design and implementation of a simulation tool...

PROJECT TITLE: EXPLORING THE COMPLEXITY OF POLICY DESIGN

DESIGN AND IMPLEMENTATION OF A SIMULATION TOOL:

OPERATIONALIZATION OF CORE CONCEPT (WP5)

Due date of deliverable: July 2012

Actual Submission date: 21st July 2012

Leading Institution for this project: Instituto Superior Técnico

BRT - ALC LS2 - EXPOD - EXPLORING THE COMPLEXITY OF POLICY DESIGN

WP 5 – Design and Implementation of SD Tool

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DOCUMENT ID: EXPS2_WP5_Draft_V4

DOCUMENT NAME: Exploring the complexity of Policy Design

WP5 - Design and implementation of SD tool

TYPE OF DOCUMENT: Report

STATUS: Draft

DISSEMINATION LEVEL: Restricted

WP ALLOCATION: WP5

MAIN AUTHOR(S): Luis Martínez, Vasco Reis, Rosário Macário (IST)

REVIEWER(S):

LIST OF CONTRIBUTORS (ALPHABETICAL ORDER):

German Freiburg (IST) MSc student

HISTORY: Version 1, 15th June, 2012

Version 2, 30th June, 2012

Version 3, 15th July, 2012

Version 4, 21st July 2012

GENERAL PROJECT INFORMATION

Acronym: ExPoD

Project Title: Exploring the complexity of policy design

Leading Institution: IST

Start Date: November 2010 Duration: 48 months End Date: October 2014

Total Persons-Months: 48



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ABOUT ALC-BRT CoE

Across Latitudes and Cultures - Bus Rapid Transit (ALC-BRT) is a Centre of Excellence for Bus

Rapid Transit development implemented in Santiago, Chile, and financed by the Volvo

Research and Educational Foundations (VREF).

This CoE was established in May of 2010 and is working as a consortium of five institutions

that include Pontificia Universidad Católica de Chile (PUC), Instituto Superior Técnico (IST)

Technical University of Lisbon, Institute of Transport and Logistics of Sydney (ITLS)

University of Sydney, Massachusetts Institute of Technology (MIT), and EMBARQ - The WRI

Center for Sustainable Transport, including its network of centers of sustainable transport.



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Table of Contents

Table of Contents........................................................................................................ 4

Table of Figure ............................................................................................................ 6

1 Introduction ............................................................................................................ 8

1.1 Research Overview ........................................................................................ 8

1.1.1 Motivation ................................................................................................ 8

1.1.2 Objectives ................................................................................................ 9

1.1.3 Approach ................................................................................................. 9

1.2 Design and Implementation of a Simulation Tool .......................................... 11

1.2.1 Objective ............................................................................................... 11

1.2.2 Approach ............................................................................................... 12

1.2.3 Interaction with other parts of the project ............................................... 13

1.2.4 Phases .................................................................................................. 13

1.2.5 Timeframe ............................................................................................. 13

2 Simulation Tool .................................................................................................... 14

2.1 Introduction ................................................................................................... 14

2.2 Agent-based models for land use and land cover ......................................... 18

2.3 Evaluating of Agent-based models................................................................ 22

3 Agents in BRT Systems ....................................................................................... 25

3.1 Sub-Classification of Agents ......................................................................... 26

3.2 Interests and Reactions of the Main Agents .................................................. 28

4 Agent Based Modelling Approach ........................................................................ 33

4.1 Introduction ................................................................................................... 33

4.1.1 Model Presentation ................................................................................ 33

4.1.2 Simulation Framework ........................................................................... 36

4.1.3 Model Implementation ........................................................................... 39



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4.2 Description of the Different Agents, Active Objects and Sub-models of the

System .................................................................................................................... 39

4.2.1 Member active object ............................................................................. 39

4.2.2 Zone active object.................................................................................. 41

4.2.3 Road and Transportation active objects ................................................. 42

4.2.4 Trip active object.................................................................................... 44

4.2.5 Enterprise active object.......................................................................... 45

4.2.6 Property active object ............................................................................ 47

4.2.7 Household Agent ................................................................................... 49

4.2.8 Office ..................................................................................................... 53

4.2.9 Education .............................................................................................. 56

4.2.10 Shopping ............................................................................................... 60

4.2.11 Transportation Mode Choice Model ....................................................... 63

4.2.12 Route Choice Model .............................................................................. 65

4.2.13 Real Estate Price Model ........................................................................ 71

4.2.14 Residential and business location choice model .................................... 74

4.2.15 Other sub-models and triggered events of the simulation model ............ 78

4.3 Data Requirements for Input Data and Validation of the Model ..................... 79

4.3.1 Household data ..................................................................................... 79

4.3.2 Activities and Employment data ............................................................. 87

4.3.3 Municipality/Government, Land Owner/Developer data ......................... 89

4.3.4 Public Transport Operators data ............................................................ 90

4.3.5 Geographical data ................................................................................. 90

4.3.6 Traffic data............................................................................................. 90

4.3.7 Real estate price data ............................................................................ 90

5 Conclusions and Further Developments .............................................................. 91

6 References .......................................................................................................... 91



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Table of Figure

Figure 1.2 – Architecture of the Simulation Model ....................................................... 12

Figure 2.1. Agent-based simulation architecture (Lin 2002) ........................................ 16

Figure 3.1 – Classification of UTS in Europe (source: MARETOPE) ........................... 25

Figure 3.2 – Interactions between stakeholders in UTS .............................................. 32

Figure 4.1. LMA Urban System Modelling Framework ................................................ 35

Figure 4.2. Scheme of the influence factors of location choice .................................... 36

Figure 4.3. Study area zoning scheme ........................................................................ 37

Figure 4.4. Household’s agent decision making flowchart .......................................... 52

Figure 4.5. Office’s agent decision making flowchart ................................................... 56

Figure 4.6. Education’s agent decision making flowchart ........................................... 59

Figure 4.7. Shopping’s agent decision making flowchart ............................................ 62

Figure 4.8. Flowchart of the transportation mode choice model .................................. 64

Figure 4.9. Flowchart of the route choice model .......................................................... 65

Figure 4.10. Traffic assignment results for the LMA morning peak .............................. 67

Figure 4.11. Public Transportation lines used in the route choice model ..................... 68

Figure 4.12. Demand-supply factor ............................................................................. 73

Figure 4.13. Conditioned decision tree example ......................................................... 78

Figure 4.14. The synthetic population reconstruction process (Birkin and Clarke 1988)

................................................................................................................................... 81

Figure 4.15. Used synthetic population reconstruction process ................................... 82

Figure 4.16. Correction coefficient convergence during the estimation process .......... 83

Figure 4.17. Age distribution of the synthetic population of the LMA ........................... 86

Figure 4.18. Number of members of household distribution of the synthetic population

of the LMA .................................................................................................................. 86

Figure 4.19. Spatial distribution of the quantity of activities in the LMA ....................... 89



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Table of Tables

Table 3.1 – List of stakeholder in UTS with BRT systems ........................................... 26

Table 3.2 – Classification of BRT system stakeholders ............................................... 27

Table 3.3 – Classification of BUS operators ................................................................ 28

Table 3.4 –Interests and Objectives of Agent in UTS and BRT Systems..................... 31

Table 4.1. Simulation scale of the agents of the model ............................................... 38

Table 4.2. Variables specification of the Member active object ................................... 40

Table 4.3. Variables specification of the Zone active object ........................................ 41

Table 4.4. Functions specification of the Zone active object ........................................ 42

Table 4.5. Variables specification of the Road active object ........................................ 43

Table 4.6. Functions specification of the Road active object ....................................... 43

Table 4.7. Variables specification of the Transportation active object ......................... 44

Table 4.8. Functions specification of the Transportation active object ......................... 44

Table 4.9. Variables specification of the Trip active object .......................................... 45

Table 4.10. Variables specification of the Enterprise active object .............................. 45

Table 4.11. Functions specification of the Enterprise active object .............................. 46

Table 4.12. Variables specification of the Enterprise active object .............................. 47

Table 4.13. Functions specification of the Enterprise active object .............................. 48

Table 4.14. Variables specification of the Household agent ........................................ 49

Table 4.15. Functions specification of the Household agent........................................ 51

Table 4.16. Variables specification of the Office agent ................................................ 54

Table 4.17. Functions specification of the Office agent ............................................... 54

Table 4.18. Variables specification of the Education agent ......................................... 57

Table 4.19. Functions specification of the Education agent ......................................... 58

Table 4.20. Variables specification of the Shopping agent .......................................... 60

Table 4.21. Functions specification of the Shopping agent.......................................... 61

Table 4.22. Trade-offs between variables of the mode choice logit model .................. 64

Table 4.23. Trade-offs between variables of the route choice logit model ................... 70

Table 4.24. Summary of the residential hedonic price model used for the LMA

(Martínez and Viegas 2009) ........................................................................................ 71

Table 4.25. Summary of the commercial and offices hedonic price model used for the

LMA (Martínez 2009) .................................................................................................. 72

Table 4.26. Trade-offs between variables of the route choice logit model ................... 77



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1 Introduction

1.1 Research Overview

1.1.1 Motivation

Integrated strategies are implemented through the combination of sectorial policies and

measures to achieve cumulative positive effects or to mitigate negative effects of any of

them. Consequently going further in one specific goal might well be done at the partial

sacrifice of another goal. Besides, integrated strategies can accrue synergies between

policies and/or measures and that is the main reason for policy packaging, where it is

important to select policies that will reinforce each other so that positive effects of

integration can be maximized or some of the negative impacts can be mitigated. In

addition, policy packaging can also occur to increase public acceptability, by adding

some compensation to social groups who would otherwise clearly be losing.

Some examples of this problem can be highlighted within the setting of BRT systems.

One of such cases is the relation between the developments of the settlement structure

and the areas where the BRT system is providing a good level of service. Another

example is the articulation with other modes and services; in particular the ones that

are expected to feed BRT systems, which has been one of the identified problems in

some BRT cases. Last but not least, the funding and financing mechanisms and the

fiscal effects (direct and indirect) that reflect in the prices at which the different modes

are offered and their influence in citizens perception and consequently in their choice

decisions.

This problem of articulation of policies is not new and has been identified by several

national and international institutions who have called for the need to adopt integrated

approaches to sustainability, combining land-use, environmental and wider social

instruments (e.g. ECMT 1995, World Bank 1996, etc.). Despite this fact the design and

implementation of integrated strategies is still a challenge since there are numerous

barriers to concerted decision processes. It is worth mentioning that these difficulties

are much related with the institutional design and legal framework which constitute an

outset condition of the decision process. The most common answer to overcome this

problem has been to develop new institutions that most frequently overlap in power and

compete with the ones already existing for the use of resources.

Evidences emerge all over the world showing that decision processes and institutional design are issues of high complexity, reason why within BRT-ALC



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they will be addressed in different projects. ExPoD (LS2) will focus on decision

processes and the policy packaging, while LS3 will address issues of institutional

design and performance, and LS1 will feed the previous two with information on

performance in the different institutional settings.

1.1.2 Objectives

As stated by Parsons (1995), policy-making takes place in conditions of uncertainty,

flux, unpredictability and variation, which means that the analysis of policy design and implementation require the understanding of a multi-agent complex system often with multiple levels of government. The policy options and the policy-making

process are instrumental for the success of any efficient policy packaging. Besides,

policy packaging often means “cherry picking” components governed by different public

sector areas, calling for negotiation and agreement whenever clear hierarchies are

absent. So, decision-making plays also a role in this problematic that cannot be

ignored.

ExPoD project aims to understand decision making processes in BRT systems and to develop a formal structure for retrospective analysis of the various interplaying policy components, embedded in a systems dynamic architecture to search for well-designed and promising policy packages.

ExPoD will focus on the BRT universe, which will be surveyed in order to select a set of

case studies where retrospective analysis of the various interplaying policy

components is possible so that we can deepen the understanding on barriers to policy

design and causes for policy underperformance. In this analysis we will clearly divide

between the merits of the “pure” policy devised at the strategic level, in an abstract

way, and its implementation at the tactical level – the measures, which are the

instruments for the policy materialization. This will lead us to the analysis of the several

cases with measures that have been devised in different places to materialize the

same policy strategy. The use of inductive case studies will allow us to grasp these

relations and hopefully find some qualitative and quantitative elements in support of our

model and analysis.

1.1.3 Approach

Despite the availability of considerable knowledge on measures, the design and

(mostly) the implementation of integrated strategies is still not fully dominated

nowadays, mainly because of the aggregated performance of instruments that is a



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scientific domain not (yet) sufficiently mastered. Some research projects have been

developed in this domain and we are taking them as a departure point in our research.

(e.g. SPECTRUM, MARETOPE, etc) .

Even when adequate policy (and instrument) packages (at tactical level) are designed,

their implementation (at operational level) is subject to several barriers. As reported by

several authors (ECMT, 1995, 2002, pp 27; May et al, 2003, pp 157, Macário et al.,

2003 in MARETOPE), poor policy integration and coordination, counterproductive

institutional roles, unsupportive regulatory frameworks, weaknesses in pricing and poor

data quality and quantity are the main barriers to pursue advocated policies.

