Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

FACULTY OF SCIENCE UNIVERSITY OF COPENHAGEN

MAPPING CYCLISTS’ EXPERIENCES AND AGENTBASED MODELLING OF THEIR WAYFINDING BEHAVIOUR PhD Thesis Bernhard Snizek, MSc

Department of Geosciences and Natural Resource Management Faculty of Science University of Copenhagen Copenhagen, 2015 Academic main supervisor: Hans Skov-Petersen, PhD, Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark Academic co-supervisor: Thomas Alexander Sick-Nielsen, PhD, Department of Transport, Technical University of Denmark, Kgs. Lyngby, Denmark Academic co-supervisor: Prof. dr. Nico Van de Weghe, CartoGIS, Ghent University, Ghent, Belgium Academic co-supervisor: dr. Tijs Neutens, PhD, CartoGIS, Ghent University, Ghent, Belgium This thesis has been submitted to the PhD School of The Faculty of Science, University of Copenhagen th Date of Submission: June 10 , 2015

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Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

Name of department:

Department of Geosciences and Natural Resource Management

Faculty:

Faculty of Science

University:

Copenhagen University, Denmark

Author:

Bernhard Snizek

Title:

Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

Subject description:

This thesis is about mapping cyclists' experiences and the agent-based modelling of cyclists' behaviour in urban settings.

Funding:

This dissertation is part of the Bikeability project (Anon 2014). Bikeability was an interdisciplinary research project, which lasted from 2010 – 2013 and was funded by the Danish Council for Strategic Research. The project investigated cycling from different angles: sociology, public health, modelling, geography, studies of the built environment, and planning research.

Academic supervisors:

Hans Skov-Petersen, PhD, Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark Thomas Alexander Sick-Nielsen, PhD, Department of Transport, Technical University of Denmark, Kgs. Lyngby, Denmark Prof. dr. Nico Van de Weghe, CartoGIS, Ghent University, Ghent, Belgium dr. Tijs Neutens, PhD, CartoGIS, Ghent University, Ghent, Belgium

Submitted:

On June 10th 2015 to the PhD School of The Faculty of Science, University of Copenhagen, Denmark

Citation style:

Harvard Reference format 1 (author-date)

Š Cover photo:

Chunli Zhao, University of Copenhagen

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Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

for Thomas M谩ni & Tobias Fl贸ki Chunli and my parents

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Abstract

This dissertation is about modelling cycling transport behaviour. It is partly about urban experiences seen by the cyclist and about modelling, more specifically the agent-based modelling of cyclists' wayfinding behaviour. The dissertation consists of three papers. The first deals with the development and application of a method for collecting experiential data via an internet-based questionnaire and statistically relating them to physical features of the city as well as the characteristics of their routes. The other two papers explain methods for building, calibrating and validating an agent-based model of cycling transport behaviour using geodata, data from the Danish travel survey as well as behavioural data extracted from trajectories recorded utilising GPS units.

Paper 1: Mapping Bicyclists’ Experiences in Copenhagen This paper presents an approach to the collection, mapping and analysing of cyclists’ experiences. By relating spatial experiences to urban indicators such as land-use, street characteristics, cycle infrastructure, centrality and other aspects of the urban environment, their influence on cyclists’ experiences were analysed. 398 cyclists responded and plotted their most recent cycle route and a total of 890 points for locations along the route where they had had positive and negative cycling experiences. The survey was implemented as an online questionnaire built on top of Google Maps, and allowed up to three positive and three negative experiences to be plotted on a map and classified. By relating the characteristics of the experience points and the routes to the traversed urban area in general, the significance of the preconditions for obtaining positive or negative experiences could be evaluated. Urban spaces can, thereby, be mapped according to whether they potentially promote positive or negative experiences. Additionally, the method may be applied to measure the effect of proposed changes to the urban design in terms of cyclists’ experiences. Statistical analysis of the location attributes, traffic environments and conflicts, cycle facilities, urban density, centrality, and environmental amenities indicate that positive experiences or the absence of negative experiences are clearly related to the presence of enroute cycling facilities and attractive natural environments within a short distance of large water bodies or green edges along the route.

Paper 2: Modelling cyclists' GPS trajectories with spatial agents and model calibration data creation This paper has two objectives, which are to develop and present a method for simulating single GPS-based trajectories by applying an agent-based model and to acquire parameter values for CopenhagenABM, an agent-based model of cyclists’ behaviour. The core of the model, the Behavioural Edge Choice Matrix (BECM), which is responsible for the agent's network edge choice in any node of the road network, was designed and expressed to comprise both local parameters and a global one. The local parameters, which represent

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subjective weighted preferences of the immediate vicinity of the decision point, i.e. a node of the network such as the greenness of outgoing edges and the availability of cycle infrastructure, the traffic environment are set by values retrieved from the analysis of GPS trackings of cyclists on their everyday trips.

The global parameter, i.e. the directional deviation was established in order to reflect the agent's knowledge of the direction towards the destination of the journey. In order to analyse the performance of the model, the model was run with a series of different values for the global parameter with and without taking the local parameter into consideration. The resulting routes' overlap with routes taken from the real world was calculated and used as a qualifier for the capacity of the model to explain the real world phenomenon. The analyses and the conclusions from these model results are discussed at the end of the paper.

Paper 3: CopenhagenABM: An Agent-based Model of Bicyclists' Trajectories and Flow CopenhagenABM, an agent-based model of cyclists’ wayfinding behaviour, was designed and implemented and its results were compared to real world counts. The central component of wayfinding, the Behavioural Edge Choice Matrix (BECM), was parameterised building on choice estimates generated from the analysis of GPS tracks recorded from Copenhagen cyclists as a local selection component and the direction towards the trip destination as an overall cognitive component. These rules were implemented into agents and the spatial behaviour was upscaled to the city level. The model was implemented in rePAST, a state-of-the-art agent-based modelling toolkit. The behavioural parameter estimates were generated from GPS tracks. A road network was taken from OpenStreetMap and enriched with information about the traffic environment, public register-based source and destination coordinates and origin/destination data from the Danish traffic model.

Counting data provided by the Municipality of Copenhagen were used to compare the results of the model with real world counts.

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Resumé

Denne artikelbaserede afhandling omhandler modellering af cyklisme. Den handler delvist om oplevelser af byen set fra en cykel, men mest om modellering - mere specifikt om agentbaseret modellering af cyklisters formålsrettede bevægelse i byen. Afhandlingen består af tre artikler: Den første artikel omhandler udviklingen og brugen af en metode til at indsamle erfaringsbaserede data ved hjælp af et internetbaseret spørgeskema, og statistisk at sætte disse data i forbindelse med byens fysiske træk såvel som med ruteafhængige parametre. De sidste to artikler forklarer metoder til at konstruere, kalibrere, og validere en agentbaseret model af cyklisters adfærd, en model der anvender geografiske data, data fra den danske nationale transportvaneundersøgelse, og adfærdsdata fra bevægelsesmønstre indsamlet ved hjælp af gps.

Artikel 1: Kortlægning af cyklisters oplevelser og erfaringer i København Denne artikel præsenterer en tilgang til indsamling, kortlægning, og analyse af cyklisters oplevelser og erfaringer. Artiklen analyserer hvordan urbane indikatorer som arealanvendelse, gadebilledets karakteristika, cykelrelateret infrastruktur, og andre aspekter af bymiljøet påvirker cyklisters oplevelser og erfaringer, ved rumligt at relatere lokaliserede erfaringer til disse indikatorer.

398 cyklister deltog i undersøgelsen og angav deres seneste cykelrute samt punkter for lokationer langs ruten, hvor de havde positive eller negative oplevelser som cyklister. Der blev ialt indsamlet 890 punkter. Undersøgelsen blev implementeret som et online spørgeskema bygget ovenpå Google Maps, og tillod definition og klassificering af op til tre positive og tre negative erfaringslokationer.

Ved at relatere de indsamlede punkters karakteristika med ruterne til det gennemrejste byområde, kunne signifikansen af betingelserne for at give positive eller negative oplevelser evalueres. Dermed kan byområder kortlægges alt efter om de har potentiale for at fremme positive eller negative oplevelser. Desuden kan metoden anvendes til at måle effekten af eventuelle foreslåede ændringer af byens design i forhold til cyklisters erfaringer og oplevelser. Statistisk analyse af de enkelte lokationers egenskaber, de trafikale omgivelser og konflikter, faciliteter relateret til cykling langs ruten, den bymæssige tæthed, og miljømæssige kvaliteter, indikerer at positive oplevelser, eller fraværet af negative oplevelser, er tydeligt relaterede til tilstedeværet af både cykelfaciliteter såvel som attraktive naturmiljøer - f.eks. tæthed til større vandområder eller grønne kanter - langs ruten.

Artikel 2: Modellering af cyklisters GPS-ruter med rumlige agenter, og konstruktion af data til modelkalibrering.

Denne artikel har to mål: For det første at udvikle og præsentere en metode til at genskabe enkelte, GPS-baserede ruter, og for det andet at erhverve

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paramaterværdier for CopenhagenABM, en agentbaseret model af cyklisters adfærd. Modellens kerne, Behavioural Edge Choice Matrix (BECM), der er ansvarlig for agentens valg af en kant fra en given knude i netværket (som model af vejnettet), er designet til at omfatte både lokale såvel som ét globalt parameter. De lokale parametre, der repræsenterer subjektive, vægtede præferencer i den umiddelbare nærhed af beslutningspunktet, som for eksempel hvor grønne udgående kanter opfattes, eller graden af tilgængelighed af cykelrelateret infrastruktur osv. er sat ud fra værdier indsamlet i en analyse af GPS- ­ ‐ registreringer af hverdagscyklisters bevægelser. Den globale parameter er etableret for at kunne afspejle agentens viden om retningen af rejsens mål.

For at kunne analysere modellens forklaringsevne er modellen blevet kørt med en serie af forskellige værdier for den globale parameter, og både med og uden hensyntagen til de lokale parametre. De resulterende ruters overlap med ruter optaget i den virkelige verden er beregnet og anvendt som mål for modellens evne til at forklare fænomener fra den virkelige verden. Analyser og konklusioner baseret på resultaterne af modelkørslerne diskuteres til slut i artiklen.

Artikel 3: CopenhagenABM: En agentbaseret model af cyklisters ruter og flow CopenhagenABM, en agentbaseret model af cyklisters rumlige adfærd, er designet og implementeret med udgangspunkt i antagelsen af at cyklisters rumlige adfærd er lokaliseret mellem fodgængeres bevægelser og vejnetværkets motoriserede trafik. Dens resultater er sammenlignet med trafiktællinger fra den virkelige verden.

