpy looks.
My current employer, the Department of Computer Science and Software Engineering at the
University of Melbourne, provided me with a lovely new staff machine (solitaire), which has
coped with model runs much better than my old postgrad machine and also my laptop (oddjob).
My vintage ’99 desktop (octopussy)also helped with typing papers and this thesis.
My proofreader and sounding board Julien Fischer was also a great help, despite his many
complaintsaboutagents. MymotherJillRonaldpointedoutthatIshouldusepeople’sfullnames
in the acknowledgements. I thank both of them for their assistance.
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Contents
Preface xvii
1 Introduction 1
1.1 Aim and scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 The complexity of pedestrianism 5
2.1 Transport planning and modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 The planning process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 Classical transport modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Data collection for modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Complex systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Pedestrian behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Planning for pedestrians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Crowding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.3 Urban behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.4 Cognitive maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.5 Wayfinding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Pedestrian modelling approaches 19
3.1 Environment representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Pedestrian modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Microsimulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Physical and mathematical approaches . . . . . . . . . . . . . . . . . . . . . . . . . 23
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3.5 Agent-based approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.5.1 Agent-based simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.5.2 Geosimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.5.3 Agent-based cellular automata approaches . . . . . . . . . . . . . . . . . . . 28
3.5.4 Agent-based approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5.5 Hybrid models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Approach framework 35
4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Stakeholders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Framework elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.1 Categorisationof domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.2 Categorisationof behaviours . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3.3 Categorisationof approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.3.4 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5 Model prototypes 49
5.1 The software development process . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 Agent-based cellular automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.2.2 Model development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2.3 Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2.4 Experimental results and discussion . . . . . . . . . . . . . . . . . . . . . . 59
5.3 BDI agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3.2 Model development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3.3 Operational assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6 Model evaluation and discussion 81
6.1 Evaluation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.1.1 Functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82