To achieve the objectives of sustainable development (Banister, 2004, in CEMT, 2004,

pp 131) defined four basic groups of policy measures to be considered, being:

Life-style oriented policies, where policy intervention is only of subsidiary help since the basic element is a change of attitude towards mobility and material consumption. Information and education play a determined role since knowing the transport consequences (e.g. environmental damage, etc.) of a given policy or the effect of certain choices may well influence behavioural change. This is a rather bottom up approach;

Market oriented policies, which assume that people are willing to change their lifestyle or behaviour towards mobility if others do the same and no material disadvantage will result. In these cases measures like fiscal reforms or changed property rights might change the incentive structure. This approach although with some top down elements relies on the public acceptance of price as a mechanism to allocate services and goods;

Regulation oriented policies, which relies upon legal and regulatory changes, technical standards and norms (e.g. speed limits, maximum weight of vehicles, etc), on innovative planning methodologies (e.g. spatial planning and transport impact assessment) and on government reform. In general this approach is rather rationalist although it can be tempered with strong participatory processes to provide the argumentative element;

Public infrastructure/public transport, the provision of infrastructure and public transport services is often adopted as a policy approach and seen as associated with regulation oriented policies, to which is also often confounded. The dominant view seems to be that there is a wide diversity of policies that must be developed in an integrated way and understood as major instruments to achieve the strategic objectives of the urban mobility systems. That is, the objectives set for any urban mobility system should be deployed through the land-use, transport, environmental and fiscal areas, so that through this concerted action adequate signs will be transmitted to citizens and their behaviour towards mobility influenced. This is of course the more important when a new service and infrastructure is implemented, as in the case of BRT.

For each of these strategically defined policy areas we can find different measures,

corresponding to different ways of materializing a given policy, which will be highly

dependent on the institutional design (note interaction with LS3). The most critical

element of this analysis is the ability to effectively assess performances, so that we can

identify the best assembled policy packages and respective measures. At the outset



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we consider that assessment has to cover both policies defined at the strategic level,

and thus with a high level of abstraction, but also measures (policy materialization)

defined at the tactical level (planning) and effectively implemented at the operational

level, and by then with low (if any) level of abstraction.

Feed-back cycles are the privileged instrument to assess the definition of strategic objectives against impacts and operational objectives against results

(Macário, 2005) and they rely on performance indicators (note interaction with LS1). It

is through the feed-back cycles that the evaluation process is made effectively enabling

to decide whether there is the need for correction of policy and/or objectives and/or

measures and/or packages and where the improvement effort should be focused. This

need results from non-anticipated consequences from results and impacts that may

lead to questioning the original option taken when setting strategies and objectives.

In the approach adopted the qualitative and quantitative information obtained will be

the input to induce a system dynamics model to support the design of policy packages.

The ExPoD project builds in three main pillars:

Decision-making processes,

Policies and packages of measures,

Agents’ behaviour.

The expected outcomes of the policy design process (policy packages) may include a

wide range of possibilities: from operational to regulatory measures. Thus, the agents

involved may vary depending on the range of measures and policies considered, with

direct implication on the decision of which agents to include in the modelling and

simulation. One fundamental question is: What types of policies are needed for successful implementation of BRT systems? Which best instruments and measure can be used in the different social and political environments ?

1.2 Design and Implementation of a Simulation Tool

1.2.1 Objective

The main objective of this part of the research project (WP5) is to develop a behavioural simulation model of an Urban Transport System’s agents, including:

citizens and passengers, transport operators (including BRT operators) or authorities).



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The model could be used to support policy design process, since it can be used to

assess the likely reaction of the agents to changes in the UTS context (as a

consequence of policy implementation).

1.2.2 Approach

Figure 1.1 presents the approach utilised in the development of the model. The model

aims to simulate the changes of behaviour in the supply and demand of an urban

transport system as a consequence of the implementation of policy packages.

Figure 1.1 – Architecture of the Simulation Model

The model has three main building blocks, being:

Impact of policy packages on the UTS – the model will simulate:

o the impacts of the policy packages on the supply and demand;

o the impacts of the other factors on the supply and demand;

The design of the policy packages or the identification of other factors is external to the

model.

Supply – corresponds to the elements involved in the production of transport services.

Demand – corresponds to the elements that either consume transport services or

require certain levels of service.

Urban Transport System

Policy Packages

Other Factors

- economy

- demography

- regulation

- technology

Behaviour

Sim

ulat

ion

mod

el

Supply Demand

- Agents (e.g.: transport operators) - Transport Network

- Land Use (e.g.: location households)

- Passengers (e.g.: mobility patterns)

- Authorities

impact

change of

impact

impact



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The purpose of any policy is to induce certain changes in the behaviour of agents so

that they start behaving accordingly with some expectations. In the case of an UTS, a

policy package changes the behaviour of the demand or supply, resulting in a certain

level of disequilibrium. The System will naturally move towards a new equilibrium point.

The main purpose of the model is to predict the changes in behaviour of the elements

and the new equilibrium point. The applicability of policy package can then be

assessed by comparing the desired against the expected (or simulated) behaviour.

Agent based modelling (ABM) approach was chosen to simulate the behaviour of the

UTS. ABM is a micro-simulation model that allows modelling the complex transport

service supply-demand dynamics. Using micro-simulation, each agent is individually

modelled as well as their interactions.

At the moment of writing this report, the model is still under first steps of development.

The model is structured around three modules: impact of policies, supply and demand.

One module only is complete at stage: demand.

1.2.3 Interaction with other parts of the project

This part of the project interacts with all other within this project. In particular, the model

is being built with the information of the review on policy analysis, case studies, policy

packages, etc. For reasons of dimension the materials associated to those inputs for

the model will be object of other detailed reports and will be repeated here only to the

extent necessary for a good understanding of the contents of this report.

1.2.4 Phases

This WP is divided into the following stages of development:

1. Collection of information about UTS and BRT Systems

2. Collection of information about agent based modelling approach

3. Development of the model (3 modules)

4. Validation and Calibration of the model

5. Testing of the Policy Packages

At this moment Stage 1 and Stage 2 are completed. Stage 3 is on-going, with one

model (demand) already completed. Stage 4 and Stage 5 are due to start.

1.2.5 Timeframe

Stage 1 - Done

Stage 2 – Done



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Stage 3 – On going (to be completed by March 2013)

Stage 4 – From December 2012 to May 2013

Stage 5 – From March 2012 to July 2013

2 Simulation Tool

2.1 Introduction This section tries to identify the main theoretical and methodological aspects of Agent-

Based Simulation (ABS) and to review the recent work and key themes of discussion

and debate in this field.

The roots of agent-based modelling are located within the field of Distributed Artificial

Intelligence (DAI). This is a relatively new field, as they have been studied only since

the 1980s and have gained widespread recognition since about the mid-1990s (Ferber

1999; Wooldridge 2002).

The DAI approach is concerned with systems that consist of many interacting elements

that present the same level of autonomy, which can perceive their environment and

also act to change that environment according to some specific goals. The distributed

nature of the elements and thus the significance of local aspects of the system mean

that the components often carry out very different tasks and have heterogeneous goals

(Taylor 2003).

DAI is characterized by a ‘bottom-up’ approach to system design, in which lower level /

component rules of interaction and behaviour are specified firstly, and then higher level

or aggregate layers are built upon the lower ones. In the ‘bottom-up’ approach there is

no central control or blackboard system, rather control of the system is intended to

emerge from the specification of interaction processes amongst the components. The

designer of this type of system would exploit this property of complex systems to

establish control and coordination rather than to program it directly. As a result of the

influence of these interaction mechanisms, individual components will tend to behave

in a regulated way. This will result in the system exhibiting structured behaviour at an

aggregate level. (Taylor 2003).

The phenomena of emergence, where macro behaviours generated by micro rules of

interaction, places the approach, and the techniques to study it, within the research

domain of complexity research (Waldrop 1993). The systems studied typically present

very complex causal relationships and the underlying behaviours cannot be identified

merely by inspecting the macro behaviour of the system (Taylor 2003). In common with



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complex systems, DAI systems are designed with dense interaction heterogeneity

amongst the component parts, which sometimes generate patterned, structured, e.g.,

emergent behaviour that an observer can recognize. In this description, it should be

pointed out that there are strong parallels with the way that many social systems are

organized. For example, emergence of behavioural norms in human societies (such as

fashion trends), population regulation in the food web, group behaviour of animals

(flocking and herding).

Multi-agent systems (MAS) are systems composed of multiple interacting computer

elements, known as agents. Therefore, the concept of agent-based models is

intrinsically linked with the notion of emergence.

In the last decade, multi-agent systems have broader their boundaries outwards of the

computer science, reaching other fields of research such as the cognitive and social

sciences (psychology, ethnology, sociology, philosophy) and the natural sciences

(Ferber 1999; David, Marietto et al. 2004). Agent-based simulation (ABS) introduced

the possibility of modelling complex phenomena where structures emerge from

interactions between individuals, opening up new avenues for theoretical and

experimental research into self-organizing mechanisms present in the real world

(Barros 2004).

An agent-based model consists basically consists of a number of agents and an

environment. A simulation environment can be defined as “a medium separate from the

agents, on which the agents operate and with which they interact” (Epstein and Axtell

1996). Figure 2.1 shows a diagram in the agent-based model form, where the action

output generated by the agent affects its environment, which in turn affects the agent’s

actions, in a feedback mechanism. Agent-based models allow modellers to explore not

only agent-environment relationships, as shown in Figure 2.1, but three distinct layers

or interactions: agent-agent, agent-environment and environment-environment (Barros

and Alves Jr. 2003).



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Figure 2.1. Agent-based simulation architecture (Lin 2002)

A multi-agent simulation includes more than one kind of agent within this modelling

framework. Because multi-agent approaches are used in a wide range of fields, an

agent can be defined in a number of ways, according to the specificity of the problem in

hand. In fact, the definition of an agent is arbitrary and depends on what an agent

represents within a simulation as well as on the objective of the modeller (Anderies

2002; Wooldridge 2002).

In general terms, “an agent is a computer system that is situated in some environment,

and that is capable of autonomous action in this environment in order to meet its design

objectives” (Wooldridge 2002). As Wooldridge points out, while there is a general

consensus that autonomy is central to the definition of an agent, there is little

agreement beyond this. According to him, the difficulty in consensus is derived from the

fact that various attributes associated with an agent are of differing importance for

different domains. While in some domains the ability of agents to learn is essential, in

others it might be unimportant or even undesirable (Wooldridge 2002).

Other agents’ attributes include intelligence, mobility, communication, perception, and

vision (Barros 2004). Multi-agent simulation (MAS) allows the possibility of directly



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representing individuals, their behaviour and their interactions. Moreover, the idea of a

system composed of individual and heterogeneous agents is a natural metaphor for

many real-world systems, as in ecological systems where each being (animal, plant,

etc) is a kind of agent cohabiting the same environment, or in social systems, where an

agent is, obviously, a human being or, alternatively, may represent an organization or

some similar entity (Barros 2004).

The proximity of the notion of agent with an individual in a society or any kind of

organization makes the construction of models of individuals and their behaviours in a

computerized form a very intuitive process, facilitating the modelling process and

making it more accessible to researchers with limited computer programming

background (Barros 2004). The development of user-friendly programming packages,

such as RePast (University of Chicago 2003) and StarLogo (MIT Media Laboratory

2004) among others like (Swarm, MASON, Repast, NetLogo, OBEUS, AgentSheets

and AnyLogic) (Castle and Crooks 2006), has also reduced the complexity of this

process, stimulating the use of ABS as experimental tools in the social sciences.

Epstein and Axtell (1996) suggest that agent-based simulation is the basis of the

‘generative social sciences’, as a new approach for social sciences, which has the

simulation of ‘artificial societies’ as its principal scientific instrument. They argue that if

a given set of initial agents, environments, and rules is sufficient to generate social

macrostructures of interest, then the latter can be considered ‘explained’ by the former

system (Epstein and Axtell 1996). In fact, agent-based simulation offers new

possibilities for studying human society in many scopes, including the relationship

between space and society (Bonabeau 2002).

Agent-based models with the focus on the interaction between agents and environment

(usually the landscape to be studied) can be classified into two main research streams

according to their research focus. The first group focuses on the agent’s behaviour and

how agents react to and within a given spatial configuration (landscape). These are

mostly pedestrian movement models that investigate issues like crowd dynamics, car

traffic behaviour and planning models, shopping models, etc. (Batty, Jiang et al. 1998;

Schelhorn, O'Sullivan et al. 1999; O'Sullivan and Haklay 2000; Turner and Penn 2002;

Batty, Desyllas et al. 2003; Moulin, Chaker et al. 2004; Cicortas and Somosi 2005; Xu,

Mhamed et al. 2007). The second group focuses on the landscape’s behaviour, that is,

they simulate spatial change. The use of ABS for this kind of modelling came out of the

understanding that human decision-making plays a major role in the process of spatial



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change and hence must be an explicit part of the model framework. Most of this

research fits into the Agent-Based Models for Land Use and Land Cover (MAS/LUCC)

category.