Som lokale valgparametre for den centrale komponent (Behavioural Edge Choice Matrix - BECM) anvendes data genereret igennem analyse af GPSruter fra cyklister i København. Som global, kognitiv parameter anvendes retningen mod målet. Disse adfærdsregler er implementeret i agenterne og opskaleret til byniveau. Modellen er blevet implementeret i rePAST, et tidssvarende agentbaseret modelleringsværktøj med adfærdsparametre, med et vejnetværk taget fra OpenStreetMap, og hvor data om trafikmiljøet fra offentlige registerdata er blevet tilføjet. Trafiktællingsdata leveret af Københavns Kommune er anvendt til at sammenligne modellens resultater med tællinger fra den virkelige verden.

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Zusammenfassung

Diese Arbeit behandelt das Erfassen der Erlebnisse von Radfahrern, und darauf aufbauend, das Modellieren ihres Orientierungsverhaltens. Sie besteht aus drei Artikeln.

Der erste beschreibt die Entwicklung und Anwendung von Methoden zur Erfassung von Erlebnisdaten durch einen Online-Fragebogen. Diese Daten dienen dem statistischen Modellieren von Einflüssen, die räumliche Eigenschaften der Stadt auf gutes oder schlechtes Erleben der Umgebung haben können. Der zweite Artikel behandelt die Entwicklung eines agentenbasierten Modells des Orientierungsverhaltens von Radfahrern. Die Validierung und Kalibrierung dieses Modells erfolgt unter Zuhilfenahme von Geodaten, Daten des Dänischen Verkehrsmodells und Verhaltensdaten, die aus der Analyse von GPS Daten gewonnen werden.

Artikel 1: Kartographie der Erlebnisse von Radfahrern in Kopenhagen Dieser Artikel beschreibt, wie Erlebnisse von Radfahrern kartographiert und analysiert werden können. Durch den statistischen Vergleich von räumlichen Erlebnissen mit urbanen Indikatoren, wie zum Beispiel Flächennutzung, Charakteristik des Straßenraums, Radwegen, Zentralität und anderen Eigenschaften des Stadtmilieus wurde deren Einfluss auf das Erleben der Radfahrer analysiert. 398 Radfahrer beantworteten die Fragebogen. Sie zeichneten ihre zuletzt gefahrene Route auf und markierten 890 Orte, die sie in guter oder schlechter Erinnerung hatten. Der Fragebogen wurde online mittels GoogleMaps implementiert und ermöglichte die Angabe von je drei guten und drei schlechten Erlebnissen. Die Signifikanz der Voraussetzungen für gutes oder schlechtes Erleben wurde statistisch ermittelt und dabei konnte belegt werden, welche städtischen Eigenschaften und Gegebenheiten zu gutem oder schlechtem Erleben führen oder dieses verhindern. Darüber hinaus kann diese Methode dazu verwendet werden, die Effekte von Veränderungen im Stadtbild auf das Erleben der Radfahrer zu erfassen.

Die statistische Analyse der räumlichen Attribute zeigt, dass besonders gute Radfahrinfrastruktur und die Nähe von Grünraum am ehesten zu gutem Erleben führen.

Artikel 2: Modellieren von GPS-Tracks mittels räumlicher Agenten und Erstellen von Daten zur Kalibrierung des Modells In diesem Artikel wird eine Methode vorgestellt, wie einzelne GPS-Tracks mit Hilfe eines agenten-basierten Modells simuliert werden können. Ferner werden aus dem Orientierungsverhalten von Radfahrern Parameterwerte für das agenten-basierte Modell CopenhagenABM abgeleitet. Der Kern des Modells besteht in der Kantenwahlmatrix, die sowohl lokale als auch einen globalen Parameter enthält. Die aus einer Analyse von GPS Daten abgeleiteten lokalen Parameter beschreiben die gewichteten subjektiven Wahlpräferenzen der unmittelbaren Umgebung des Wahlpunktes, wie etwa

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den Anteil von Grünflächen, das Vorhandensein von Radfahrinfrastruktur und das Verkehrsmilieu. Als globaler Parameter diente die Abweichung der Richtung zum Ziel. Um die Leistungsfähigkeit des Modells auszuloten, wurde es sowohl mit einer Reihe von Werten für den globalen Parameter ausgeführt als auch gänzlich ohne denselben. Die dabei erzeugten Routen wurden mit den GPS-Tracks verglichen und somit untersucht, inwieweit das Modell in der Lage ist, die Wirklichkeit nachzubilden.

Artikel 3: CopenhagenABM: Routenwahl von Radfahrern

ein

agenten-basiertes

Modell

der

CopenhagenABM, wurde entworfen und implementiert, und in der Folge konnten die Modellergebnisse mit Verkehrszähldaten verglichen werden. Die zentrale Komponente, die Kantenwahlmatrix, wurde mit Hilfe von Parametergewichten aus GPS-Daten für die lokale Komponente und von einem optimalen Wert für die globale Komponente parametrisiert. Diese Matrix wurde in den Agenten implementiert und deren räumliches Verhalten auf die Ebene der Stadt Kopenhagen hochgerechnet. Die Implementierung des Modells erfolgte in Repast, einem Toolkit zur Erstellung von agentenbasierten Modellen. Der Straßengraph wurde aus OpenStreetMap gewonnen und mit Daten des Straßenmilieus, Umweltdaten etc. angereichert. Die Modellergebnisse wurden mit Zähldaten der Stadt Kopenhagen verglichen und damit konnte die Güte des Modells bestimmt werden.

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Acknowledgement Many people have helped me finish this PhD thesis. First of all, I would like to thank my supervisors, Hans Skov-Petersen, Thomas Sick Nielsen, Nico van de Weghe and Tijs Neutens for good discussions, brainstorming sessions, encouraging criticism and repeated willingness to read my draft papers again and again. The same applies to my co-authors Ann Vanclooster, Bernhard Barkow and Godwin Yeboah.

Special thanks go to the CartoGIS group at Ghent University, Belgium for hosting me for several months.

I would also like to thank Åse Boss Henrichsen at the City of Copenhagen for letting me use the City of Copenhagen's traffic counting data.

A big thank you to all my colleagues at the Department of Geosciences and Natural Resource Management for good companionship, especially Prof. Jette Bredahl Jacobsen for her help with the outcomes of the choice experiments.

Many thanks go to Hans De Four, my landlord in Ghent and my other friends in that city for reminding me of the existence of the world outside the PhD. I would also like to thank Sune Wøller for helping me with the Danish résumé and Assistant Professor Jasper Schipperijn, University of Southern Denmark, for giving me feedback on the improved version of the introduction to the dissertation.

Very special thanks to my parents; to my father for discussions on traffic modelling methodologies and my mother for proofreading.

Finally, I want to thank Chunli, Thomas Máni and Tobias Flóki for having to cope with a partner and father who was occasionally a little unfocussed.

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Contents

1

Abstract .............................................................................................. 7

2

Resumé .............................................................................................. 9

3

Zusammenfassung.......................................................................... 11

4

Contents ........................................................................................... 15

5

Introduction ..................................................................................... 17 The thesis within a wider scope ................................................................... 17

5.1 5.2 5.3 5.4 5.5

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6.1 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.3.6

State-of-the-art: Cyclists' experience mapping and agent-based modelling of cyclists' wayfinding behaviour .............................................17 Knowledge gaps – experience mapping and agent-based modelling of cyclists' wayfinding behaviour ................................................................. 18 Objectives .................................................................................................... 18 Overview of the dissertation ......................................................................... 20

Background ..................................................................................... 23 Experience mapping ....................................................................................23 Human wayfinding........................................................................................25 Agent-based modelling ................................................................................30 Why an agent-based model in this particular context? ................................30 Agent-based models ....................................................................................30 Applications of agent-based models ............................................................32 Agents ..........................................................................................................33 Environments ...............................................................................................33

The Overview, Design concepts and Details protocol (ODD) – a way to describe and standardise agent-based models ....................................... 34 6.3.7 Model implementation – choosing a modelling system ................................38 6.3.8 Factors for choosing a model toolkit for the development of CopenhagenABM ....................................................................................40 6.3.9 Results of agent-based modelling systems .................................................41 6.3.10 Conclusion ................................................................................................... 41

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Methods............................................................................................ 43 Overview and introduction ............................................................................43 Experience data collection and experience mapping ................................... 44 Model construction .......................................................................................45 Calculating the local decision weights of the BECM .................................... 45 Simulation of GPS tracks and model calibration value harvesting ...............45 Calibration of the BECM ...............................................................................46 Full-scale simulation.....................................................................................46 Validation against real world counting data ..................................................46

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Summary of Results ........................................................................ 47 Paper 1: Mapping bicyclists' experiences in Copenhagen ...........................47

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8.1 8.2 8.3 8.4 8.5

Paper 2: Modelling cyclists' GPS trajectories with spatial agents and model calibration data creation................................................................49 Paper 3: CopenhagenABM: an agent-based model of cyclists' trajectories and flow.................................................................................49 Novel method paper 1: Geospatial testing ...................................................50 Novel method paper 2: Model results ..........................................................50

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9.1 9.2 9.3 9.4 9.4.1 9.4.2 9.4.3

Discussion and future research .................................................... 53 How do the results compare to the results of others .................................... 53 Discussion of the results ..............................................................................55 Strengths and weaknesses of the methods .................................................56 Future developments ...................................................................................57 Experience mapping ....................................................................................58 Future development of the agent-based model ...........................................58

9.4.4 9.4.5 9.4.6

Combining the quantitative experience mapping method with an agent-based model ..................................................................................59 Modelling experiences .................................................................................60 Different preferences, agent types and behaviours ..................................... 61 The agent-based model as a scenario tool ..................................................62

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Conclusion ....................................................................................... 63

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References ....................................................................................... 65

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Table of Figures .............................................................................. 79

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Appendix .......................................................................................... 81

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5

Introduction

In this introductory section, I present the wider context in which this dissertation is set. Then, I give an overview of the state-of-the art of the knowledge in the specific fields of experience mapping, wayfinding and agentbased modelling, and the knowledge gaps I try to fill. The objectives are presented and explained briefly. Finally, an overview of the dissertation is provided.

5.1

The thesis within a wider scope

As cities are gradually becoming more and more congested (Fuchs 1994) and polluted by motorised individual traffic (Glaeser 1998; Jakubiak-Lasocka et al.

2014; Downs 1992), policy makers need to address problems like air pollution (Jakubiak-Lasocka et al. 2014), sprawl (Zhao 2010), issues of increasing greenhouse gases (European Environmental Agency 2014) and accelerating issues regarding public health (i.e. obesity and cardiovascular diseases etc.)

(Lopez & Hynes 2006). Alternative transport solutions could contribute to solving these problems (Transportation for a Livable City 2002). Cycling with its triple benefit of reducing congestion, improving public health (Gatersleben & Uzzell 2007; Krizek 2006) and reducing greenhouse gas emissions is seen as a remedy to these problems (Lee & Moudon 2008).

Models of cyclists' travel could contribute to achieving an understanding of urban utilitarian cycling (Pucher et al. 2011). Developing and applying these models makes it possible to experiment with different scenarios (Yeboah et al.

2015).

Many city administrations have already realised and acknowledged the benefits of cycling for cities (Pucher et al. 2011; Jagielska 2012), but need data to underpin their plans in order to allocate and distribute financial resources (Rietveld & Daniel 2004). A scientifically grounded and calibrated model of cycling is, therefore, desirable. Focussing on supporting good experiences (Stefansdottir 2014a; Stefansdottir 2014b; Eisenman et al. 2009) regarding people’s daily cycle trips might convince motorists to use the cycle instead to commute and for leisure activities. Becoming healthy (Dill 2009; Saarloos et al. 2009; de Geus et al. 2007; Copenhagen 2014) by cycling on uncongested infrastructure and saving time are some of the arguments that may persuade motorists to change to alternative healthy transport modes.

5.2

State-of-the-art: Cyclists' experience mapping and agentbased modelling of cyclists' wayfinding behaviour

Today mappings of experiences are widely applied (Treib 1980). Some of them are simple with respondents being asked to draw on paper maps, which are then digitised to map-enabled questionnaires (Fessele & Poplin 2011).

Others store them in Geographic Information Systems for further geospatial

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analysis (Olafsson 2012; Caspersen & Olafsson 2009; Dodge & Perkins 2009).

Collecting and analysing experiential data via geo-located web 2.0 services such as Twitter or Instagram (White & Roth 2010; Batty et al. 2010; Andrienko et al. 2010) facilitates the targeting of a greater group of respondents connected with lesser costs, but at the same time it demands the greater filtering of content. Taking a top down-approach rather than looking into few respondents like in this study lets us represent places from the top down.

SoftGIS (Rantanen & Kahila 2009; Kahila & Kyttä 2009; Kahila & Kyttä 2006), i.e. the web-based mapping of experiences into a Geographic Information System (GIS) could be the basis of decision-making in democratic planning processes.

Agent-based modelling (ABM) is a versatile modelling paradigm when it comes to modelling human spatial behaviour in addition to diverse geodata.

Geospatially explicit agent-based models are well-established, especially within urban computing. Models of cycling, though, are not widespread yet.

Groeneveld et al. (2011) modelled cyclists accessing train stations, while Yang et al. (2013) created a model of children's active travel stressing cycling in relation to a series of changing factors and policies.

Rybarczyk et al. (2010; 2014) built a model with a binary logit mode selection and agents that operate on space-syntactical (Hillier 1996) measures.

5.3

Knowledge gaps – experience mapping and agent-based modelling of cyclists' wayfinding behaviour

In this dissertation, the impact of physical features of the city on the likelihood of having good or bad experiences is of great interest.

Administrative entities within the field of planning such as municipalities and regional governments have recently taken an interest in evidence-based knowledge regarding alternative transport solutions.

Acquiring and analysing measures of the impact on cyclists' experiences could facilitate planning and thereby lead to solutions and policies of a higher quality.

5.4

Objectives

This section presents the research objectives presented in the three main papers of this thesis as well as in the two Novel method papers, which are brief documentations of the novel methods developed through the course of building the agent-based models.

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Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

The overall objectives are: A. to determine the impact of urban features on cyclists' experiences, B. to simulate cyclists' trips by an agent-based model, and finally, C. to model cyclists' wayfinding behaviour on a city level during a day (24 hours).

These objectives are achieved by defining the following research objectives organised in papers and two novel method papers included in this dissertation: Paper 1: Mapping bicyclists' experiences in Copenhagen In Paper 1, the focus is on how data of cyclists' good and bad experiences can be collected using a map-based online questionnaire. Furthermore, the paper analyses the impact of urban factors such as greenness, cycling infrastructure, parking alongside the cycle infrastructure, proximity to commercial units and homes on the probability of having good and bad experiences.

Paper 2: Modelling cyclists' GPS trajectories with spatial agents and model calibration data creation The main research goal in Paper 2 is to build and calibrate an agent-based model, which is able to simulate real-world GPS trajectories of cyclists.

Special focus is put on the development of methods to calibrate the agents' wayfinding core (local factors parameterised by GPS data, global component, parameterised by a calibration parameter harvesting process covered in Paper 2), the selection of optimal parameter weights and finally to compare the modelled tracks with the real ones.

Paper 3: CopenhagenABM: An Agent-based Model of Cyclists' Trajectories and Flow The research goal covered in Paper 3 is to investigate whether an agentbased model of cyclists’ behaviour building on a core component of spatial decision-making can be applied to given traffic zones of a whole city, to traffic loads between these zones, a road network and validated towards real-world traffic counts in several locations of Copenhagen applying the optimal parameter weight for the global parameter from Paper 2.

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Novel method I: Geospatial testing The goal of Novel method I is to investigate whether broadly applied paradigms of software testing could be applied to quality-assure a geographically explicit agent-based model.

Novel method II: Model results Descriptions of the output of agent-based models are widely lacking in the literature. This novel methods paper proposes a data structure in which the results of agent-based models can be stored for further analysis and processing.

5.5

Overview of the dissertation

This section presents an overview of the papers and the novel method papers of this dissertation.

Table 1: The three papers and the two novel method papers contained in this dissertation PAPER 1 PAPER 2

PAPER 3

NOVEL METHOD PAPER 1 NOVEL METHOD PAPER 2

(Snizek et al. 2013) Snizek, Vanclooster, Yeboah, Barkow, Nielsen, Skov-Petersen, and Van de Weghe Manuscript ready for submission.

Snizek, Vanclooster, Yeboah, Barkow, Nielsen, Skov-Petersen, and Van de Weghe Manuscript ready for submission.

Snizek Manuscript.

Mapping Bicyclists' Experiences in Copenhagen Modelling Cyclists' GPS Trajectories with Spatial Agents and Model Calibration Data Creation

Snizek Manuscript.

Model Results

CopenhagenABM: An Agent-based Model of Cyclists' Trajectories and Flow Geospatial Testing

Paper 1 deals with the development of a method for collecting experiential data via an internet-based questionnaire, and calculating the impacts that the physical features of the city as well as route-bound characteristics have on good and bad experiences (Snizek et al. 2013).

The remaining two papers elaborate on methods of building, calibrating and validating CopenhagenABM, an agent-based model of cycling transport behaviour.

Paper 2 presents a method of letting single agents re-walk all origindestination pairs of the GPS-tracks collected by Harder et al. (2011). A wayfinding core was designed, i.e. a decision component that estimates values drawn from the outgoing road segments weighted with parameter weights at nodes of the road network. Those values were later calculated from GPS-tracks collected by cyclists in Copenhagen (Skov-Petersen et al. 2012).

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Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

The final parameter weight needed for the full-scale simulation laid out in Paper 3 was found by systematically changing the global parameter of the central behavioural model and measuring the overlap of real-world versus modelled tracks.

Paper 3 describes a model which describes a whole day of cycle traffic within the city of Copenhagen on top of a road network from OpenStreetMap (Haklay & Weber 2008), counting data from the Danish Traffic Survey (Christiansen 2012), commercial and housing units. The behaviour described in Paper 2 is used in a model with the spatial limits of the city of Copenhagen and the temporal extent of a full day (24 hours).

Data Collection

Model Construction

Model Calibration

Model Run

Model Verification

Result Interpretation

PAPER 1

PAPER 3, NMP 1 NMP 2

PAPER 2 PAPER 3

PAPER 3 PAPER 2

PAPER 3 NMP 1

PAPER 3

Figure 1: The building blocks of the agent-based model and the respective papers of this dissertation (NMP … New Method Paper)

The documentation and discussion of the novel methods – testing and storing results - developed while modelling are added as Novel method papers in the appendix to this dissertation. Transferring testing concepts from the realm of software development to computer-generated geographic results is a novelty and is presented in Novel method paper 1. A framework for storing the results of agent-based models presents an opportunity to further analyse data stemming from agent-based models in Novel method paper 2. Both methods were developed in order to ensure the quality of the agent-based model.

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6

Background

Papers 1, 2 and 3 build on concepts of: a. Experience mapping b. Wayfinding c. Agent-based modelling This section lays out the fundamentals and the background of the topics.

6.1

Experience mapping

In this section, the foundations and prerequisites for Paper 1 (Snizek et al.

2013) are presented.

Experience mapping consists of the acquisition and mapping of information concerning people's good or bad experiences, while they reside in or move through space (Treib 1980).

In 1959, Lynch and Rivkin (1959) claimed to be the first to have measured humans' reactions while walking through the city. In Boston, they performed guided walks and let their subjects comment on their experiences and perceptions after having returned.

Technologically advanced methods have been developed in recent years in order to acquire data on outdoor experiences (Olafsson 2012). GIS-based methodologies have been established to map spatial experiences and psychological responses to the environment.

Today, thanks to the growing democratisation of data, anyone with basic technological insight can obtain data from the Internet including experience data, i.e. recordings of subjects' experiences. A good example of the accumulation of data on features like landscape beauty onto a road network is RouteYou (Anon 2006), a Belgian company which collects the "nicest or most interesting roads to travel" (RouteYou 2015). The trajectories of these trips are made available on their site and end-users are then responsible for improving the routes and adding pictures.

Several steps have been taken to map experiences and emotional reactions towards the environment while walking the city. Initially, paper maps were used to track respondents' experiences (Lynch & Rivkin 1959). However, the methods briefly presented here all rely on the use of GPS devices, which can track continually while the respondent is moving.

Nold (2009b) designed and built an electronic device consisting of a standard GPS unit and a sensor measuring the Galvanic Skin Conductance (Zeile et al.

2009). He then sent respondents on city walks in, for example, San Francisco

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(Nold & Gallery 2007) and Greenwich (Nold 2005), constantly measuring their skin conductance. After having collected these data, Nold used them to produce his Emotion Maps, i.e. maps where emotions were mapped where they happened; both based on Google Earth1 and printed paper maps.

Figure 2: Two kinds of Emotion Maps; on the left, an image of the paper-based, emotion map of Stockport, England; on the right, an Emotion Map built on Google Maps, which shows the emotions of different respondents' trajectories. Both images are courtesy of Christian Nold.

Leahu and Schwenk (2008) tracked their subjects' subjective experiences and contributed to what they call psychogeographies during a walking time of 30 minutes. The results, similar to Nold's, were printed immediately after the walks and thereby served as the basis for discussion of place and experience in a collaborative process.

These attempts to map human experiences more or less directly in a Geographic Information System and the growing need to include their results in policy-making has led to the creation of several online products. One of these, Mapita (2015), a spin-off of Aalto University in Finland, lets their customers add geographic components to online questionnaires. Their product can contribute to SoftGIS, the interdisciplinary approach for supporting planning processes and decision-making where local knowledge is collected by means of technology (Rantanen & Kahila 2009; Kytt채 et al. 2012; Kahila & Kytt채 2009; Kahila & Kytt채 2006).