2.2 Agent-based models for land use and land cover Multi-agent systems for land use and land cover (MAS/LUCC) can be defined as a

specific class of agent-based model that “[…] combines two key components into an

integrated model. The first component is a cellular model that represents the landscape

over which actors make decisions. The second component is an agent-based model

that describes the decision-making architecture of the key actors in the system under

study. These two components are integrated through specification of

interdependencies and feedbacks between the agents and their environment.” (Parker,

Manson et al. 2003).

The cellular module of a MAS/LUCC is commonly misinterpreted as a cellular

automaton model (CA) but, in fact, may draw on a number of spatial modelling

techniques (including cellular automata), such as spatial diffusion models and Markov

models (Parker, Berger et al. 2001). Moreover, it may not have any kind of autonomous

dynamic behaviour and thus may simulate a static landscape modified by agent

behaviour only (Barros 2004).

Furthermore, in LUCC models, the similarities between agent-based and CA models

lead to confusion about the categorization of some models. CA models incorporate a

number of relaxations from their original formulation. In some cases, each cell is

treated as an autonomous cell (or agent) with individually defined neighbourhoods and

transition rules and, therefore, this model fits into the agent-based definition, although it

can be considered a CA model as well (Barros 2004).

The category of agent-based models is very extensive, since its modelling framework

allows working with different layers of interactions, although their definition does not

require that all these layers are taken into account in the model. From this point of

view, perhaps, it is wise to differentiate CA models from MAS according to the kind of

interactions they incorporate: while ABS include all agent-agent, agent-environment

and environment-environment interactions, a CA model explores only environment-

environment interactions through fixed neighbourhood relationships transition rules)

(Barros 2004).



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The main advantage of MAS/LUCC is that they consider decision-making behaviour

explicitly, while CA models may at best use transition rules as proxies for decision

making. Another important advantage of ABM over CA models is that they offer a “high

degree of flexibility that allows researchers to account for heterogeneity and

interdependencies among agents and their environment. Further, when coupled with a

cellular model representing the landscape on which agents act, these models are well

suited for explicit representation of spatial processes, spatial interaction, and multi-

scale phenomena” (Parker, Berger et al. 2001).

Nevertheless, the choice for an MAS approach is not always a matter of its advantages

over a CA model, but of the specific requirements of the system under study. Box

(2002) points out that “there are a number of systems where population dynamics and

environmental interaction are so fundamentally interrelated that a modeller cannot

satisfactorily represent one without the other”. In these cases, a dynamic

representation of interactions among agents and between agents and the environment

is required, that is, it is essential to study the population effects on their environment,

as well as the effects of changes in the environment on the population’s actions (Box

2002).

The later feature emulates many systems in the real world, and that has led to an

extensive use of agent-based techniques in a number of different fields, including

archaeology, ecology, agricultural (land) economics, and urban studies (Parker, Berger

et al. 2001; Parker, Manson et al. 2003). In archaeology, ABS have been used to

simulate population patterns in historical settlements from landscape properties like

resources and climate records (Dean, Gummerman et al. 2000; Kohler, Kresl et al.

2000). In ecology, they have been particularly used for the study of ecosystems

management (Janssen 2002). In agricultural economics, they have been used to

examine the effects of new agricultural practices within a region, including the study of

spill over effects (Thomas 2001; Ballmann, Happe et al. 2002; Deffuant, Huet et al.

2002) as well as the adoption of new agricultural practices by farmers (Berger 2001;

Polhill, Gotts et al. 2001). There are also studies that have recreated Von Thünen’s

location theory using agent-based settings (Sasaki and Box 2003; Turton 2003).

There have also been several studies applying ABS to urban studies, where at least

two distinct research directions can be identified. The first focuses on the dynamics of

land use, which emerges from a bottom-up process where agents are understood as

individuals who locate according to their individual preferences. Otter and colleagues



20

(Otter, van der Veen et al. 2001) developed a generic agent-based model which

simulates the locational decisions of households and firms and explores the formation

of spatial patterns. Arentze and Timmermans (2003) propose a MAS model in which

developers and suppliers negotiate and generate proposals for developing a site, given

multiple candidate locations in an urban area.

Research within this focus also includes specific urban land-use and morphological

problems like urban sprawl (Loibl and Toetzer 2003; Torrens 2003; Brown, Page et al.

2004; Xie, Batty et al. 2007).

Benenson has also studied residential dynamics and spatial segregation in Israeli cities

(Benenson 1998; Benenson, Omer et al. 2002; Benenson 2004). Ducrot et al (2004)

have developed a model of urbanization in peri-urban areas in Brazil to investigate the

connection between urbanization, land-use change and hydrological processes, and

Manson (2005) developed a model to asses land change in the Southern Yucatan

Peninsular Region (SYPR) of Mexico through the SYPR Integrated Assessment

(SYPRIA).

The second research direction investigates the planning process itself, in an attempt to

understand the conflict of interests among different actors involved in the process. In

this case the emergent land use pattern is seen not as the result of a myriad of

individuals’ locational decisions, but as a mixture of bottom-up and top-down processes

where rules and conflicts define the final outcomes (Barros 2004).

Within this last framework, Ligtenberg et al developed a hybrid model of multi-actor

decisions in land use planning combined with land use allocation processes

(Ligtenberg, Bregt et al. 2001). Semboloni et al (2004) developed another interesting

model that simulates urban dynamics according to agents’ behaviours and an

economic system. Interestingly, their simulation model can be driven by virtual agents

as well as by human users, and as a result it works as a simulation tool as well as an

interactive decision support system.

Recently, some hybrid projects involving different aspects of urban change have been

developed. An example is ILUTE (Integrated Land Use, Transportation, Environment),

an ambitious project that is being developed by a team of Canadian researchers

(Miller, Douglas Hunt et al. 2004). ILUTE is a hybrid agent-based system that includes

all spatial processes affecting land use, locational choice, and transportation within the

urban system.



21

The integration of Geographic Information System (GIS) within the ABS models has

also been a target of research. So far, the most common form of integration is to use

GIS to create a model’s landscape from “real world” data by importing data into an ABS

system through a GIS (Box 2002; Gimblett 2002; Castle and Crooks 2006; Crooks

2006).

While MAS/LUCC models appear to be useful tools, the discussion about the type of

knowledge that can be obtained from them emerged. To types of goals have come up

in the development of these models: exploratory and predictive (or descriptive) goals

(Barros 2004).

Exploratory research conceives simulation as a laboratory where theories can be

explored and developed. Modellers start from a theoretical framework and formalize it

in computer code in order to examine the ramifications of their framework and

potentially generate new hypotheses to explore empirically. These models usually

focus on particular processes or dynamics in order to achieve some fundamental

understanding of specific aspects of a phenomenon (Parker, Manson et al. 2003).

Exploratory modelling can be seen as part of a theory-building process. It generally

includes testing the theory (a conjuncture) by demonstrating that a set of rules can lead

to the outcome of interest; it also allows the modeller to explore other possible causes

that lead to the same outcome, formally exploring the robustness of the proposed

causal explanations. In addition, this process might lead to the finding of outcomes not

originally anticipated (Parker, Manson et al. 2003).

The main limitation of this approach is the lack of an established method for evaluating

the real-world validity of the simulations. Since they are usually built upon abstract

concepts and the outcomes are general patterns, it is difficult to determine what the

models tell us about reality. While exploratory models can be excellent tools for

provoking insights into general phenomena, they may provide less understanding of

specific real-world systems (Parker, Manson et al. 2003).

Descriptive approaches, on the other hand, are more concerned with empirical validity

and/or predictive capacity. These approaches attempt to reproduce specific real-world

systems to facilitate direct empirical and policy scenario research (Parker, Manson et

al. 2003).

Parker at al (2003) suggest that MAS modelling methods can be more effective than

more conventional urban models due to their ability of modelling at a finer resolution



22

and therefore make the best use of available data. Simultaneously they explore

heterogeneity and interdependencies, reflecting important endogenous feedbacks

between processes. Last, they are not constructed to meet a set of equilibrium criteria

and can thus represent discontinuous and nonlinear phenomena (Parker, Manson et al.

2003). Despite all the potential shown by MAS, the question as to whether a predictive

role is an appropriate goal for ABM/LUCC remains open (Parker, Berger et al. 2001).

2.3 Evaluating of Agent-based models

Model evaluation is an important part of any model’s development processes and

includes both comparisons of the model outputs with the modelled real-world system

as well as understanding the sensitivity of the model to its internal parameters (Turner,

Gardner et al. 2001).

These two evaluation steps are more commonly referred to as verification and

validation and concern, respectively, the correctness of model construction and

truthfulness of a model with respect to its problem domain. In other words, verification

means building the system correctly (do the thing right), and validation means building

the correct or most appropriate system (do the right thing) (Sargent 2001; Parker,

Manson et al. 2003).

To perform verification, it is essential to conduct a sensitivity analysis of relationships

between the parameters of the model and the outputs. Validation, on the other hand,

concerns how well the model outcomes fit the observed behaviour of the real system

(Castle and Crooks 2006).

There are a number of approaches for the validation of simulation models (Sargent

2001), including matching model output. In the case of LUCC models’ this means

matching its spatial outcomes to measured variables in the real-world system, and

matching a model’s components’ structures and processes to structures and processes

in the real-world system. In either case, validation depends largely on the model’s

objectives, and a critical issue is the decision as to how much detail the model is being

designed to match.

Validation usually involves performing a set of analyses that will demonstrate the

relevance and accuracy of the model’s results to understand or predict the real world,

depending on the model’s purpose.



23

For model validation purposes, it is important to clearly identify the objectives of a

model. Where accurate predictions are the main goal, measures of the accuracy of

spatial outcomes are necessary. Where the goal is to represent a process and explain

general patterns that are observed across a variety of situations, validation might

require evaluating how well a model reproduces critical system properties in terms of

spatial and temporal dynamics (Rand, Brown et al. 2003; Brown, Walker et al. 2004).

Brown et al. (2004) stress that this process “involves judgments about how well a

particular model meets the modeller’s goals, which in turn depends on choices about

what aspects of the real system to model and what aspects to ignore”.

Validation is traditionally achieved by comparing the model outputs either with real-

world data or observations or other model’s outputs (Parker, Manson et al. 2003). This

comparison is usually carried out using statistical methods, by establishing a

reasonable correlation between a model’s outputs and real data.

Validation it is a critical issue for any modelling approach applied to any system, but it

can be especially difficult when using ABM to model complex systems. A number of

authors stress these difficulties, which are summarized below:

The nature of complex system, and in particular path dependences and feedbacks, makes them very difficult to predict (Parker, Manson et al. 2003; Brown, Page et al. 2004).

These complex systems normally require large quantity of detailed data of behaviour of agents which is difficult to obtain, and validation is made through aggregate spatial structure outcomes (Parker, Berger et al. 2001).

The validation of agents’ interactions with real-world extensive dataset at individual level scarcely available.

It also appears that for explanatory models that intend to look at possible theoretical

frameworks to explain a general phenomenon, a simple statistical comparison between

outcomes and static data from a specific system might not lead to an adequate

validation (Barros 2004).

Validation issues remain as one of the main pitfalls of ABS modelling approach and

there remains a need for new measures of fit between the model and data that go

beyond spatial matching to focus on variability of outcomes and dynamics (Parker,

Berger et al. 2001; Parker, Manson et al. 2003; Brown, Walker et al. 2004).

There are just a few MAS/LUCC models that demonstrate a consistent validation, with

proper statistical proof of consistency between real data and model outputs.

Nevertheless, in face of the issues discussed above, it seems that the concept of

validation for MAS must be adapted to cope with dynamics and uncertainty issues.



24

In recent research, the most common form of validation found in MAS/LUCC models is

through pattern analysis. Pattern analysis consists in the use of spatial configuration

and composition metrics, mostly originating from landscape ecology (Barros 2004).

Although the vast majority of recent publications on MAS/LUCC models do not make

any reference to validation and only a few mentions some kind of statistical analysis

with supposed satisfactory results, some significant research is being developed in this

direction. Brown et al. (Rand, Brown et al. 2003; Brown, Page et al. 2004; Brown,

Walker et al. 2004) offer some interesting discussions of validation of ABS and propose

alternative methods for validation of MAS/LUCC.

Rand et al (2003) propose validation of global patterns which are evident in empirical

analysis of urban development processes: power law relationships between frequency

and cluster size and a negative exponential relationship between density and distance

from the centre.

Following the same assumption of the impossibility of validating ABS models in the

traditional manner, Brown et al (2004) propose validation through comparison of two

different models (implementations) for residents’ settlement choices in the presence of

a green belt that generate the same fundamental results: a mathematical model and an

agent-based model. They argue that agent-based models serve as minimally realistic

models of real-world complex systems, but the fact that ABS models cannot prove

theorems, gives them a shaky foundation. Brown et al. (2004) suggest that the

scientific enterprise can be enriched if theorems of simplification can be proved through

a mathematical model and the conclusions of those theorems explored in a more

general context using ABS.