Within cycle-related research and with experience mapping as an outset, special interest has been put on investigations of the level of service of roadway segments for non-motorised transport, i.e. what quality the road offers the bicyclists, which has an impact on experience. The Danish Road Directorate commissioned a study (Jensen 2008) in which roadway sections were filmed from a bike. This footage was shown to subjects in combination with a questionnaire where the degree of user satisfaction was established.

The type and width of the cycle infrastructure proved to have the biggest positive influence on user satisfaction.

In Paper 1, I have chosen to develop a web and map-based questionnaire where subjects could mark three good and three bad experiences and draw 1

Google Earth is a virtual globe combining diverse data sources (Stefanakis

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& Patroumpas 2008)

Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

the route they cycled. The collected geo-explicit data was statistically related to features of the city and the trip thereby indicating how these city features contribute to a good or bad experience.

6.2

Human wayfinding

The agent-based model of cyclists' behaviour presented in Paper 2 and 3 relies on wayfinding. Therefore, in this section, I discuss concepts and the analysis and modelling of wayfinding, which contributes to the understanding of the Behavioural Edge Choice Matrix (BECM), which lies at the core of the agent-based model described and discussed in Paper 2 and Paper 3.

The term wayfinding was originally coined by Kevin Lynch (1960), originating from his work on how people perceive cities. Apart from developing a methodology for categorising the city into paths, edges, districts, nodes and landmarks, Imageability, i.e. the ability to form a mental image (Montgomery 1998) and Legibility, i.e. the ability to understand a spatial setting wayfinding were part of his theoretical framework.

30 years later, Reginald Golledge (Golledge 1999, p.6) defined wayfinding as: "[...] the process of determining and following a path or route between an origin and a destination. It is a purposive, directed, and motivated activity. It may be observed as a trace of sensorimotor actions through an environment. The trace is called the route. The route results from implementing a travel plan, which is an a priori activity that defines the sequence of segments and turn angles that comprise the path to be followed. The travel plan encapsulates the chosen strategy for path selection."

Wiener et al. (2009) provide a taxonomy of wayfinding strategies shown in Figure 5 below. According to Montello (2001) and Montello and Sas (2006), Wayfinding and Locomotion are parts of Navigation in which locomotion is a strategy for dealing with the immediate surroundings such as avoiding local obstacles, recognising surfaces to move on and taking note of landmarks. Wayfinding is the spatial behaviour in relation to distant, global goals including planning and (spatial) decision-making.

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Figure 3: Taxonomy of wayfinding tasks. Taken from (Wiener et al. 2009)

According to Wiener et al. (2009), wayfinding can be divided into aided wayfinding and unaided wayfinding. Aided wayfinding is supported by signage, navigation assistants such as pilots in naval navigation or technological assistance such as GPS. Unassisted wayfinding relies only on the spatial knowledge and the immediate decisions of the person navigating.

In Wiener's taxonomy, unaided wayfinding can be divided further into undirected and directed wayfinding. The former reveals roaming behaviour such as picking berries, exploration, cruising and pleasure walking.

Wayfinding relies on spatial reasoning (Freksa 1992; Frank 2007; Cohn 1995; Wallgrün & Dylla 2010). Different fields of human2 wayfinding have emerged: walking behaviour both indoors (Hölscher et al. 2012; Hölscher et al. 2005) and outdoors (McArdle et al. 2013; Morse Blivice 1974; Van der Hoeven & Van der Spek 2008; Hölscher et al. 2005; Meilinger 2007), transport modelling including driving (Hensher & Button 2007), public transport (Raney et al.

2003; Davidsson et al. 2005) and cycling (Menghini et al. 2003).

The number of navigation strategies presented here is limited and only includes those in which the humans act directly in response to their immediate surroundings. A significant part of the specific literature deals with mental maps (Lynch 1960) and their representation in digital structures. Descriptions and discussions of complex mental maps, e.g. the anchor-point theory (Couclelis et al. 1987) can be found elsewhere (Pilgrim 2007; Arentze & Timmermans 2005). Understanding wayfinding is especially interesting and highly relevant in practice when connected with evacuation in evacuation models (Veeraswamy et al. 2009; Troffa & Nenci 2009; Ronchi et al. 2015)

2

Wayfinding amongst animals and the body of literature dealing with this is excluded from my analysis in this dissertation.

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Heuten et al. (2008) suggest that wayfinding might even rely on stimuli other than visual such as tactile and olfactory. Nevertheless, I only focus on wayfinding as a result of visual sensing in the context of this dissertation. The agents' memory just records their trace through the city – it is not used for spatial decisions.

According to Golledge (1995), the choice of destination and route selection are the two pillars of wayfinding. By way of an overview of the different strategies, which have been mentioned in the literature and tested in lab environments, I have identified eight widely used strategies, which are discussed below: a. First noticed path The first noticed path is the one a subject immediately sees in a situation requiring a decision. According to Golledge (1995), the first noticed path strategy ranks fifth among the ten strategies he compared. Khanan & Xia (2010) found that females are more likely to use this strategy. According to Xia et al. (2009), older respondents are also more willing to use this strategy. b. Fewest turns In the fewest turn strategy, a subject tends to minimise the number of turns (Golledge 1995). In the cycling literature, a special case of this theory is propagated: cyclists are believed to avoid left turns because left-turning is connected with a higher risk of accidents (Broach et al. 2012) (This only applies, of course, to countries where one drives on the right-hand side of the road). c. Shortest leg first The shortest leg first strategy, i.e. the selection of the shortest outgoing edge, ranks worst in Golledge's comparison of strategies (Golledge 1995). d. Least angle strategy Rooted in a desktop experiment in which respondents were asked to make edge selection decisions based on a distant visible destination, Hochmair & Karlsson (2005) presented the least angle strategy. When applying this strategy, the subject always selects the most direct edge towards the destination, i.e. the edge with a minimum angle between the outgoing edge and the direct line towards the destination. In the specific literature, the least angle strategy is often connected with costs of turns (Winter 2002). Hochmair (2005) investigated this strategy in unknown street networks. Hillier and Ida (2005) investigate correlations between pedestrian flows and different navigation strategies: least length, fewest

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turns and least angles in four areas in London. The least angle strategy scores in all the areas the highest R-square values for pedestrians. A slightly different result is found for motorists, but in 11 of 16 areas (both vehicular and pedestrian movement) the least angle strategy has the best results. e. Initial segment strategy The initial segment strategy (Hochmair & Karlsson 2005) is one where the chosen direction is as straight as possible compared to that of the last selected edge. Strategies based on these heuristics result in the selection of different routes for different directions of the origin-destination pair. f. Straightest route selection This strategy is also called the fewest turns strategy (Jiang & Liu 2011). In an experiment, Conroy-Dalton (2003) followed a number of participants through a virtual world and revealed that they were, "choosing the straightest possible routes as opposed to the more meandering routes" (2003). Conroy-Dalton noted that these results matched those of Hillier (1997) and the space syntax community and suggests that people's movements are actually composed of a series of micro-scale decisions, which follow the straightest line.

According to this theory, an agent would need to have a global wayfinding component similar to those in CopenhagenABM, mainly to set it off at the beginning of the journey. On the way, the agent then wants to take the angle with the minimum deviation between the outgoing edge and the line from the current position towards the destination. In their experiment of modelling the fewest turns strategy, Manley et al. (2011) concluded that about 75% of their results represented better models than the shortest path strategy for the same origin-destination pairs. g. Longest line of sight According to Conroy-Dalton & Bafna (2003) citing Hillier (1997), the longest line of sight is the feature that the respondents chose within a virtual world to select the next edge to travel upon. The core representation for space syntactic analysis (Hillier 1996) is the line of sight of the axial map which includes the longest and fewest lines. The path from an origin to a destination within a road graph with fewer, but longer lines is easier to navigate than one with many short links. In an experiment including eye tracker equipment, Emo (2014) proved the importance of the longest line of sight: 74% of eye tracker fixations are made on the longest line of sight and its surroundings.

In an agent-based model, this easily implementable angular decision rule may be supplemented by a notion of directionality towards the destination.

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h. Complex edge and route attributes and route choice models The wayfinding strategies touched upon above rely on different simple geometrical features of the outgoing edges such as length, direction as well as the route in total. However, the literature points to the fact that other properties of the edges travelled on also play a role in wayfinding.

Rybarczyk et al. (2010) built an agent-based model where cyclists operate on a series of space syntax measures (Bafna 2003). Penn & Turner (2002) let agents walk in a virtual mall without a global destination. Their spacesyntax based model is, therefore, only a model of roaming or pleasure walking and not path searching.

Route choice models consist of a transportation network link, path attributes and origin-destination pairs. These models can have different objectives, such as minimising travel time and optimising towards personal preferences. Models trying to fulfil the former optimise towards minimum travel time and thereby shortest path, the latter takes temporal trade-offs into consideration in order to be optimal in other belongings such as reducing exposure to noise or minimising dangers. Online routing mapping engines such as Google Maps use, for example, the route, which is best to cycle on, the greenest or the quietest. The simplest way of taking spatial choices is the well-defined shortest path (Dijkstra 1959). Being behaviourally unrealistic (Dill 2009), different approaches were taken resulting in a significant volume of approaches to model route choices.

Freijinger (2008) presents an overview of route choice models. Discussing these approaches here would go beyond the scope of this dissertation.

Nevertheless, I present an example from the area of modelling of cycling.

Broach et al. (2012) extracted a series of variables based on revealed preference GPS data stemming from cyclists: road features, intersection design, traffic volume, distance and signalling. Also based on GPS data, Menghini et al. (2010) built a cyclists' route choice model with route length, average and maximum gradient, percentage of marked cycle paths, number of traffic lights and path size as variables. Their model suggests that the length of the trip has the greatest influence on the cyclists' choices followed by the presence of cycling infrastructure and the gradient.

In CopehagenABM, wayfinding was implemented by a component comprising local parameter weights representing the outgoing edges from any decision point and a global one providing the agents with a notion of the general direction of the goal of their journey.

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6.3

Agent-based modelling

In this section, I first give reasons for why an agent-based model was chosen in the current context and then discuss agent-based modelling.

6.3.1 Why an agent-based model in this particular context?

The agent-based modelling paradigm used to develop CopenhagenABM was chosen to satisfy a series of needs. The nature of the data stemming from the analysis of the GPS data available enabled the construction of a behavioural component that can facilitate step by step navigation, opposed to the production of cost surfaces (de Dios Ortuzar & Willumsen 1994) as applied in traditional transport models. The probabilistic nature of the decision processes (from a limited number of outgoing edge alternatives, one is selected randomly) reflects a heterogeneous population better than other modelling approaches. The capacity to visualise the movement of each single agent on the screen, as well as being able to reproduce a whole journey with all decision situations, helps to understand movement and facilitates the removal of bugs. The potential to show the running model to an audience of laymen, to zoom in on certain junctions, streets or areas is of great value when trying to convey the effects of new, democratic planning initiatives. Finally, the organisation of the agent-based model into objects of the physical city like roads, traffic zones, environmental factors and those of the traffic (road) environment, the simple behavioural core of the agents can make modelling more accessible than equation-based models, which need a greater insight into mathematics.