Brown et al. (2005) discuss the impact of path dependence and stochastic uncertainty

on the viability of validating MAS/LUCC. The authors argue that there are two

contradictory impulses in the process of development of ABS models: the desire for

accuracy of prediction and the recognition of unpredictability in the process due to path

dependence and stochastic uncertainty in the models. They suggest that the

predictability of such models is questionable and, therefore, the important issues

concerning ABS models are whether the mechanisms and parameters of the model are

correct, rather then the model outcomes themselves. They then propose the invariant-

variant method to assess the accuracy and variability of the multiple outcomes

generated by MAS/LUCC models. This is built upon techniques for measuring spatial

similarities.



25

It is possible to conclude then that validation methods of ABS models are not yet

mature, and that it is unlikely that it could soon be completely developed to the point

that these kind of models will produce results with reliable predictive power (Brown,

Page et al. 2005).

Validation of ABS models seems to be following a different path, which brings more

understanding about the model, its mechanism, parameters, processes and

behaviours. It seems that sensitivity analysis plays a very important role in this process,

as well as a precise analysis of the outcomes, using pattern metrics and other similar

techniques (Barros 2004).

3 Agents in BRT Systems

MARETOPE project classified the UTS stakeholders as follows:

Public transport operators and associations Public authorities (transport / political) Citizens / customers Employees and trade unions Producers of transport means and services

The definition of agents of a public transport system depends on the organization form

adopted in each case. The figure below presents a classification for UTS in Europe

from MARETOPE Project.

Figure 3.1 – Classification of UTS in Europe (source: MARETOPE)



26

A list of agents for discussion, to be further completed and refined is presented below, highlighting the main agents and a preliminary identification of the kind of entities that each one can assume:

Table 3.1 – List of stakeholder in UTS with BRT systems

Sector Agent EntityBRT Authority/Administrator Public Agency/GovernmentBus Operators Private/Public CompanyFare Collection Manager Private/Public CompanyInfrastructure Manager Public Agency/GovernmentTrust Fund Manager Private/Public CompanyEmployees Individuals

Political Authority/Head of Government

Individual/Government

Other modes' authorities Public Agency/GovernmentTraffic Engineering authority Public Agency/Police/GovernmentOther related public authorities (environment, urban development, public works, etc)

Government

Competing public transport operators/routes

Public Companies

Competing private transport operators/routes

Private Companies

Shopkeepers and businesses around corridors

Individuals/Private Companies/Associations

Users IndividualsCar Users IndividualsCitizens (public opinion/voters) Individuals/AssociationsPublic Transport Associations AssociationsUnions (drivers, etc.) OrganizationsResidents neighbouring corridors Individuals/AssociationsTransport Experts & Mobility related NGOs and associations

Individuals/Associations/Private Companies/Universities

News Media Private/Public Company

Traditional agents in BRT systems

BRT System

Private Sector

Civil Society

Public Authorities

3.1 Sub-Classification of Agents

Many of the agents listed above may assume different institutional configuration,

depending on the organizational form of the system.



27

The BRT Planning Guide of ITDP describes different existing arrangements for the

agent representing the transport authority in charge of BRT systems:

Table 3.2 – Classification of BRT system stakeholders

Type of Institution DescriptionTransport Department Large entity with a wide range of regulatory and

management responsabilities; typically reports directly to city to political officials

Transport Authority Organization with wide oversight on all transport activities; frequently given autonomous status through a board of directors

Public Company A specially created company that is owned and managed by the local government

Specialised Transport Agency Smaller organization with a focused mandate; typically reports directly to city to political officials

Non-governmental Organization Independent outside organization that is given the responsibility of managing the public transport system

BRT Authority/Administrator

Source: ITDP BRT Planning Guide

The MARETOPE project also identifies several roles that can be played by authorities:

Licensing authority: granting access to the profession (in all regimes),

Authorising authority: granting access to the market (in market initiative regimes),

Concessioning authority: granting access to the market (in authority initiative regimes),

Regulatory authority: setting the ‘rules of the game’ for operators present on the market, together with the actual watchdog or referee monitoring and enforcing the rules of the game in all regimes,

Enterprising authority: when the authority creates and bears the entrepreneurial risks on transport services she creates either by owning a public transport company (or non-corporatised internal division producing transport services) or by outsourcing the production of services she has designed. This either under authority initiative (legal public monopoly) or under market initiative (the services created by the authority have to be granted an authorisation by the authorising authority), and

Subsidising authority: for two purposes: stimulate the general supply of services and redistributing wealth to politically chosen target groups in society (such as handicapped, elderly, unemployed,…).



28

The agents representing the bus operators can also assume a range of different

arrangements, which depend on features of the system. The table below presents

three of those features for which different alternatives typically exist in BRT systems.

Table 3.3 – Classification of BUS operators

Characteristic AlternativesRoute scheme Trunk & feeders

Trunk onlySemi-open system (routes partly in mixed traffic also)Open system (routes fully in mixed traffic also)

Ownership of operators Private companiesPublic companiesPrivate and Public companies

Number of operators MonopolyMultiple in coordinationMultiple non-coordinated

Bus Operators

3.2 Interests and Reactions of the Main Agents

The MARETOPE report presents some conclusions that should be considered in the

definition decision making of agents in the modelling process: “The role of each actor is

thus dependent and influenced by the legal/ regulatory framework in place. The

research also indicates that past experiences, qualifications and aims influence the

perception and the ways that each actor react during the process”. The range of

decisions/reactions may change depending on the environment.

There a set of decision relative to the planning and control of the service supplied by

the public transport system that can be divided in three levels, following a classification

defined in MARETOPE:



29

Source: MARETOPE

This framework is important to identify decisions, but it should be noted that it does not

apply to the strategies of each agent, as noted in the report:

…the strategic, tactical and operational levels considered here are seen from

the point of view of the appearance of transport services to the passenger, i.e.

at the system level, and not from the point of view of a specific (private)

transport operator involved in production somewhere in the chain of actors, i.e.

at the actor level. Indeed, any such actor will have its own strategy, tactics and

operations and these should not be confused with what is presented above.

Regarding the strategic decisions of each agent as a function of their respective

interests, one important issue to be discussed is the enforcement of the compliance of

service schedule by the bus operator, for example, which tends to perform less mileage

but receive payment for all the planned service.

A preliminary identification of the interest of each agent and their possible decisions

and reactions is presented in the following table (Table 3.4).

Figure 3.2 presents the key functions of a BRT system. Green functions have direct

contact with passengers, while blue functions do not have.



30



31

Table 3.4 –Interests and Objectives of Agent in UTS and BRT Systems

Agent Interests Reactions/DecisionsBRT Authority/Administrator Ensuring the provision of transport

serviceDefine fareDefine supply level, frequencies or timetableSupervise and enforce service provision by bus operatorSupervise and enforce fare collection systemSupervise and enforce infrastructure management

Bus Operators Receiving payment in exchange for providing transport service

Operate vehicles (perform bus trips)Perform maintenance of vehicles

Fare Collection Manager Receiving payment in exchange for managing fare collection system

Operate fare collection system (collect revenue)Perform maintenance of fare collection system

Political Authority/Head of Government

Achieving public acceptance of the transport service provided

Define policy objectives of BRT systemDecide about the budget pf BRT system

Shopkeepers Avoiding negative impact of BRT on commercial activity

Promote public protestsSpread word about negative impacts of BRT system

Users Availability of transport service: Fast, Affordable and Reliable

Decide on using or not using the service of the BRT systemSpread word about perceived quality of service of BRT system

Car Users Preserving at least previous level of service in road network

Promote public protestsInterfere with bus operation (non-compliance of traffic rules along corridors and intersections)

Citizens Availabilty of alternatives of transport services

Spread word about perceived quality of service



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Figure 3.2 – Interactions between stakeholders in UTS



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4 Agent Based Modelling Approach

4.1 Introduction

4.1.1 Model Presentation

In this section we present the framework of the agent-based model. The Lisbon

Metropolitan Area (LMA) has been used as case study. Nonetheless, the model is

flexible enough to allow any other urban region to be modelled.

The developed model considers six main agents that interact in the LMA environment:

Households, which represent the household residing in the LMA. Municipality/Government that represent the regulatory agent of land use and

decision maker about transportation infrastructure and services. Land Owner/Developer, which are the agents that may change the land use

patterns of the study area constrained by the land use municipal regulation. Offices, Education and Shopping, which represent a simplified set of activities

of the residents (work, study and leisure).

There are other important components in this model defined as active objects. These

components are relevant for the modelling process but they do not have decision

making abilities like the agents. These active objects are:

Member is an object that represents a member of a household with all its main attributes like age, education, job, etc. Each household is formed by a set of members and they are the ones that work or study and make trips.

Zone object results from a spatial discretisation of the study area. All the agents present a base zone.

Transportation Network formed by Road and Transportation, represents the transportation infrastructures and services that are available. These objects receive the travel demand from household’s members.

Trip object contains the attributes of all the trips performed by the household’s members.

Enterprise embodies the business sector of LMA containing all the jobs information about the existing activities.

Property formed by a set of properties assigned to each zone that contain the attributes of the properties that bought or rented by the households and the enterprises.

There are also sub-models that are used for the simulation process:



34

A macroeconomic model, which estimates during the simulation process the variation on the economic environment of the study area during the simulation process, affecting the financial balance and stock of business, and the population migration of the study area.

A demographic migration model that estimates the migration stocks in the study area (emigration vs. immigration) during the simulation process.

A transport mode choice model, with the function of representing the mode choice for each trip of the household’s members.

A route choice model that assign trips to the transportation network. A real estate price model that defines the price of real estate during the

simulation process. A residential and business location choice model.

All these components and sub-models will be explained in detail in Section 4.

After this brief presentation of the main features of the model, we present the basic

framework of the developed agent-based model and their main interaction.

Figure 4.1 presents the model framework and some relationships between agents and

active objects. The global workflow of the model can be summarized as: each

household is formed by a set of members which perform different activities and make

trips using the transportation network which dynamically changes the environment

conditions.

The interactions between agents are simulated considering that location choices of

households and businesses are influenced by land prices, travel costs (including time

costs as opportunity costs) and “neighbourhood quality”, the later being measured by

the average rent from households and a land use mixture index – entropy index

(Cervero and Kockelman 1997; Potoglou and Kanaroglou 2008)i.



35

Figure 4.1. LMA Urban System Modelling Framework

The factors influencing location choices and their relationships are presented in Figure

4.2. It is easy to notice the existence of a significant number of feedback loops between

the different factors and housing and business demand, which stresses the complexity

of the location choices considered in the model. The red arrows in the figure represent

the direct factors that are influencing the housing and business demand, although there

are also indirect relations that results from the feedback loops.



36

Figure 4.2. Scheme of the influence factors of location choice

The next points of this section explain in more detail the modelling hypothesis

considered and the simulation process.

4.1.2 Simulation Framework

The first step into setting of the simulation framework was the establishment of three

main variables that are directly related with the simulation size and computer

processing time:

The time unit used for the simulation process. The time unit selected was days.



37

The spatial resolution of the model. The study area was discredited into 66 zones, 40 of them inside Lisbon municipality, the main modelling area (see Figure 4.3).

The simulation scale parameter, which set the relation between the number of agents in the model and the number of agents in the real world. This parameter takes different values for the each type of agents in the model, as presented in Table 4.1.

Figure 4.3. Study area zoning scheme

The differences in the simulation scale of the different agents result from the

experimental measurement of the simulation running time. The relation between the



38

households and the businesses scale was established also experimentally derived

from incomplete data available from the businesses side that would lead to an

underestimation of job availability to the household’s members. This issue will be

analyzed in detail in Section 4.

Table 4.1. Simulation scale of the agents of the model

Agent Simulation Scale

Household 1/250 inside each zone

Municipality/Government 1 per zone

Land Owner/Developer 1 per zone

Offices 1/100 inside each zone

Education 1/1 inside each zone

Shopping 1/100 inside each zone

The simulation also contains some scheduled events for decision making for the

households:

Monthly events to decide the shopping visits for the next month Yearly events to make decision on other family issues as getting married,

having children, leaving home, etc.

These scheduled events are a complement to the simulation events triggered for each

member flowchart described in detail in Section 4.

The model statistics during the simulation are recorded monthly in order to capture the

behavioural changes of the agents.

All the trips performed by the household’s members are made between zones (discrete

space). The transportation mode selected to make each trip is calculated using a

transportation mode choice model that will be presented in Section 4. After the mode

choice calculation each trip is assigned to the network using a probabilistic route choice

model that is also discussed in Section 4.



39

4.1.3 Model Implementation

The model presented here was written in the JAVA Programming Language, using

Anylogic. This is a software framework for creating agent-based simulations, system

dynamics modelling and discrete event simulations using the JAVA language

developed by a research Groups in Saint Petersburg, Russia.

Anylogic provides a library of JAVA classes for creating, running, displaying and

collecting data from an agent-based simulation. In addition, Anylogic allows the user to

customize simulation outputs.

JAVA simulation programs that use Anylogic libraries typically have at least two

classes: object class and agent class. The agent class describes the behaviours and

characteristics (states, capabilities) of agents and it is largely simulation-specific. The

agent class sets up and controls both the representational and infrastructure parts of

an Anylogic simulation.

4.2 Description of the Different Agents, Active Objects and Sub-models of the System

This section presents a complete description of all the agents, active objects and sub-

models integrated in the LMA agent-based model. In order to explain more

comprehensively all the agents and sub-models, an initial presentation is made of the

active objects of the model that are used during the simulation process by the agents

and that are responsible for setting the environment.

After that, each agent is described with all the relevant variables and function used for

their decision taking and simulation output and the flowchart on how each agent takes

decisions along the simulation. Two of the agents presented in the model framework in

Section 3 (Municipality/Government, Land Owner/Developer) were not yet fully

developed and are not be presented in this section.

The simulation model also includes some models that are relevant for the decision

taking of the agent like the transport choice mode model, trip assignment, etc. This

sections also describes how these models were developed and how the interact with

the main model during the simulation.

4.2.1 Member active object

The member active object was created as a Java class and contains a set of attributes

that are used by the household agent, as later presented in detail.



40

The variables or attributes of this active object are described in Table 4.2. These

attributes encompass the data that is used during the simulation to model the

interaction between the household agent and the environment (e.g. members trips are

assigned to the transportation network) and the interaction between different agents

(members work, study and visit these different agents).

The creation of this active object and the definition of its attributes during the simulation

will be explained in detail in the household’s agent description.

Table 4.2. Variables specification of the Member active object

Variable Description

household Id of the household of the member

age Integer variable of the age of the member

sex Boolean variable of the sex of the member

marital_status Integer variable of the marital status of the member (1..5)

head_household Boolean variable – 1 if the member is head of the household

Education school Id of the Education facility where the member is studying

education_level Integer variable of the educational level of the member (1..5)

fees Value of the fees paid by the member (euros)

posgrad_time Time passed since the start of Pos-graduation studies (years)

shopping Array of the Ids of the shopping facilities visited by the member

shopping_spent Name of the zone were the household resides

enterprise Id of the enterprise where the member is working

salary Salary of the member (euros)

trip Array of trips made by the member

trip_rate Array with the monthly rate of each trip made by the member

uses_car Boolean variable – 1 if the member uses car for its trips

last_km Quantity of km travelled by car of the member



41

4.2.2 Zone active object

The zone active object was developed as one of the main features of the modelling

process allowing the reduction of complexity of the problem introducing a spatial

discretisation of the study area (presented above).

This active object plays an important role during the simulation process as the main

data gather and provider source for the agents’ decision making. The main variables

stored during the simulation and functions of the active object are presented in Table

4.3 and Table 4.4.

As presented in these tables, the zone active object gathers information about all the

existing agents in the study area during the simulation with some aggregate summary

variables important to assess the emergent system behaviour.

Another main task of this active object is to determine the availability of land space and

the real estate price or rent that should be paid every month by every agent. This last

feature will be explained in detail in the real estate price model presented in this

section.

The zone active object is present in the model simulation panel and their variables can

be assessed dynamically during the model run.

Table 4.3. Variables specification of the Zone active object


enterprise Array of all the enterprises in the zone

household Array of all the households in the zone

offices Array of all the offices in the zone

school Array of all the education facilities in the zone

shops Array of all the shops in the zone

properties Array of all the properties in the zone

road Array of all the roads in the zone

shapez Area geometry of the zone in the simulation panel

selected Boolean variables – 1 if selected in the simulation panel

roads_increased_capacity Integer variable of the increased road capacity in the zone



42


waiting Quantity of households in the Waiting state

extreme Quantity of households in the Extreme state

total Quantity of households

bReduce Quantity of enterprises in the Reduce state

bworking Quantity of enterprises in the Working state

btotal Quantity of enterprises

Table 4.4. Functions specification of the Zone active object

Function Description

getBusinessRentPrice Calculates the business rent price for the given attributes

getHabitatRentPrice Calculates the housing rent price for the given attributes

hasBusinessPlaces Identifies if there is any free business place in the zone

hasHabitatPlaces Identifies if there is any free housing place in the zone

joinz Adds an agent to the arrays of agents of the zone

leavez Deletes an agent to the arrays of agents of the zone

4.2.3 Road and Transportation active objects

The road and transportation active objects assemble all the information of the

transportation infrastructure and services of the study area. Each active object

represents a section of transportation infrastructure and services that is available for

the households’ members to use in their daily trips.

The traffic load assigned to each section of the road and transportation active objects

are determined using the transportation model choice model and the route choice

model the will be also presented in this model.

The main variables and functions of both active objects are presented from Table 4.5 to

Table 4.8. The main variables that characterize both active objects are their length,

speed and capacity, and the two later are used dynamically to determine the time spent



43

to travel in each section of the transportation network and their cost (not depending of

the load-capacity ratio for the transportation active objects).

Both active objects are included in the simulation panel and their load factor can be

assessed dynamically during the model run.

Table 4.5. Variables specification of the Road active object


abbr Name or code of the road

colorR Color of the road in the simulation panel


shapeR Linear geometry of the road in the simulation panel

increased_capacity Value of the increased capacity to the initial capacity

length Length of the road (km)

load Monthly average daily traffic (cars/day) of the road

load_base Initial daily road capacity

load_limit Actual daily road capacity

speed_limit Maximum/free flow speed of the road (km/h)

speed Actual speed of the road (km/h)

trip_time Actual time needed to pass through the road (hours)

Table 4.6. Functions specification of the Road active object


joinR Adds a car to the load of the road

leaveR Deletes a car to the load of the road

selectR Selects the road in the simulation panel

updateParameters Updates the simulation panel parameters (color, shape)



44

Table 4.7. Variables specification of the Transportation active object


shapeT Linear geometry of the PT arc in the simulation panel

colorT Colour of the PT arc in the simulation panel


length Length of the PT arc (km)

go_time Time taken travelling on this PT arc (hours)

wait_time Time taken waiting on this PT arc (hours)

cost Cost of the PT arc (euros)

load Monthly average daily passengers (pass/day) of the PT

arc

load_limit Daily PT arc capacity

Table 4.8. Functions specification of the Transportation active object


joinT Adds a car to the load of the PT arc

leaveT Deletes a car to the load of the PT arc

selectT Selects the PT arc in the simulation panel

updateParameters Updates the simulation panel parameters (colour, shape)

4.2.4 Trip active object

The trip active object was created as a Java class and contains a set of attributes that

are used by the households’ members to decide which trip to perform during the

simulation run.

This active object works as a data provider for the transportation mode choice and to

the route choice model gathering the data of all the possible combinations of trips that

can be performed between two zones on the simulation model.



45

The main variables of this active object are described in Table 4.9. The main variables

of this active object are the source and target zone of the trip, the trip mode, the trip

motivation (work, study or leisure) and the trip length.

Table 4.9. Variables specification of the Trip active object


zone_src Source zone of the trip

zone_tgt Destination zone of the trip

private_transportation Boolean variable – 1 if uses car

motivation Id of the enterprise that generated the trip

lanes Array of the roads/PT arcs that are used in this trip

length Total length of the trip (km)

4.2.5 Enterprise active object

The enterprise active object is used as an intermediary class between the households’

agent and the other agents that are job providers for the households’ members. Its

main task is to gather and provide information about the financial status and company

staff and perform some tasks related with job placement and job dismissal, and with

the setting of salaries of the employees.

The variables of this active object are described in Table 4.10. The main variables of

this active object are the sector of activity of each enterprise, the financial information

(business, income and balance) and the staff variable where all the information from

the employees is collected.

Table 4.10. Variables specification of the Enterprise active object


zone Name of the zone were the enterprise is located

selected Boolean variables indicating if it is selected in the simulation panel

sector Type of enterprise (Education, Offices or Shopping)

sector_int Integer variable with a enterprise type code



46


places Number of employees of the enterprise

business Monthly revenue of the enterprise (euros)

income Monthly amount of money spent in salaries (euros)

balance Accumulated profits (business-income-rent) (euros)

staff Array variable containing the set of objects of the employees

time Simulation time

The functions performed by the enterprise active object are presented in Table 4.11.

The main functions are related with the staff management (join, leave, bate, fire, etc.),

the salary control (ltbl_base_salaries, growSalaries), the enterprise relocation

(moveForPrice) and an aggregate control of the enterprise performance (isReduce,

isWorking).

All these functions can be invoked by all the agents integrated in the enterprise sector

(Education, Offices and Shopping).

Table 4.11. Functions specification of the Enterprise active object


canJoin Identifies if an employee can be hired

join Hires an employee to the enterprise

leave Erases an employee from the staff array of the enterprise

bate Identifies is the staff size of the enterprise should be reduced

fire Fires an employee of the enterprise

enableHire Allows an increase of the staff size of the enterprise

growSalaries Increases the salaries of the employees of the enterprise

moveForPrice Relocates the enterprise in a new target zone

setsector Set the type of enterprise when it is created

ltbl_base_salaries Gets the salary amount of the employees



47

selectE Selects the enterprise in the simulation panel

isReduce Identifies if the enterprise is in an Reduce state

isWorking Identifies if the enterprise is in a Working state

4.2.6 Property active object

The property active object is the backbone of the real estate price model, and links the

demand from the households for dwelling and enterprises for commercial, offices and

educational space, with the available supply that exists in each zone of the model. The

main task of this object is to gather and provide information about the property

attributes in order to determine the life cost of households and rent costs of enterprises

to simulate the real estate price fluctuations and location dynamic process.

The variables of this active object are described in Table 4.12. The main variables of

this active object are the type of property (dwelling, office, education building or shop),

the structural attributes of the property (size, house, number of bedrooms, etc), the

neighbourhood attributes of the property (SPLAG, EntropyIndex, etc.) and the dynamic

variables dependent from the real estate market (MarketPrice and MarketRent).

Table 4.12. Variables specification of the Enterprise active object


zone Name of the zone were the property is located

Enterprise Enterprise Id if property owned or rented by an enterprise

Household Enterprise Id if property owned or rented by a household

IsDwelling Boolean variables indicating if it is a dwelling

Price Monthly payment by the owner if it is owned (euros)

Rent Monthly payment by the renter if it is rented (euros)

House 1 if the property is a house

Size Area in square meters of the property (sqm)

Nbedrooms Number of bedrooms of the property if it is a dwelling

Nfloor Number of the floor of the property



48


Age Age of the property

SPLAG Variable reflecting the average value of the properties until 500

meters around

EntropyIndex Entropy Index within a walking distance of 500 meters

k

1i

ii500 )kln(

)pln(pEI (Cervero and Kockelman 1997; Potoglou

and Kanaroglou 2008)

EducationalIndex Number of undergraduate persons/Population over 20 years old

(500 meters radius)

MetroAcess1 1/(1+exp(6.812 -0.659 *(17 – walking time subway station)))

MetroAcess2 1/(1+exp(4.394 -0.439 *(20 –walking time subway station)))

RailAccess 1/(1+exp(4.394 -0.439 *(20 –walking time rail station)))

Road1Access 1/(1+exp(8.789 -0.007 *(2000 –access distance)))



MarketPrice Updated market value of the property (euros)

MarketRent Updated market rent value of the property (euros)

The functions performed by the property active object are presented in Table 4.13. The

main functions are related with the property assignment to a household or an

enterprise (canJoin, join, leave), and changes or updates of the property variables

(change, update).

All these functions can be invoked by all the agents integrated in the enterprise sector

(Education, Offices and Shopping) or households.

Table 4.13. Functions specification of the Enterprise active object


canJoin Identifies if a property can be rented or bought can be hired



49

join Links an enterprise or household to the property

leave Unlinks an enterprise or household from the property

change Change some attribute of the property (e.g. number of bedrooms)

update Update some of the property attributes (e.g. MarketPrice)

4.2.7 Household Agent

This point presents all the components of the household agent and its link with the

some on the active objects of the simulation model (member, trip and enterprise).

The household agent tries to model the behaviour of household agent in its daily

decisions and its implication of the residential location choices in the study area. The

household agent is formed by a set of members, the attributes of which were previously

described and those don’t have any decision making role, but represent the interaction

points of this agent with the environment and the other agents.

In order to model the household choices during the simulation process a set of

variables and functions were created to simulate their behaviour. Table 4.14 presents

the variables of the household agent and its description.

There are three main types of variables: variables used in the simulation process for

graphical output (e.g. hs_x, hs_y), auxiliary variables that are used in the agent

functions to calculate other decision making variables (e.g. zone, youngest, oldest),

and variables used during the simulation process for the decision taking (e.g. income,

balance). Some variables of the model can also work in some situations as auxiliary

variables and in other moments as decision variables (e.g. cars variable). All the

variables that relate with other elements of the model (e.g. life_cost), are dynamically

updated at every time step.