6.3.2 Agent-based models Agent-based models (ABM) are a type of computational model for simulating complex and often spatial or even geospatial tasks by means of agents: autonomous, active, re-, inter-, and proactive, heterogeneous and mobile entities. According to Batty (2012), ABM belongs to the six styles of spatial models, which are: (1) land-use transportation interaction models; (2) cellular automata; (3) agent-based models; (4) spatial econometric models; (5) systems dynamic models, and; (6) micro-simulation models.

In the remainder of this section, the preconditions necessary for building an agent-based model are presented; an overview of the development within the field of agent-based modelling is provided, and the fields in which agentbased models have been used are identified. Then, the components of agentbased models as well as their principles are explained. Agent-based modelling toolkits provide a series of basic building blocks to speed up development. I present a few popular toolkits and then arguments for the selection of a specific platform to be used for developing, implementing and running models.

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Finally, I explain why the agent-based modelling technique has been chosen to solve the challenges of modelling cycle transport.

Rather than investigating phenomena in the real world, the modeller can study a computer-generated population within a computational (agent-based) model. Such models, or populations, are called in silico (Bonabeau 2002), i.e. populations thriving within a computer (programme) opposed to models in vivo (the real world) or in vitro (in a test tube). Computational simulations or simulation models are implementations of a model.

Modelling is an approach which comprises a research question formulation, data collection, model design and building, the collection of model parameter estimates, the calibration of model runs, the analysis of model results and validation towards real world measurements and finally visualisation and further statistical and numerical processing.

Certain steps are common to any kind of modelling including abstraction, reduction and simplification. In the current case, the geographical features in particular constitute abstraction. Another particular example of abstraction of the spatial decision making process is the implementation of the cyclist agents at the junctions of the road network as in the Behavioural Edge Choice Matrix of CopenhagenABM. Model validation is an important step, which is performed in order to ensure the quality of the model, and to be able to state the degree to which the model can actually explain the phenomenon studied. Validation also includes the predictability of future behaviours and observations, an explanation of past observations, which were not part of the original model building - in this context, the number of agents travelling on a segment of the road network is compared to real-world counts.

Van Dyke Parunak et al (1998) make a distinction between agent-based modelling (ABM) and equation-based modelling (EBM). With agent-based modelling, as elaborated below, the model relies on the behaviours contained in agents and the system emulates these behaviours on execution. Equation based models are comprised of equations that are evaluated on execution of the model. According to Van Dyke Parunak et al (1998, p.22), ABMs are easier to construct. They make it easier to, "distinguish physical space from interaction space" and can be, "validated on an individual level", "support direct experimentation" and, "are easier to translate back into practice".

Geospatial models and simulations are those containing geographic components, i.e. representations of Cartesian space. Their elements hold locational data. The syllable geo refers to a location on the surface of the earth (Wikipedia 2015).

Agent-based models are especially interesting as they can reflect heterogeneity and capture emergent phenomena with simple means, while

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they can be described easily following standardised protocols (Polhill et al.

2008). Freely available tool kits make it possible for people with limited coding experience to build complex models.

6.3.3 Applications of agent-based models Applications of agent-based modelling can be found in many different fields.

One can make a clear distinction between models without geography and those including geographical information, i.e. as part of geospatial simulations.

Among the non-geographic models are those within economic modelling such as market simulations (North et al. 2002). Another application is to be found in business communication and monetary policy. Within business marketing, the analysis and modelling of decision making is of particular interest (Forkmann et al. 2012). Leifield (2014) has built and discussed a model of political discourse. Within energy distribution networks, electricity is frequently modelled also using agent-based models such as in (Tarhuni et al. 2015) or (Rahman et al. 2015).

Agent-based models in diverse fields can be found among models containing geospatially explicit components. Indoor navigation and people tracking is also a field in which agent-based models exist (Frank et al. 2001; Shi et al.

2009; Marchesotti et al. 2003; QuanLi et al. 2015). QuanLi et al. (2015) describe land cover changes; Mei et al. (2015) city-wide disease spreading.

Crowd behaviour and crowd dynamics are fields with diverse use of agentbased models (Zhou et al. 2015; Helbing & Johansson 2009; Crociani et al.

2013; Moulin et al. 2003). Evacuation models belong to this model category (Yamamoto 2013; Almeida et al. 2013; Song et al. 2013; Ronchi et al. 2015; Tan et al. 2014; Shi et al. 2009) and are part of the planning process for railway and subway stations in order to minimise casualties in the event of accidents, and simulating the effects of terror attacks or other disasters.

Generally, crowd control has also been dealt with (Batty et al. 2003). Torrens et al. (2013) developed a model in order to analyse crowd build up during riots. Urban resource management models often use the ABM paradigm such as parking management systems (Levy et al. 2015). Finally, route choice and travel demand questions can be modelled by applying agent-based models as discussed in (Zhu et al. 2007) and (Raney & Nagel 2003).

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6.3.4 Agents At the core of an agent-based model is the autonomous agent. It is a distinct object (e.g. a person on a bike), which is characterised by variables (i.e. speed, grade of fatigue) and states (i.e. cycling, resting). Agents can be hierarchically grouped, e.g. into humans and animals. Humans can be grouped into, e.g. cyclists and pedestrians; animals into different species, e.g. foxes and badgers. In geographically explicit models, entities are bound to geographical coordinates and can relate topologically to other moving or stationary entities (e.g. part of a flock, in a city, on a road). Apart from mobile agents, an ABM can contain other, stable entities such as roads, zones, addresses, counters, etc. Agents can be programmed with behavioural rule sets that can be triggered by different events, e.g. breaking at the right time to stop at a traffic light or fleeing when frightened if the agent is an animal.

Agents contribute to emergence, i.e. a greater, overall pattern on the scale of the model, which would not otherwise have been predicted. Agents can be adaptive (Berry et al. 2002); they can choose different strategies at different times of their lifetime or adjust speed and other behaviour to the underlying landscape or traffic situations. Agents can apply different strategies to pursue an objective, i.e. reaching a destination (coordinate) in a road network. Agents can learn (Alonso et al. 2001), they can internalise the whole or parts of a road network, which enables them to reach their destination more easily in a later model run and thereby perform wayfinding. Agents can combine different stimuli, e.g. vision (Kennedy 2012). Agents can interact (Demazeau 1995) as already mentioned above. They can flee (Tan et al. 2014) or they can exchange knowledge (Wang et al. 2009) such as routing directions. Agentbased models are characterised by having a stochastic component in order to generate variability in decisions and populations, while they can rely on decision trees processed at decision situations. Collectives (Epstein 2002) can be built by agents of the same kind, e.g. flocks of birds or groups of hikers. Agent-based models are stackable: they can contain sub-models, e.g. a junction can be modelled with a higher resolution than other geographic parts of the model.

6.3.5 Environments Within spatially explicit agent-based models, environments are the spatial backbone on which agents operate. According to Gilbert (2008), environments can be the basis of direct or indirect communication among agents. In geospatial micro simulations, environments reflect geographic features such as places, linear structures such as roads or paths and polygonal structures such as areas of cities, parks or water bodies. Agents can be restricted so that they are contained by these structures. They can move along a road as cyclists, jump between them as hikers who enter a forest in pursuit of mushrooms or walk towards, e.g. mountain huts or office locations.

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Environments can account and be queried for agent activity such as the number of agents residing in one place at the same time. They can change when touched by agents. Representations of natural settings can change and, e.g. deteriorate on use.

6.3.6 The Overview, Design concepts and Details protocol (ODD) – a way to describe and standardise agent-based models All features and components of the agent-based model presented above were described in the ODD protocol on (Grimm et al. 2006), the purpose of which is twofold. On the one hand, it is thought to help modellers who do not have much experience achieve a structured approach when designing and organising their model. On the other hand, and as a de-facto standard it can be used to describe an agent-based model in a standardised way, thereby forcing the modeller to think in a structured way and making a comparison between models possible. Another aspect which is important to mention is the ability to support the communication of models (Grimm & Railsback 2011).

This section briefly introduces the basic concepts and keywords of the ODD standard. Table 2 below shows its simple, overall structure.

Table 2: The basic pillars of the ODD protocol as described in (Grimm et al. 2006)

OVERVIEW DESIGN CONCEPTS

DETAILS

Purpose State Variables and Scales Process Overview and Scheduling Basic principles Emergence Adaption Objectives Learning Prediction Sensing Interaction Stochasticity Collectives Observation Initialisations Submodels

The following short presentation of the ODD protocol is based on (Grimm et al. 2006).

1 Overview The overview section combines information on the purpose, state variables and scales and the process overview with scheduling

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1.1 Purpose Explaining the purpose of a model is the most basic task to be fulfilled when beginning to conceive an agent-based model. The purpose specifies as concisely as possible which real world phenomenon the model describes and in which context it is set. At the same time, it justifies the existence of the model and the resources to be used in the model building and validation tasks.

1.2 State Variables and Scales State variables are attributes of the basic entities of the model. They can describe, e.g. static attributes, the gender of the agents, the colour of birds and as well dynamic attributes such as speed, degree of hunger or mood. Scales refer to the length of the time step of the scheduling component as well as the grid size in raster-based spatially explicit models and the geographic extent of the spatially explicit models.

1.3 Process Overview and Scheduling All agent-based models should contain processes both environmental and individual. Each agent-based model has a scheduler of some kind, which launches processes with a certain temporal resolution. These processes are bound to the result of the scheduler which some call tick. Visualisations in the form of flow charts can be helpful to understand processes. Sequence diagrams such as those we know from the UML standard (Pilone 2003) with columns representing the different entities included in a process may be suitable for conveying the message.

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Figure 4: Sequence Diagram taken from (Bersini 2012). The model's entities (world, agent, site1, sites, site2) are shown on the abscissa; the time is represented on the ordinate downwards; the arrows show interactions between entities

2 Design Concepts Design concepts contain a series of the principles of agent-based models.

2.1 Basic Principles The Basic principles section of the ODD contains information about hypotheses, whether new ABM theories are introduced and the purpose of the study.

2.2 Emergence Emergence is a model's result on a higher level. This section presents the way emergence is expected and which rules would result in which results.

2.3 Adaptation Adaptation deals with the way the agents respond to the environment or other agents and which rules are responsible for adaptive behaviour.

2.4 Objectives What are the overall objectives and goals the model is supposed to achieve by introducing rules for adaptation?

2.5 Learning

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When agents are able to learn, they can store successful adaptive rules for later use in similar situations.

2.6 Prediction If the agents can evaluate the results and impacts of their decisions, they can predict the future.

2.7 Sensing Agents can draw information from the environment and other agents by sensing. Certain triggers such as visual cues or sensing through social networks can change state variables, thereby triggering reactions such as fleeing behaviour.