Table 4.14. Variables specification of the Household agent


hs_x First projection coordinate in simulation panel

hs_y Second projection coordinate in simulation panel



50


shape Point geometry presented in the simulation panel

selected Boolean variables indicating if it is selected in the simulation panel

points_visible Boolean variables indicating if it is visible in the simulation panel

colorH Colour variable of the agent in the simulation panel

zone Name of the zone were the household resides

members Array variable containing a set of objects of the member’s class

youngest Age of the youngest member of the household

oldest Age of the oldest member of the household

cars Number of cars of the household

trip_time Total travel time spent per month (hours)

trip_cost Amount of money spent in transportation per month (euros)

education_cost Amount of money spent in education per month (euros)

shopping_spent Amount of money spent in shopping per month (euros)

life_cost Total money spent per month (housing + transportation) (euros)

total_income Sum of the monthly salaries of the household’s members (euros)

income_spent Percentage of income spent per month

income Monthly savings from the total income (euros)

balance Accumulated savings (euros)

diss_time Recorded time of entry on Dissatisfied state

dissatisfied_keep Maximum time that the household can keep on Dissatisfied state

time Simulation time

The functions used in the household agent are presented in Table 4.15. There are

mainly two types of functions: the functions that are use by the household members to

populate their attributes (e.g. createMembers, die, findJob, findEducation, findShop)

and the functions that are used to assist the decision making process.



51

From the functions related to members the most important and that can have more

impact on the emergence of the urban system are the one related with the allocation of

job, study and leisure activities and their associated trips. These functions can have a

significant impact on the life cost of the household and determine its future relocation or

the relocation of activities, which cannot be directly controlled by the household.

Some of these functions encompass some formulation complexity and depend directly

of some attributes of the household’s members and the target activity. All these type of

functions incorporate a natural preference or willingness to perform these activities in

the zone surrounding the household location. This willingness is modelled as an

increase of the probability of choice (from the available set of choices) on the Monte

Carlo simulation process.

Table 4.15. Functions specification of the Household agent


findJob Search a job offer for a household member

findEducation Search a education facility for a household member

findShop Search a shop for a household member

createMembers Creates a new member in the household

die Erases a member from the members array variable

moveh Relocates the household in a new target zone

resetTrips Reset the trip set of each member due to the household’s

relocation

getMemberWorst Identifies the member with the lowest monthly salary

isExtreme Identifies if the household is in an Extreme state

isWaiting Identifies if the household is in a Waiting state

selectH Selects the household in the simulation panel



52

After the definition of the main variables and functions of this agent, the decision

making process of the household agent is explained. Figure 4.4 presents a flowchart of

the decision making process.

The flowchart is formed by four main states:

Happy (Satisfied), which indicates that the household has enough money to cover its life expenses and save some money to improve its life standards (e.g. buy a car or move for a more expensive and fancy neighbourhood).

Waiting that indicates some dissatisfaction from the household relative to its life quality. This is emulated by the fact of not being able to save for several months and the balance (savings of the household) start decreasing.

Recovery, which result from a change in the job of some of the members of the household or due to a residential relocation of the household.

Extreme that reflects the inability to cover the life cost of the household and a complete exhaustion of the accumulated savings.

Figure 4.4. Household’s agent decision making flowchart

Between these four states there are some different types of transitions of states:

Conditional transitions that are triggered when the condition is satisfied and make instantaneously a change in state from the household (e.g. relocate).



53

Timeout transitions between states, which are triggered when the agent enters the state and establishes a transition time between states (e.g. transition between Recovery and Waiting states). These transitions can also introduce some guard conditions to avoid automatic triggering of the transition (e.g. change work transition).

Timeout transitions inside the same state, which are triggered when the agent enters the state and establishes a transition time to re-enter the state (e.g. buy a car). These transitions, as the previous ones, can also introduce some guard conditions to avoid automatic triggering of the transition.

Default transitions between states, which are triggered when all the other possible transitions available cannot be triggered due to unfulfilled conditions (e.g. change work).

All the households enter the system in the Happy (Satisfied) state and, depending on

their financial status, activate some of the possible transitions or wait until the

conditions of some of the transitions are satisfied. In this state there are two possible

internal timeout transitions: buy a car, relocate. Both transitions emulate a living

standard improvement from the household, but at the sometime, an increase of the

living cost of household.

When the household is in the Waiting state, there are three possible transitions:

became Extreme, go the Recovery state by a possible change of work of some of the

household’s members or by the residential relocation, or become Happy again, which

normally can only happen after coming from a Recovery state.

When the household is in the Extreme state, the household can turn into the Waiting

state directly or through a previous Recovery state (resulting from a job change or

relocation).

4.2.8 Office

The office agent presents two types of interactions in the model: interactions with the

environment that result from the rental cost of the office, and with household agent

through the employment of the household’s members.

This agent tries to model the behaviour of an office in a competitive market

environment and its daily decisions in order to improve the business. The office agent

is formed by a set of employees (members of the households).



54

In order to model the office choices during the simulation process, a set of variables

and functions were created to simulate their behaviour. Table 4.16 presents the

variables of the office agent and a description of its meaning.


graphical output (e.g. of_x, of_y) and variables used during the simulation process for

the decision taking (e.g. income, balance, pressure_grow).

Although the variables from the enterprise active object and this agent are very similar,

the enterprise active object is only used to gather data of all the activities and

employment, while the office agent presents decision making ability (e.g. hire, bate).

Table 4.16. Variables specification of the Office agent


of_x First projection coordinate in simulation panel

of_y Second projection coordinate in simulation panel


color Colour variable of the agent in the simulation panel

zone Name of the zone were the office is located

enterprise Enterprise Id of the office

employees Array variable containing a set of employees of the office

business Monthly revenue collected by the office (euros)

income Monthly expenditure in salaries (euros)

rent Monthly rent of the office space (euros)


business_rate Rate of growth of the balance of the office

pressure_grow Indicator that identifies if the company need to grow

The office agent has only two functions which are presented in Table 4.17. Both

functions are related to the agent relocation into a cheaper or more competitive

location.

Table 4.17. Functions specification of the Office agent



55


move Relocates the office in a new target zone

moveForPrice Find the best new location for the office

After the definition of the main variables and functions of the office agent, its decision

making process is explained. Figure 4.5 presents a flowchart of the decision making

process.


Starting that only happens when the office enters the system for the first time or recovers after going bankrupt.

Working, that it is an intermediate state in the office development or business decrease period.

Grow, which indicates a positive financial situation of the office which can lead to an increase of the employees’ salaries, hire of new employees or relocation to more interesting location.

Reduce that reflects a slowdown of the office business and can lead to some dismissals and relocation into a cheaper location.

Almost every transition between states, or within the same state, is of the timeout type

(normally with guard conditions). The only exception is a transition between the

Reduce state and the Starting state relative to bankruptcy.

All the offices enter the system in the Starting state and, after a timeout period, move to

the Working state. In this state there are three possible transitions: a timeout internal

transition with one year duration (responsible for the salaries grow), and two other

possible timeout transitions with opposed guard conditions. The first transition occurs

whenever the business rate is equal or greater than one and moves the office into the

Grow state. The second transition is triggered if the office balance is negative and

changes the office into the Reduce state.

In the Grow state there are also three possible transitions: two internal timeout

transitions and a timeout guard transition into the Working state. One of the internal

timeout transitions is responsible for the growth of the salaries and the available job

places, and another relocates the office in a more competitive location.

In the Reduce state there are four possible transitions: two internal timeout transitions,

a timeout guard transition into the Working state, and a conditional transition into the

Starting state relative to bankruptcy. One of the internal timeout transitions is



56

responsible for the reduction of the staff size (employees dismissal), and another that

relocates the office in a cheaper location.

Figure 4.5. Office’s agent decision making flowchart

4.2.9 Education

The education agent presents again two types of interactions in the model: interactions

with the environment that result from the rent cost of the education facility and with the

household agent through the employment of the household’s members and enrolment

of the students.

The education agent tries to model the behaviour of an education facility in a mostly

public controlled market environment and its daily decisions in order to increase the

number of students up to its capacity. The agent is formed by a set of employees and

students that in some cases have to pay fees.

In order to model the education facility location choices during the simulation process,

a set of variables and functions were created to simulate their behaviour. Table 4.18

presents the variables of this agent and a description of its purpose in the model.


graphical output (e.g. ed_x, ed_y), variables used by students to choose the education



57

facility to attend (e.g. status, level, type, fee), and variables used during the simulation

process for the decision taking (e.g. income, balance, pressure_grow).

Table 4.18. Variables specification of the Education agent


ed_x First projection coordinate in simulation panel

ed_y Second projection coordinate in simulation panel



zone Name of the zone were the education facility is located

enterprise Enterprise Id of the education facility

employees Array variable containing the set of employees

business Monthly revenues of the school (euros)

income Monthly expenditures in salaries (euros)

rent Monthly rent of the education facility space (euros)


status Quality indicator of the education facility (0-1)

pressure_grow Indicator that identifies if the education facility needs to grow

level Integer code with the education level lectured

type Integer variable with a code of the research field (0-6)

publicB Boolean variable – 1 if education facility is public

max_students Maximum number of students of the education facility

fee Monthly fee value of the education facility

posdegree Boolean variable – 1 if the university has post graduation degrees

posgrad Monthly fee of post graduation degrees

students Array variable containing the set of students



58

The education agent has only six functions which are presented in Table 4.19. The first

function is relative to the initial set up of the education facility, while the next three

functions are relative to recording of the students. The last two are related to the agent

relocation into a cheaper or more competitive location.

Table 4.19. Functions specification of the Education agent


set Sets the attributes of the education facility when created

canJoin Identifies if a student can join the education facility

join Add a new student to the array of students

leave Removes a student from the array of students

move Relocates the education facility in a new target zone

moveForPrice Find the best new location for the education facility

After the definition of the main variables and functions of the education agent, its

decision making process is explained. Figure 4.6 presents a flowchart of the decision

taking process.


Starting that only happens when the education facility enters the system or after going to bankrupt (not possible in public owned facilities).

Working, that it is an intermediate state in the education facility development or decline period.

Grow, which indicates a positive educational and financial situation of the education facility which can lead to an increase of the employees’ salaries or hire of new employees.

Reduce that reflects a slowdown of the education facility and can lead to some dismissals and relocation into a cheaper location (not possible in public owned facilities).



Reduce state and the Starting state relative to bankruptcy (not possible in public owned

facilities).



59

All the education facilities enter the system in the Starting state and, after a timeout

period, move to the Working state. In this state there are three possible transitions: a

timeout internal transition with one year duration (responsible for the salaries grow),

and two other possible timeout transitions with approximately opposed guard

conditions. The first transition occurs whenever the pressure_grow is equal or greater

than 0.8 and positive balance and moves the education facility into the Grow state. The

second transition is triggered if the education facility balance is negative and changes

the office into the Reduce state.

In the Grow state there are also two possible transitions: one internal timeout transition

responsible for the growth of the salaries and the available job places, and a timeout

guard transition into the Working state.




responsible for the reduction of the staff size (employees dismissal), and another that

relocates the education facility in a cheaper location (not possible in public owned

facilities).

Figure 4.6. Education’s agent decision making flowchart



60

4.2.10 Shopping

The shopping agent presents also two types of interactions in the model: interactions

with the environment that result from the rental cost of the shop and with household

agents through the employment of its members and the visits of households the buy

their products.

The shopping agent tries to model the behaviour of a shop in a competitive

overpopulated market environment and its daily decisions in order to improve their

sales. The agent is formed by a set of employees and visitors that have a monthly rate

of trips to each shop.

In order to model the shop location choices during the simulation process, a set of variables and functions were created to simulate their behaviour.

Table 4.20 presents the variables of this agent and a description of its purpose in the

model.


graphical output (e.g. sp_x, sp_y), variables used by household’s members to choose

the shop to visit (e.g. status, price_level), and variables used during the simulation

process for the decision taking (e.g. income, balance, pressure_grow).

Table 4.20. Variables specification of the Shopping agent


sp_x First projection coordinate in simulation panel

sp_y Second projection coordinate in simulation panel



zone Name of the zone were the shop is located

enterprise Enterprise Id of the shop



61


employees Array variable containing the set of employees

business Monthly revenue of the shop (euros)

income Monthly expenditures in salaries (euros)

rent Monthly rent of the education facility space (euros)


status Quality indicator of the shop (0-1)

price_level Price statistical distribution of the store

pressure_grow Indicator that identifies if the shop needs to grow

size Size of the shop in square meters

visitors Array variable containing the set of visitors of the shop

The shopping agent has only six functions which are presented in Table 4.21. The first

function is relative to the initial set up of the store, while the next three functions are

relative to recording of the visitors. The last two are related to the relocation of the

agent into a cheaper or more competitive location.