2.8 Interaction Is there any interaction between agents and how does communication occur?

2.9 Stochasticity Most agent-based models contain stochasticity in decision processes, i.e. random behaviour of differing magnitudes, which reflects heterogeneity.

2.10 Collectives Collectives are groups of agents of the same type that share common behaviours and often have internal organisation such as flocks of birds or groups of tourists.

2.11 Observation Observational structures are those that can collect longitudinal information throughout the lifetime of a model.

3 Details The details section comprises two groups: initialisations and submodels.

3.1 Initialisations The Initialisation section of the ODD standard defines how the system variables are to be populated on model start, the input data, i.e. the data sources that are read on model launch and possibly during model run.

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3.2 Submodels As already mentioned above, a model can be split into submodels in order to organise the programme code and combine functionalities into logical units. In addition, developing a model in a team is easier when different people or groups take responsibility for different parts of the model.

6.3.7 Model implementation – choosing a modelling system In order to run agent-based models, software either has to be programmed from scratch, or code from agent-based modelling system kits needs to be used and extended. This software can provide mechanisms to read and write to and from files, a clock that provides a temporal backbone and different kinds of analytical tools such as a display, interfaces for statistical, geographical, and spatiotemporal or numerical analysis.

A question any modeller has to address right at the beginning of a modelling task is - apart from defining the modelling area, the spatial and temporal resolutions – in which modelling system they want to implement the model.

This process involves an investigation into the currently available systems (Crooks et al. 2007) and the amount of work, which will have to be invested in order to achieve one’s goals in relation to one's programming abilities.

Experience with modelling, knowledge of the computer language needed to code as well as the availability of backup from fellow modellers via relay chats and lists on the Internet are also important factors when deciding to go for one of the many options in modelling systems. The ability to switch between ABM toolkits without investing too much work must also be taken into consideration prior to modelling. The demands modelling toolkits make on operating systems as well as hardware requirements must also be considered.

Railsback et al. (2006) review four of the most widespread and successful agent-based modelling simulation platforms: NetLogo, MASON, Repast and Swarm. In the following paragraphs, I introduce two frequently used platforms, both of which can represent Cartesian spaces: NetLogo and Repast. Before finally accounting for the choice of a modelling platform, I briefly introduce these agent-based modelling platforms.

Furthermore, the applicability of the modelling platform in relation to the current modelling task is evaluated. Briefly, the criteria are: • • • • • •

38

Type of geographic representation (vector or raster) Computer language supported Input data readers and formats Output data formats Existing code to reuse Developers' community

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NetLogo

Figure 5: A screenshot of the SprawlEffect (Shoko & Smit 2013) model implemented as part of the Urban Suite in NetLogo. On the left, the buttons and gauges used in order to control the model and change parameters. On the right (in green), the modelling geography with built-up areas represented as blue houses.

NetLogo is an immensely popular modelling system due to its simple programming language with a flat and short learning curve and its large community. At the time of writing, nine books had been published in English (Banos et al. 2015; O'Sullivan & Perry 2013; Damaceanu 2012; Janssen 2012; Railsback & Grimm 2011; Teahan 2010; Wilensky & Rand 2009; Vidal 2010; Gilbert & Troitzsch 2005) and more than 500 papers from the first in 1999 (Wilensky et al. 1999) to the most recent in 2015 (Polhill 2015). A library of about 400 example models from all kinds of domains (art, biology, chemistry, physics, computer science, social sciences, geography, etc.) is directly packaged into the modelling platform and can stimulate the modeller’s intuition as well as provide code snippets to be used in the learning process as well as recycled directly into new code.

It is hard to say how many people actively use NetLogo on a daily basis; the system offers a twitter feed (NetLogo 2014a), an announcement mailing list, a WIKI (NetLogo 2014b), a user group, a developers’ group, an educators’ group and even an Internet Relay Chat. Anyone who is part of the community can upload and share their models on the NetLogo site.

In the current context, NetLogo offers the opportunity to describe road networks with the help of Road Network Builder Extension (The Center for Connected Learning and Computer Based Modeling 2015). Nevertheless, it is not clear whether shapefiles can be important or how many edge attributes can be represented.

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Furthermore, the application of NetLogo requires knowledge of LOGO (Montello 2001; Friendly 1988).

Repast

Figure 6: A screenshot of a model implementation of system dynamics of rabbit populations implemented in Repast, taken from (Argonne National Laboratory 2013).

The Repast Suite is also a popular modelling system, albeit not as popular or widespread as NetLogo. Repast comes in two versions: RepastHPC, a high performance platform for large-scale agent-based modelling (Collier & North 2012), which is a parallel implementation of Repast in C++, an objectorientated programming language.

The other version of Repast, which is publicly available, is Repast Simphony, currently available in version 2.3.13. Repast Simphony heavily builds on top of and depends on GeoTools (Turton 2008), an Open Source JAVA GIS Toolkit currently used by several geospatial open source projects. Repast is, therefore, highly suitable for developing geospatial agent models – the modeller does not have to spend time or energy on (re)building basic functionality like raster and feature readers, network algorithms (shortest paths etc.), spatial transformations, or spatial indexes.

6.3.8 Factors for choosing a model toolkit for the development of CopenhagenABM This section discusses the reasons why Repast was chosen as the basis for CopenhagenABM. At the time when modelling work was about to commence, the representation of roads was easier in Repast than in NetLogo. NetLogo had a network module, but it was not clear whether it was able to hold more than one value per edge. In contrast, Repast's network model built on those of GeoTools, which already then had the required complexity.

3

st

Released on June 1 2015 (Argonne National Laboratory 2015)

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Another reason Repast was chosen was due to the fact that repastCity (Malleson 2011) was publicly available on the Internet. Malleson had already implemented road networks with complex edge attributes based on GeoTools and mechanisms for agent movement. In an initial phase of the project, this software acted as inspiration and the basis for further development. However, the final code no longer relies on that of Malleson's.

Repast supported vast numbers of agents, which was a crucial parameter in the search of a system.

Finally, extending Repast was possible by using JAVA (Wikipedia 2014).

6.3.9 Results of agent-based modelling systems As mentioned above, the incentives for building an agent-based model can be different. Judging from the literature, a greater emphasis is put on the description of the model and the process that led to its genesis than how its results are stored and treated for further analysis.

Novel Model Paper 2 presents a design of logging components the task of which is to store the results of different parts of the models in order to be analysed in subsequent steps of the modelling process.

6.3.10 Conclusion I have now introduced agent-based modelling including a discussion of how models fit within processes of knowledge gathering. A brief explanation as to why an agent-based model was chosen to solve the current task was also given. Finally, I presented my motivations for choosing Repast for building CopenhagenABM. The following section provides an introduction to the methods used to reach the research goals of the respective papers.

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7

Methods

7.1

Overview and introduction

This section briefly presents all the methods included in this dissertation as shown in Table 3, below.

Table 3: Methods in papers

METHOD Experience data collection and experience mapping

PAPER Paper 1: Mapping Cyclists' Experiences in Copenhagen

Model construction Calculation of the local decision weights of the BECM Remodelling of GPS tracks and model calibration value harvesting Validation of the BECM

Paper 2: Modelling Cyclists' GPS Trajectories with Spatial Agents and Model Calibration Data Creation

Full-scale simulation Validation against real world counting data Test case development and application Design of data structures for result storage

Paper 3: CopenhagenABM: An Agent-based Model of Bicyclists' Trajectories and Flow Novel Method Paper 1: Geospatial Testing Novel Method Paper 2: Data Storage

The spatial limits within which the experience data were acquired as well as the location of the model's activity were the municipalities of Copenhagen and Frederiksberg, Denmark, as shown in (Boss Henrichsen 2014) and Figure 7, below.

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Figure 7: Delineation of the study area and the 119 Counting Locations 2010-2012 set up by the City of Copenhagen.

7.2

Experience data collection and experience mapping

Spatial, experiential data were collected using an interactive Google Mapdriven data collection page, which was connected to a survey of cycling behaviour and route choice in Copenhagen (Skov-Petersen et al. 2012). The research subjects were asked to draw their most recently travelled bike trip and mark the location of three good and three bad experiences they had had on this route. The routes and experience points were map-matched to a road network acquired from OpenStreetMap (Haklay & Weber 2008). On every route, points were laid out at a distance of 50 m from each other – representing the experience spaces. Values of a series of neighbourhood metrics such as road type, number of commercial units within a given radius etc. were joined to all points. The points where experiences were reported (experience points) were compared to those where no experience was reported. Thereby the significance of the preconditions for obtaining good or bad experiences was estimated. With this method, any urban space can be assessed according to the potential increase or decrease in good or bad experiences given the same background data as used in the study.

Furthermore, the method could potentially be applied to assess the effect of proposed changes to the urban design in terms of cyclists’ experiences.

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7.3

Model construction

CopenhagenABM, a model of cyclists' behaviour presented and discussed in Paper 1 and Paper 2 was implemented as an agent-based model on top of the freely available modelling toolkit rePAST and in addition to that using portions of code from Malleson's (2010) as well as the rePAST based agentbased model repastCity (Malleson 2011) of criminal behaviour. Apart from the most basic model entity, the agent, other model entities were established such as; (1) a road network; (2) homes; (3) commercial units, and; (4) zones from the National Traffic Survey.

The agents' wayfinding behaviour is controlled by the Behavioural Edge Choice Matrix (BECM) – the central behavioural component of the agent which is activated and evaluated at any decision point, i.e. nodes of the road network. This BECM basically consists of two components: local decision weights and a global directional weight, which ensures that the agent remains oriented towards the destination of their trip.

7.4

Calculating the local decision weights of the BECM

Ten decision variables originating from a dataset generated by Skov-Petersen et al. (2013) were derived. During three weeks in May 2010, 210 individuals were selected to carry a GPS unit on them at all times. From 194 respondents, 108,809 points and 1,291 trips were recorded. The GPS points were then map-matched to the road network and trajectories (polylines) were constructed. Parameters were estimated by analysing the GPS dataset based on the options, i.e. the outgoing edges a person has at decision locations, the results are shown in Table 4, below.

Table 4: Local parameter weights of the BECM and their standard errors

Variable Left turn Right turn Cycle track lane Cycle path Path (pedestrians and bikes) Percentage of green Secondary Road Residential Street Multi-storey housing Housing with shops

7.5

Weight -1.47 1.33 0.64 -0.54 -0.52 0.60 -0.40 0.37 -0.73 -1.02

SE .0135145 .013037 .0235353 .0499011 .0413737 .0293955 .0507316 .0425605 .053345 .0428388

Simulation of GPS tracks and model calibration value harvesting

A method for calibrating the BECM was developed. The ten local decision parameter weights of the BECM were kept constant while the global

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parameter, a value dependent on the bearing towards the trip destination, was alternated. The resulting overlap factor with the GPS tracks, i.e. how much of the modelled track was similar to the GPS track, was recorded and evaluated.

The optimal value for the global parameter was identified and the model was run in a full-scale model run as discussed below. In addition, methods for evaluating where agents are removed from the model because they exceed a given route length fraction were established and evaluated.

7.6

Calibration of the BECM

Two validation criteria were defined for model quality: (1) The success of completing the trip within a length arbitrarily set to 150% of the length of the original route, recorded by GPS. (2) The overlap of those reaching their destination with the GPS trajectory. The weight of the global parameter was set to: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0 and 100.0; the local weights were switched on and off for each of these numbers resulting in 56 model runs.

7.7

Full-scale simulation

The global parameter's value of the BECM was set according to the findings in Paper 2 and agents created in homes and commercial units according to the National Travel Survey (one hour slots). Agent activity was recorded on the level of road network links and was prepared for model verification (below). The generation of agents in static geographic entities such as homes and commercial units in defined temporal windows again reflects standard agent-based modelling methods such as implementing timetables.

7.8

Validation against real world counting data

Agent counting data of the 119 links of the Copenhagen road model were compared to real world counts performed by the City of Copenhagen's traffic counting secretariat. A GEH statistics (Balakrishna et al. 2007), invented in the 1970s by Geoffrey E. Harvers and used to compare two sets of traffic volumes, was chosen to compare the peak hour volumes of simulated traffic to the real ones.

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8

Summary of Results

This section summarises the main findings of the three papers and the two novel method papers.

Table 5: Compact overview of objectives and results PAPER 1

OBJECTIVE Develop a method to collect experiences on the cycle path.

Relate reported experiences to urban features in order to investigate which factors have an impact on which kind of experiences.

2

Build an Agent-based model of cyclists' wayfinding behaviour.

Simulate GPS trajectories.

Develop methods to determine the overlay factor between GPS trips and simulated ones.

Calculate an optimal parameter weight of the global factor of the BECM.

3

Model 24 hours of cycle traffic in Copenhagen (full model) Validate the full model against traffic counting data.

8.1

RESULT A Google Map-based online questionnaire instrument was developed and connected to a bigger survey of subjects' cycling attitude and behaviour.

398 routes and 890 experience points were collected.

The availability of cycling facilities, a high percentage of and shorter distance to green areas and water bodies along the route and a greater distance to bus stops increased the probability of a good experience.

Primary and secondary roads as well as less straight routes reduced the possibility of having good experiences. Proximity to intersections and companies and shops increased the probability of a bad experience, while primary roads, residential streets and the greenness of the cycling environment reduced the probability of having bad experiences.

A model was built with a Behavioural Edge Choice Matrix (BECM), which is responsible for wayfinding in nodes of the road network. The BECM consists of a global component which evaluates the direction towards the destination and a local component composed of a series of parameter weights taken from behavioural analysis of GPR trajectories.

A method was developed in which agents were simulating trajectories given the destinations of GPS trips.

A method was developed whereby the global factor of the BECM was altered and the local factor was switched on and off. The overlap factor between a simulated trip and the GPS trip was calculated.

Agent termination criteria were introduced in order to determine successful routes.

An optimal parameter value of the global factor of the BECM was found by iterating through a series of values and evaluating the overlap factor of the successfully executed trips and the GPS trajectory.

The value for the global factor is set to 20.0.

A model was built with commercial units and homes as spatial entities for origin and destinations and zone data from the National Transport Survey.

Link load data from the model were compared to counting data by using GEH statistics (see below).

The results were not satisfactory (20% of the results under a GEH of 5.0) and point to a need to further develop the model and the data sources it relies on (see section 0 below).

Paper 1: Mapping bicyclists' experiences in Copenhagen

The objectives presented in Paper 1 were the development of a web-based collection method for cycle-related experiences and methods to determine the influence of urban factors on good and bad experiences.

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Experience data obtained from the web-based survey instrument can be mapped (see Figure 8, below) as a basis for assessment and analysis.

Adding geographical data layers to experience points and route segments permits further statistical analysis of the environmental experiences.

Figure 8: Geographic distribution of good (left) and bad (right) spots; the municipalities of Copenhagen and Frederiksberg are shown in grey.

The results of the statistical analysis are fourfold: Features of the city environment can: A. reduce or B. increase the probability of C. good or D. bad experiences.

The availability of cycling facilities, a high percentage of and shorter distance to green areas and water bodies along the route and a greater distance to bus stops increased the probability of a good experience. Primary and secondary roads as well as less straight routes reduced the possibility of having good experiences.

Proximity to intersections and companies and shops increased the probability of a bad experience, while primary roads, residential streets and the

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Snizek: Mapping cyclists' experiences and agent-based modelling of their wayfinding behaviour

greenness of the cycling environment reduced the probability of having bad experiences.

8.2

Paper 2: Modelling cyclists' GPS trajectories with spatial agents and model calibration data creation

Building an agent-based model of cycling behaviour, simulating GPS tracks and calculating optimal parameter weights are the overall goals of Paper 2, which presents the design of CopenhagenABM, an agent-based model to recreate single, GPS-based trajectories and to acquire parameter values. The core of the model, the Behavioural Edge Choice Matrix (BECM), which is responsible for the agent's network edge choice in any node of the road network, was designed and contains both local parameters and a global parameter (GP). For a description of the BECM, see sections 7.3 and 7.4 above.

Simulations were run for all 1,268 GPS tracks recorded by Harder et al.

(2011) for values 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0 and 100.0. As shown in Table 4 above, the local parameter weights ranged between -1.47 and 1.33. Two validation criteria were defined for model quality: (1) the success of completing the modelled GPS trajectory within a length arbitrarily set to 150% (successfully completed routes) of the length of the original route, recorded by GPS, and; (2) the overlap of those reaching the destination with the GPS trajectory.

As suggested in Paper 2, in order to increase the number of successful completions of the tracks, the weight of the global parameter should be set to a value of 20.0, while maintaining the weights of the local parameters at the same levels. This will ensure that as many agents as possible arrive at their destinations, although perhaps not exactly on the same trajectories as their GPS counterparts.

8.3

Paper 3: CopenhagenABM: an agent-based model of cyclists' trajectories and flow

The goal of Paper 3 was to model cycling transport in CopenhagenABM for 24 hours in Copenhagen.

The global parameter was – as recommended in Paper 2 - set to 20.0, the local parameters were included in the simulation with the values retrieved from the analysis of the trajectories of the Copenhagen GPS study, and the model was run to simulate 24 hours in the whole project area of Copenhagen.

In order to retrieve viable results, CopenhagenABM was run 40 times and then a cumulative standard deviation was calculated. A visual inspection of results suggested that 27 runs were already sufficient for a viable result.

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Nevertheless, all 40 runs were included the final calculation. The model was run for all 53.133 links and at 199 locations compared with the counting data retrieved from the City of Copenhagen (Boss Henrichsen 2014) by using the GEH statistics, i.e. statistics to compare peak hour volumes of traffic (Balakrishna et al. 2007). These statistics show that the majority of links have a GEH value of more than 10.0 and only 20% below 5.0, which indicates a good fit between simulation and reality. Figure 9, below illustrates the frequency of the GEH values of the 119 counting locations in Copenhagen.

Frequency of GEH Values

Frequency

80 60 40 20 0 < 5.0

5.0 - 10.0

> 10.0

GEH Values Figure 9: Frequency of GEH values of model results vs. real world traffic counts. GEH Values of < 5.0 show good accordance between real world loads and those modelled. The data is based on 199 counting location selected by the City of Copenhagen.

8.4

Novel method paper 1: Geospatial testing

The goal addressed in Novel method paper 1 was to develop and describe methods to test geospatial components of agent-based models.

In order to be able to quality-assure the wayfinding component of the agents, a concrete test case was designed. The places where agents rest between model steps were recorded and compared to manually designated 'touch down areas'. Every possibility that could arise at a decision point (node in road network) was taken into consideration (touch down at current edge, touch down at junction, touch down at next road segment, jump over two decision points in one step).

8.5

Novel method paper 2: Model results

Novel method paper 2 addresses the results from the agent-based models.

To analyse the results from the agent-based models, a data model for storing these results had to be conceived. A model is proposed in which different parts of program code log into tables within a geospatial database as well as plain, comma-separated files. The following tables were implemented in the model developed within this thesis:

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A. The DotLogger creates a record at each timestep of the model.

Basically it consists of the locational component (latitude and longitude), the tick number and the road segment ID. B. The CalibrationLogger describes the overlap of the modelled route versus the track given by GPS coordinates. It records the route ID, the value of the global parameter of the decision component and the overlap factor (pathsize).

C. The CalibrationRouteLogger stores the trajectories as polylines including metadata such as origin, destination, and point of premature agent removal due to timeout.

D. The SuccessPerRouteLogger simply logs the number of successful simulated tracks for given O-D relations.

E. The RoadLoadLogger counts how many agents step onto each of the 53.133 links. It is subsequently used to calculate the GEH statistics in order to establish the degree to which the model explains real cycling traffic loads in Copenhagen.

The data from these five loggers were imported into GIS programs and spread sheets for further treatment.

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9

Discussion and future research

In this section, I discuss the results of the research presented in this dissertation, compare them to other researchersâ&#x20AC;&#x2122; results in the field and discuss the steps I would follow if I were to continue to work with such issues.

9.1

How do the results compare to the results of others

The method developed in Paper 1 using Google Maps (Bearman & Appleton 2012) created a dataset, which is suitable for further analysis and investigation. Nuojua and Kuutti (2008) also built a tool to collect local knowledge in order to use it within participatory planning.

Nold (2009a) measured skin resistance in situ in the city and mapped the results to Google Earth and paper maps. A statistical analysis such as the one conducted in Paper 1 could be performed on these results in order to investigate the significance of diverse urban factors that influence cyclists and their experiences.

Henshaw & Mould (2001) measure a series of physiological factors such as heart beat, temperature and kinaesthetic factors, etc. These can be interpreted as, or mapped to, good and bad experiences, thereby forming the basis for further investigation. Figure 10 below shows an isometric way of mapping/presenting their results regarding visual stimuli, sounds, odours, etc.

Unlike Nold (2009b), Henshaw and Mould do not assess a systematic and general correlation between experience and place.

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Figure 10: Emotional and descriptive map of route. Taken from Henshaw and Mould (2001)

Paper 1 shows that urban features influence cyclists' good and bad experiences. Since the publication of this paper, new research in the field has been carried out. In a study conducted in six European cities including Copenhagen, Den Haag and Rotterdam, Hull & O'Holleran (2014) used uniform questionnaires and focused on cycling infrastructure of different categories such as coherence, directness, attractiveness, traffic safety, etc.

Their findings go a little further than those of Paper 1 as they come up with a series of recommendations for cycle infrastructure planning such as wide cycle lanes, clear signage, lighting, frequent parking, etc. This method could, therefore, be used to support policy-making activities.