Table 4.21. Functions specification of the Shopping agent


set Sets the attributes of the shop when created

canJoin Identifies if a household member can visit the shop

join Add a new visitor to the array of visitors

leave Removes a visitor from the array of visitors

move Relocates the shop in a new target zone

moveForPrice Find the best new location for the shop

After the definition of the main variables and functions of the shopping agent, its

decision making process is explained. Figure 4.7 presents a flowchart of the decision

making process.



62


Starting that only happens when the shop enters the system or after going to bankrupt.

Working, that it is an intermediate state in the store development or business decrease period.

Grow, which indicates a positive financial situation of the shop which can lead to an increase of the employees’ salaries, hire of new employees or relocation in more competitive location.

Reduce that reflects a slowdown of the shop business and can lead to some dismissals and relocation into a cheaper location.

Figure 4.7. Shopping’s agent decision making flowchart



Reduce state and the Starting state relative to bankruptcy.

All the shops enter the system in the Starting state and, after a timeout period, move to

the Working state. In this state there are three possible transitions: a timeout internal

transition with one year duration (responsible for the salaries growth), and two other

possible timeout transitions with opposed guard conditions. The first transition occurs

whenever the business rate is equal or greater than one and moves the shop into the



63

Grow state. The second transition is triggered if the shop business rate is smaller than

one and changes the shop into the Reduce state.

In the Grow state there are also three possible transitions: two internal timeout

transitions and a timeout guard transition into the Working state. One of the internal

timeout transitions is responsible for the growth of the salaries and the available jobs,

and another relocates the store in a more competitive location.




responsible for the reduction of the staff size (dismissal of employees), and another

that relocates the shop in a cheaper location.

4.2.11 Transportation Mode Choice Model

The transportation mode choice model was developed to estimate the mode share of

the two transportation modes considered (private car and public transportation as a

single choice) for the trips made by the households’ members during the simulation

run.

The model was developed considering the following hypothesis:

The model uses the best possible solution in travel time for each mode to compare them and estimate a probability of choice. The travel time includes in the public transportation mode the off-board time, the on-board time and the transfer time of the all transportation chain.

The attributes of the choice set (private car and public transportation) are equivalent and they have the same utility function.

Using these initial hypotheses a mode choice model was developed following the

flowchart presented in Figure 4.8. The model starts by the identification of the best

possible solution in travel time for a given origin-destination (OD) pair and mode (from

a previously established set of possible routes). This set of possible routes for ach OD

pair is presented below in the point relative to the route choice model.

After the identification of the best route for the given OD pair and each mode, the

model uses the route attributes the estimate (dynamically during the simulation run) a

utility function for each mode, from which the probability of choice is derived. After this,

the actual choice is simulated by cumulating the probabilities of the possible solutions

and generating a random number between 0 and 1, after which the mode is selected



64

for which the generated random number falls between its starting and ending

cumulative probability value.

Figure 4.8. Flowchart of the transportation mode choice model

Due to the lack of data available in order to calibrate a logit model (revealed or stated

preferences survey) it was used a generic utility function with non calibrated trade-offs

between the used variables and without household attributes variables. The variables

in the utility function are: travel time in hours, travel cost in euros and number of

transfers (for public transportation).

The utility function specification used is presented in (1). The coefficients of the

variables have been estimated using “good sense” trade-offs between the variables in

lack of available data to calibrate a model.

tranfersttimeUtilityMODE cos 4.2.11.1.1.1 (1)

The trade-offs between variables used are presented in Table 4.22.

Table 4.22. Trade-offs between variables of the mode choice logit model

Variable Travel time Travel cost Number of transfers



65

Travel time - 10 euros/h 2.57 transfers/h

Travel cost 0.1 h/euro - 0.26 transfers/euro

Number of

transfers

0.39 h/transfer 3.88 euros/transfer -

Considering the coefficient of time equal to -1, the following utility function can be

estimated:

tranfers39.0tcos1.0time00.1UtilityMODE (2)

Using the probability estimation equation of the logit model (3), it is possible now to

estimate the probability of choice of each possible route and run the model.

N

jjMODE

iMODEiMODE

Utility

UtilityP

1)exp(

)exp(

(3)

We recognize this is a very rough approach but it was used as a first approach to the

mode choice modelling and will be improved in future steps of the research if more

data is available.

4.2.12 Route Choice Model

The route choice model is a sub-model of the main model that is used during the

simulation run to determine the route used for each trip of the households’ members.

The model is based on a set of possible routes between each pair of zones of the model that are recorded on a database. During the simulation run, the utility of each possible route in the pre-

selected mode is estimated using a utility function, after which the probability of use of each route is computed. After this, the actual route taken is chosen by using a random number between 0 and

1, as was done for the mode choice.

Figure 4.9 presents the flowchart of the route choice model with all the model steps.

Figure 4.9. Flowchart of the route choice model



66

After the definition of the framework of the model, we present how the route choice set

for each origin-destination (OD) pair was estimated for the private car and for public

transportation.

For the private car estimation of the route choice set for each OD pair, demand data

from the Lisbon’s Mobility Plan Survey was used, based on an available network for the

LMA area.

A macro-simulation model using the Aimsun software was then calibrated for the morning peak hour. The results are presented in

Figure 4.10 where it is easy to perceive the congestion on the main roads around the

Lisbon municipality.

Using the modelled traffic flows and the congested travel times of all the arcs of the

network, a k-shortest paths calculation between all the OD pairs was performed to

estimate the k=10 best routes under congested conditions constrained to 50% time

increase compared to the shortest route, which reduced in some cases the creation of

1 or 2 paths only. This modelling resulted in a total set for the 66 zones of 6231 routes

instead of an unconstrained situation of 42900 paths.



67

Figure 4.10. Traffic assignment results for the LMA morning peak

For the public transportation estimation of the route choice set for each OD pair, the

information available about all the current services available on the LMA area was

used, considering railway, metro and bus services. The current services with available



68

data are presented in Figure 4.11 where a lack of coverage in some municipalities of

the north area of the LMA can be perceived, due to lack of available data.

Using Geographic Information Systems (GIS) software (Geomedia Professional 5.2),

the off-board, on-board and transfer times between different services were calibrated

using the available data.

After this initial calibration, a GIS application (Geomedia Transportation Analyst) was

used to estimate the k-shortest paths for OD pairs (just considering travel time) and

non congested environment (fixed travel times) constrained to 50% time increase

compared to the shortest route. This modelling resulted in a total set for the 66 zones

of 6120 routes.

Figure 4.11. Public Transportation lines used in the route choice model



69

After the identification of all possible routes that the can be used in the model during

the simulation run, the method used for route utility estimation had to be established.

Due to the lack of data available in order to calibrate a logit model (revealed or stated

preferences survey) a generic utility function was used with non calibrated trade-offs

between the used variables. The variables in the utility function are: travel time in

hours, travel cost in euros and number of transfers (for public transportation).

The utility function specification used is presented in (4) where the coefficients of the

variables were estimated using trade-offs between the variables from previous studies

in lack of available data to calibrate a model.



70

tranfersttimeUtilityROUTE cos 4.2.12.1.1.1.1.1.1 (4)


Table 4.23. Trade-offs between variables of the route choice logit model

Variable Travel time Travel cost Number of transfers

Travel time - 10 euros/h 2.57 transfers/h

Travel cost 0.1 h/euro - 0.26 transfers/euro

Number of

transfers

0.39 h/transfer 3.88 euros/transfer -

Considering the coefficient of time equal to -1, the following utility function can be

estimated:

tranfers39.0tcos10.0time00.1UtilityROUTE (5)

Using the probability estimation equation of the logit model (6), it is possible now to

estimate the probability of choice of each possible route and run the model.

N

jjROUTE

iROUTEiROUTE

Utility

UtilityP

1)exp(

)exp(

(6)

The method used is unreliable given the roughness of the parameters, but it was used

as a first approach to the route choice modelling and will be corrected in future steps of

the research if more data is available. The purpose in this paper is simply to

demonstrate the workability of the agent-based simulation approach with this complex

formulation.



71

4.2.13 Real Estate Price Model

The real estate price model implemented in the simulation model results from two

components:

Hedonic price models developed for residential, commercial and offices. These models were calibrated for the LMA in a previous studies (Martínez 2009; Martínez and Viegas 2009).

Demand-supply ratio or scarcity of supply.

The summary of the hedonic price model used for the residential properties is

presented in Table 4.24. The hedonic price model presented was developed by

Martinez et al. (2009) and uses an Ordinary Least Squares (OLS) method that allows

an easy measurement of the variables during the simulation run, which would be

difficult with a spatial lag model.

Similarly Table 4.25 presents the hedonic price model used for the commercial and

offices properties using once again an OLS model.

Table 4.24. Summary of the residential hedonic price model used for the LMA (Martínez and Viegas 2009)

Variables Coef. Std. Error SP_LAG_LOGPRICE 0.3561 *** 0.0085 Constant 6.9089 *** 0.0999 Structural attributes Bedrooms 0.0427 *** 0.0030 House 0.1685 *** 0.0154 Floor 0.0155 *** 0.0009 Area 0.0064 *** 0.0001 Age2 -0.1034 *** 0.0063 Age3 -0.0729 *** 0.0068 Garage 0.1126 *** 0.0059 Neighbourhood attributes Educational Index 0.4160 *** 0.0225 Entropy Index 0.2312 *** 0.0234 Local Accessibility Attributes 2MAccess 0.0916 *** 0.0133 1Maccess 0.0652 *** 0.0084 Network1 -0.0732 *** 0.0069 Network2 0.0458 *** 0.0064 Network3 -0.0380 *** 0.0060 Sintra -0.0614 *** 0.0134 Cascais 0.1517 *** 0.0259 Pseudo R2 0.795



72

LM statistic 1154.496 *** Log likelihood 236.608 ***, **, and *

denote coefficient significantly different from zero at the 1%, 5%, and 10% level of significance (two-tailed

test), respectively.

Table 4.25. Summary of the commercial and offices hedonic price model used for the LMA (Martínez 2009)

Variables Coef. Std. Error SP_LAG_LOGPRICE 0.0394 *** 0.0088 Constant 10.2640 *** 0.1143 Structural attributes Store 0.4147 *** 0.0446 Office 0.3892 *** 0.0530 Floor 0.0227 ** 0.0108 Area1 0.0079 *** 0.0002 Area2 0.0018 *** 0.0001 Area3 0.0005 *** 0.0001 Age2 -0.1775 *** 0.0281 Age3 -0.1560 *** 0.0263 Garage 0.1316 *** 0.0344 Neighbourhood attributes Educational Index 0.9892 *** 0.0939 Shopping Centre 0.2308 0.1518 Local Accessibility Attributes 2MAccess 0.2163 *** 0.0466 1Maccess 0.0918 ** 0.0357 Network1 -0.1270 *** 0.0360 Network2 0.1029 *** 0.0302 Pseudo R2 0.760 LM statistic 19.940 *** Log likelihood -693.486 ***, **, and *

denote coefficient significantly different from zero at the 1%, 5%, and 10% level of significance (two-tailed

test), respectively.

The component of the real estate price model is a demand-supply ratio that is

computed dynamically during the simulation. This ratio depends of the availability of

properties by zone that is determined by the municipality (derived from the municipal

site plans) and the demand of properties for a specific use that can change along the

time in the simulation model. The demand-supply ratio works as an adjustment factor

to the price estimated by the hedonic price model in order to incorporate the real estate

market dynamics.

This factor is calculated using the following logistic function:



73

))ply ratiodemand-sup-(331052.2-5.31421(exp13DS factor

(7)

where its graphical output is presented in

Figure 4.12.

Figure 4.12. Demand-supply factor

The resulting property price it will be then given by:

)( EurosDStimateHedonic esPrice Estimated factor

The property price has to be converted into a monthly rent paid by the household to the

bank. This monthly rate is dependent of the macroeconomic circumstances of the

financial market (Euro Interbank Offered Rate – EURIBOR) that is given by the

macroeconomic model not presented in this paper.

The estimation of the monthly payment to the bank considers a thirty years period of

the bank loan, a less down payment of 5%, and an interest rate (IR) that result from

annual average of the EURIBOR index plus a bank spread of 1,05% and 0,5% of loan

Dem

and-

supp

ly fa

ctor

Demand-supply ratio



74

expenses (e.g. mortgage insurance and loan taxes). The estimation is obtained from

the following equations:

)( )1)1(()1(

))1((

)( 12

)5.0(

360

360

EurosIRIR

IRPrice EstimatedIRPayment Monthly Mortgage

temonthly ra SpreadEURIBORIR

(8)

The mortgage monthly payment obtained is an estimative of the household and

business monthly fixed costs which can have a significant impact in the household or

business budget and can trigger relocation in the study area.

4.2.14 Residential and business location choice model

One of the main features of an urban system model is the location choice model for

residential and business. These models require a large quantity of data on the

households and business choices in order to calibrate the models. The most common

models in the literature are the discrete choice models (multinomial logit models) but

other methods as the reference dependent models based on Prospect Theory are been

applied lately.

In absence of location choices data, it was developed based on data from previous

studies in other locations. Two different models were built up:

a residential location choice model, using attributes of the household and some aggregate attributes of the zones of the model;

a business location choice model, using attributes of the enterprise characteristics (e.g. number of employees and sector) and some aggregate attributes of the zones of the model;

The developed residential location model uses a simplified reference dependent model

were the reference is based on the actual location on the household. Based on the

work of Habib and Miller (2009), the general utility function of a dwelling i for a

relocation of decision maker j in a purchase occasion t within a utility maximization

framework is given by:



75

ijtijtijtijtijt XLossGainU 210 (9)

Here, Gain refers to the amount of attribute value by which the alternative dwelling i

exceeds that of current dwelling for the decision maker j at choice occasion t. And,

Loss refers to the amount of attribute value by which dwelling i is below than that of

current dwelling for household j at choice occasion t. Xijt denotes other attributes such

as dwelling type, price etc. for the dwelling unit i at choice occasion t (Habib and Miller

2009).

Collectively denote Gainijt, Lossijt and Xijt as Zijt and all the corresponding coefficients as

β. Now, assuming εijt as independently and identically distributed (iid) the choice

probability of the decision-maker j choosing dwelling unit i in the choice occasion t can

be expressed as McFadden’s multinomial logit form (McFadden 1977):

k

i

Z

Z

ijtijtj

ijtj

e

eP

1

(10)

The model considers two main attributes for the gain and loss measurement:

the travel costs that result from the actual set of activities of the household (work, education and leisure) also including travel time costs (5 Euros per hour);

a status coefficient of the zone estimated using the average income of the households living in the zone relative to the total average of the LMA.

Depending on the household state and the cause for relocation, the model uses

different gain and loss specifications:

If the household desires an improvement in its live standards (satisfied state) the choice will be more dependent on the status of the zone – improve. In this case are considered gains in the status coefficient as the major factor, but also losses in travel costs are considered.

If the household desires a reduction in its live costs (waiting state) the choice will be dependent of the reduction of the combination of the mortgage monthly payment and the travel costs – reduce. In this case are considered losses in travel costs.



76

If the household needs desperately to reduce its live costs (extreme state) the choice will rely on the mortgage monthly payment – extreme. In this case neither gains nor losses are considered.

The attributes of the dwelling considered in the utility function are:

The natural logarithm mortgage monthly payment (LMMP) that results for a dwelling located in the different zones of the model (depending on the number of rooms, the age and the parking availability requirements) (Euros).

The natural logarithm of the proportion of household income devoted to mortgage payment (LPMP).

After the definition of the main components of the residential location model, the utility

function is defined using coefficients based on the data from Toronto (Habib and Miller

2009). Three different utility functions are presented with depending on the relocation

cause:

LPMPLMMPU

LPMPLMMPLossU

LPMPLMMPLossGainU

EXTREME

COST TRAVELREDUCE

COST TRAVELSTATUSIMPROVE

0487.02798.1

0487.02798.11872.0

0487.02798.1

1872.03327.0

(11)

The calculation of the utility function for all the possible location allows the estimation of

the probability of location of household j in the zone i. With the probability distribution of

all the target zones estimated, the model generates a random number between 0 and 1

and selects the zone with highest accumulated probability that is smaller than the

random number generated.

Due to the lack of data, the business location model was developed a multinomial logit

based on qualitative perception of the trade-offs between variables. The variables in

the utility function are: number of employees of the zone, the mortgage monthly

payment (as a proxy of the monthly rent), and a land use mixture indicator (entropy

index).



77

The utility function specification used is presented in (12) where the coefficients of the

variables were estimated using “good sense” trade-offs between the variables in lack of

available data to calibrate a model.

entropymortgageemployeesUtilityZONE (12)


Table 4.26. Trade-offs between variables of the route choice logit model

Variable Employees Mortgage Entropy

Employees - -0.2 euros/employee 0.0005 1/employees

Mortgage -5 employees/euro - -0.0025 1/euro

Entropy 2000 employees/1 -400 euros/1 -

Considering the coefficient of employees equal to 1, the following utility function can be

estimated:

entropy2000mortgage5employees00.1UtilityZONE (13)

Using the logit probability estimation equation (14), it is possible now to estimate the

probability of choice of each possible zone and run the model.

N

jjZONE

iZONEiZONE

Utility

UtilityP

1)exp(

)exp(

(14)

With the probability distribution of all the target zones estimated, the model generates a

random number between 0 and 1 and selects the zone with highest accumulated

probability that is smaller than the random number generated.



78

The methodology used is highly unreliable, but it was used as a first approach to

business location choice modelling and will be corrected in future steps of the research

if more data is available.

4.2.15 Other sub-models and triggered events of the simulation model

The simulation model contains sub-models are actions that are performed during some

scheduled events for decision making of the household agent:

The monthly scheduled events are responsible for the shopping and leisure program and assignment of the members of the household as clients.

The yearly scheduled events present several actions and sub-models that tested or performed:

o Find education centre for next education level or degree. o Have a child in households with married members. o Find a spouse/husband to marry and form a new household, change

existing households or merge households. o Leave the parents home and form a new household.

These sub-models or actions are formed probability conditioned decision trees

procedures (see Figure 4.13), which are not presented in detail in this paper. Al these

actions are tested using a random generation process if some probabilities parameters

are verified. These actions encompass a significant number of parameters which must

be calibrated before the verification and validation of the model.

Figure 4.13. Conditioned decision tree example



79

4.3 Data Requirements for Input Data and Validation of the Model

This section focuses on the data requirements for the data input and calibration of the

model. The data requirements are structured by type of agent that uses this data in the

simulation model:

Data used for the household definition as input data and for calibration of some parameters of the choices models included in the functions of this agent.

Data relative to the employment and activities location for the definition of the enterprise related agents.

Qualitative information from the Municipality/Government, Land Owner/Developer agents in order to design their decision making flowchart.

Geographical information and characteristics of the transportation network arcs. Traffic data (measured or estimated) used for calibrate parameters of the model

(transportation mode choice model and route choice model) Real estate price model calibration data resulting from available cross-sectional

and time series databases.

4.3.1 Household data

The developed simulation model requires a vast quantity of data about households’

composition and attributes. In order to estimate the households’ composition and

members attributes from the census data it was built up a synthetic population

procedure based on the Birkin and Clarke process (Birkin and Clarke 1988), also

described later by Williamson et al. (Williamson, Birkin et al. 1998).

There are several synthetic population procedures more sophisticated than the current

previously presented, but they are based on seed populations, which were not

available for this study reducing the range of options. Some of this procedures are

used to develop inputs to activity based models as DAYSIM (Bowman and Bradley

2006) or Multi-Agent Integrated Land Use / Transportation Models as ILUTE (Pritchard

and Miller 2009).

The synthetic population reconstruction process suggested by Birkin and Clarke is presented in



80

Figure 4.14. The reconstruction follows an iterative stochastic process using a Monte

Carlo simulation approach until the convergence criteria is achieved. The convergence

criteria are measured by the error relative to the census data in all the aggregate

values for the main households’ attributes for each spatial location.



81

Figure 4.14. The synthetic population reconstruction process (Birkin and Clarke 1988)

The reconstruction procedure used is similar to the one presented by Birkin and Clarke but just estimates

part of the attributes of the households’ members suggested by them. The procedure was programmed in

VBA macro in Excel and follows the reconstruction sequence presented in

Figure 4.15.



82

Figure 4.15. Used synthetic population reconstruction process

The procedure incorporates a correction coefficient to the attributes probability

functions distributions (based in census data) for each age group that is computed

every iteration in order to reduce the convergence error. The correction coefficients

start the process with the value of 1 and increase or decrease during the process in

order to achieve the convergence error. The correction coefficient equation is:



83

Corr(i)

members Householdmembers Household

P1)Corr(i

GROUP AGE

GROUP AGEGROUP AGE

'#'#

(15)

where P is the probability of an age group in the census data. The convergence error is

controlled by the overall fit of some population data for each location considering as

maximum convergence error a 5% relative error. The variables used to measure the

error are:

the total number of inhabitants for each location; the percentage of population per age group for each location; the percentage of population of each sex for each location; the percentage of different household sizes for each location; the percentage of the different marital status of the population for each location; the percentage of different education levels of the population for each location.

Figure 4.16. Correction coefficient convergence during the estimation process

Some of the aggregated statistics obtained for the synthetic population of the LMA are

presented in In order to validate the model, it also required time series data of the

population per age group and number of households for each location. This data is

0

2

4

6

8

10

12

14

0 10 20 30 40 50 60 70 80

Corr

ectio

n co

effic

ient

Iterations

0-45-910-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100



84

available from annual estimates of the Portuguese National Statistics Institute (INE)

(www.ine.pt).



85

Figure 4.17 and Figure 4.18.

In order to validate the model, it also required time series data of the population per

age group and number of households for each location. This data is available from

annual estimates of the Portuguese National Statistics Institute (INE) (www.ine.pt).



86

Figure 4.17. Age distribution of the synthetic population of the LMA

Figure 4.18. Number of members of household distribution of the synthetic population of the LMA

0,00% 1,00% 2,00% 3,00% 4,00% 5,00% 6,00% 7,00% 8,00% 9,00%

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99

100

Percentage of population per age group

Age

grou

ps Census dataEstimated

Perc

enta

ge

Number of members of the household Estimated Census data



87

4.3.2 Activities and Employment data

The developed simulation model also requires a large quantity of data about business

spatial distribution and employment. A considerable part of this data was not available

leading to several simplifications in the inputs of the model.

It was obtained aggregate data about the quantity of activities (education centres,

offices and shops) for each zone of the study area, which was used to set the inputs of

activities spatial distribution. There was information available on the size of the different

activities, the employment they generate and their trip attraction. The attributes relative

the activity size and the employment were then randomly generated for each activity.



88

Figure 4.19 presents a spatial distribution of the quantity of activities in the LMA considering a dot density procedure inside each zone in order to increase the readability of the results. As the figure shows there is a great concentration of offices in the centre of the Lisbon municipality, which generates high commuter traffic levels from the other municipalities.



89

Figure 4.19. Spatial distribution of the quantity of activities in the LMA

4.3.3 Municipality/Government, Land Owner/Developer data

The simulation model also needs data from the land controlled by the municipalities

and developed by the developers. It was gathered some information from the census

on the residential capacity for each zone of the model, although data of the business

space supply is lacking to introduce in the model. There is also a lack of data about



90

vacant land to develop for each zone, which will be collected for future developments of

the model.

It is important to be aware that for the modelling of the behaviour of these two agents, it

will be needed a lot in qualitative information that it might be obtained through

interviews.

4.3.4 Public Transport Operators data

4.3.5 Geographical data

There is a large set of geographical data required for the model simulation as zones

boundaries, the transportation network (road and public transportation). An important

part of this data is already available and inserted in the model, although there a

significant lack of information of bus services in some municipalities of the LMA which

can bias the results in transportation mode choice model.

4.3.6 Traffic data

The traffic data from the road network of the LMA and data of number of passengers

from the public transportation systems are important for the verification and validation

of the model.

There is road traffic data available from a macro-simulation model of the LMA

developed and calibrated for the 2004 Lisbon Mobility Plan (Câmara Municipal de

Lisboa 2005). Relative to the public transportation services there is data available at an

aggregate level about the annual number of passengers for each service. These data

will be used to verify and validate the model.

4.3.7 Real estate price data

The simulation model requires a significant quantity of data on real estate price data for

the calibration of the real estate model and to validate the model. There are two

different data sources of real estate price that are being used in this model:

Cross-sectional real estate asking prices database with properties location geocoded for the municipalities of Lisbon, Amadora and Odivelas (Online realtors’ database of Imokapa Vector – February 2007).

Aggregated time series database of the average price per square meter of real estate for different uses in the Lisbon municipality (database of Confidencial Imobiliário – January 1988 to December 2004)

The cross-sectional real estate price data was used to calibrate the real estate price

models (Martínez 2009; Martínez and Viegas 2009). The time series real estate price



91

data it will be used for the verification and validation of the model in future stages of the

model development process.

5 Conclusions and Further Developments

Recalling the purposes of EXPoD project, we have:

to understand decision making processes in BRT systems and

to develop a formal structure for retrospective analysis of the various interplaying policy components, and finally

to develop a systems dynamic model to search for well-designed and promising policy packages.

The objective concerning the modelling approach has been adjusted. Upon the

analysis of the interactions between the agents in an urban transport system, we

concluded that agent based modelling would provide a more suitable approach than

system dynamics. As explained, a micro-simulation approach allows simulating each

agent individually as well as their interactions, providing better results on the changes

of behaviour of the agents.

The model is structured around three modules, being:

Demand

Supply

Impacts of policy packages

The first module is already concluded.

The next steps include:

development of the remaining two modules: supply and impacts of policy packages.

validation and calibration of the model using data from the Lisbon Metropolitan Region.

customisation of the model to other urban regions.

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i Entropy index used to measure the land use mixture was developed by Cervero and Kockelman (1997) and then adapted by Potoglou and Kanaroglou (2008) to consider point data and 500 meters around a point.

design and implementation of a simulation tool...

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