CopenhagenABM, the agent-based model presented in this thesis â&#x20AC;&#x201C; a model holding only one agent type: the cyclist â&#x20AC;&#x201C; as presented in Paper 2 and Paper 3 is basically a wayfinding model. The results are overlap factors between tracks recorded by GPS and their agent-based simulated counterparts as well as a global parameter weight, which is used in Paper 3.

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Similar results could not be found in the current literature. Thompson et al.

(2014) discuss an agent-based model they have built, which models conflicts between motorists and cyclists, states where they occur and, therefore, has a slightly different scope. In addition, the geography of the road network is not a representation of a section of the real world, but is synthetically generated in silico.

Regarding the route choice core, Broach et al. (2012) built a route choice model from the data of 146 cyclists in Portland, Oregon, with a series of variables. Their work does not include any simulation, and its validity, therefore, cannot be directly compared to the work presented here. In the future, it might be interesting to implement their parameter weights into CopenhagenABM. One has to be aware of the fact that cycling behaviour is most definitely different in these two cities where crowding, motoristsâ&#x20AC;&#x2122; behaviour towards the cyclists (Wardman et al. 2007), terrain (Menghini et al.

2010; Menghini et al. 2003), cycling culture (GĂśssling 2013), speeds and infrastructure (Dill 2009) affect the modal split.

9.2

Discussion of the results

The results from the experience mapping approach show that the method laid out in Paper 1 was an adequate way of collecting spatial experience data from people via self-reporting on the Internet. The results of the statistical model can be used to show how the immediate, urban environment has an impact on whether we perceive a certain spatial situation as a good or bad experience. The results themselves give us a clear impression of which elements of the city strengthens and which weaken people's good and bad experiences.

Apart from the experiential results, the spatial distribution as seen in Figure 8 above showed that some areas have a higher density of good or bad experiences. These follow sections with high loads of cyclists. Certain special places such as the recently established green cycling route (good) as well as an annoying set of stairs 4 on a highly frequented link over the harbour (negative) reflect pleasure and annoyance most of the cyclists in Copenhagen would be able to identify with.

It is also important to note that the results of this investigation are probably highly dependent on the specific culture of the country in which it is set.

Applying the same investigation to a city in a different country with a different cycling history, different acceptance of cycling versus motorised traffic as well as a different modal split would require special insight into these factors and, therefore, the careful selection of variables.

Within this dissertation, apart from building CopenhagenABM, emphasis was put on validating the model. The first step of the validation (overlap between 4

Today a recently built cycling bridge bypasses this "red spot".

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GPS track and modelled track) is laid out in Paper 2, the second (upscaling to city level and correlation with real world traffic counts) in Paper 3. The method chosen to verify the model was to re-model every single track from the GPS dataset.

Here the agents were only allowed to travel farther than 150% of the length of the respective GPS before they were prematurely removed. The reasons for exceeding this criterion could be explained as follows: â&#x20AC;˘

Certain types of urban form such as very dense residential areas have an impact on the road structure and, therefore, lure the agents into traps from which they cannot escape.

â&#x20AC;˘

Agents running on trajectories close to cul-de-sacs might suffer from a similar fate to those described above.

â&#x20AC;˘

Tracks with a high number of nodes, i.e. very dense road networks might lead to a higher number of decision points and, therefore, are more likely to be aborted.

CopenhagenABM was in full-scale mode set to model 24 hours. In the locations where the City of Copenhagen provided counting results, the model results were compared to the real-world counts using a GEH (Balakrishna et al. 2007) statistics. Unfortunately, only a small fraction of the counting locations showed a GEH < 5.0, which is considered a good fit. Therefore, the capability of the model to explain and replicate peak hour cycle-traffic in Copenhagen is only limited.

Finally, it should be noted that the results from the model run with the local parameters switched on and off did not live up to the a priori expectations. As shown in Figure 9 above, only 20% of the GEH values were under 5.0, which points to an unsatisfactory performance of the model. In section 9.4.2 below, I discuss options for improving the model in order to increase the ability to replicate reality.

9.3

Strengths and weaknesses of the methods

In this section, I discuss some of the strengths and limitations of the methods.

The method of assessing the influence of spatial situations on experiences presented above is a simple one with exchangeable methods for data acquisition and different data sources from open source data sources. Our model is extendable to other urban or transport-related variables such as traffic injuries and casualties, spatial indicators taken from Space Syntax (Bafna 2003) and the implementation of modelling of experiences of crowding, surface quality and conflicts as suggested in Paper 1.

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Both the travelled route and the experience points had to be drawn on top of an interactive web-based map. It is, therefore, not certain that the respondents actually; (1) remembered their last trip in detail, and; (2) located the experience points in the right places, or even; (3) could recall the most significant experiences.

Agent-based modelling is a flexible approach to modelling complex matters.

Due to its object orientated architecture of entities, attributes, behaviours, etc. it is a clear modelling paradigm. Agent-based models are extensible and their components are exchangeable. Agent-based modelling toolkits reduce the steep learning curve of acquiring knowledge regarding programming. The inclusion of open source geospatial libraries supports the development of spatially explicit models.

The poor degree of overlap between the modelled and the real world counts shows weaknesses. These might be rooted in insufficient background data such as the road network as well as the selection and calibration of the parameters of the BECM. If one wants to systematically implement the methods developed in this dissertation, it is important to bare in mind that background data such as the road network and other web 2.0 generated data sources need to be qualityassured and standards need to be developed and complied with in order to ensure the quality of the data. This may be a great challenge as service levels regarding cycling may be quite different between countries.

Finally, applying the GEH statistic to compare simulated loads to real ones may distort the results. The GEH statistic was developed to compare loads of motorised traffic during peak hours. This may not be optimal for nonmotorised traffic such as cycling and walking. Furthermore, it is from a time where transport was modelled with four step models (McNally 2000) which were equation-based. At that time, agent-based models were uncommon.

These models had a lower fraction of detail compared to our agent-based model where every road is represented.

Therefore, further investigations into and comparisons with different statistics may be necessary to obtain a more precise and usable measure of simulated versus real cycle traffic. An agenda for the verification of agent-based models with one-to-one representations of roads and cyclists needs to be established.

Finally, the spatial distribution of the counting station has to be investigated in order to achieve an even spatial distribution.

9.4

Future developments

This section discusses perspectives for further research regarding; (1) experience mapping; (2) the improvement of the ABM; (3) the incorporation of the experience mapping data into the ABM; (4) differentiating agents into

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types, and finally; (5) the use of the model as a scenario tool within planning processes.

9.4.1 Experience mapping In future studies, researchers could analyse experience types thoroughly, and by applying rounds of user tests, may establish universal classes that could be used nationwide to facilitate comparability. Developing internet-based tools or those being deployed on mobile devices such as smart phones or digital interfaces, cycle sharing schemes, such as the one in Copenhagen, may help to systematise the acquisition of experiential data and make their use standard within the early stages of planning.

Rather than collecting data manually as presented in Paper 1, mobile technology such as smartphones with special apps could help obtain more precise results. These apps could be programmed to pop up at certain time intervals or in special locations in order to remind the subject to enter experiential data.

Another approach would be to avoid setting up a formal survey like the one discussed here and instead harvest from big data such as Instagram or Twitter and narrow this search down to certain geographical areas as well as employ language or sentiment analysis (MartĂ­nez-CĂĄmara et al. 2014).

9.4.2

Future development of the agent-based model

The statistical comparison with traffic counts undertaken by the City of Copenhagen (Boss Henrichsen 2014) by applying a GEH statistic led to unsatisfactory results. Possible solutions for improving the model have been discussed. As with any other software project, the continuous improvement of the underlying data sources, the accuracy of data acquisition and melioration of the codebase may drive the development of such science-driven models forward.

When one builds models, the resources such as the amount of hours to code and the time-consuming process of refining the geospatial data sources limit the detail of the model. Therefore, and as Patel & Hudson-Smith (2012) note, modelling is always a trade-off between simplicity and complexity. Simple models are easier to understand and present than complex ones and take fewer hours to develop, but may not satisfactorily catch all details of the modelled phenomenon.

In order to avoid stopping or interrupting the development process, the modeller applies Occam's razor, a paradigm to justify implementing a not entirely satisfactory behaviour (Forster 2000). A responsible modeller notes each of these 'Occamish decisions' optimally in the same source code management system in which the model's code is stored as issues or tasks. If 58

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there are resources still available after the first release of the model, they are organised by complexity and as many as possible are solved or extended. In the current model the following issues were identified: A. Agent heterogeneity Identical behavioural rules were assigned to all agents. This disregards differences concerning speed, node choice, etc. Heterogeneity, which is clearly one of the basic pillars of agent-based modelling, was, therefore, not implemented in the model.

B. Road Network Simplification The network on which the model runs was taken from OpenStreetMap (Haklay & Weber 2008), which is a web user generated data source where the lack of data and representations of differing quality is sometimes an issue. Links of the road network are often overrepresented, i.e. lines represent lanes and not links. In the current application, this means that agents potentially have a higher number of decision points than in reality: choosing the outer lane would never be an option for turning in the real world. Therefore, the road network needs to be simplified so that a road segment between two junctions is represented by exactly one edge. Such a simplification is not a straightforward task regarding geometrical operations and aggregation challenges.

C. Turn resistances Another feature not facilitated by OpenStreetMap is turn information. All turn operations are allowed in any node, which does not reflect reality where left or right turns might be restricted. Implementation of turn restrictions into the road network will decrease the number of choice possibilities.

9.4.3 Combining the quantitative experience mapping method with an agent-based model Combining the agent-based model presented in the papers with the method for quantifying experiences from Paper 1 would result in a product, which may potentially be useful in the context of planning and urban development. The evidence-based experiential mapping methods can produce maps containing the following information: â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

Positive impact on good experiences Negative impact on good experiences Positive impact on bad experiences

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â&#x20AC;˘

Negative impact on bad experiences

As shown in Paper 1, these four data layers which were generated from urban indicators such as distance to commercial units, types of bike infrastructure, distance to the centre, etc. could be internalised into the agents' behavioural rule-sets thereby guiding the agents towards more realistic behaviour. A connection between the papers would, thereby, be formed.

9.4.4 Modelling experiences Today due to the high modal split of cycling in cities like Amsterdam or Copenhagen, crowding is becoming an issue. Copenhagen is currently fighting this by widening cycle paths (Grimar 2009), establishing green waves for cyclists or intersection modifications (Pucher & Buehler 2008). One could develop sub-models simulating crowding behaviour, which may be a logical step towards a model that explains cycling behaviour to a higher degree than the model presented here. Additional empirical investigations into the nature of crowding on cycle paths, especially when crowding is perceived as a negative experience, are needed and may be achieved by using advanced technology such as the video analysis of moving objects (Hansen et al. 2008), as well as interactive in situ questionnaires conducted via, e.g. smartphones (Christensen et al. 2011).

Further work on dissecting cycling experiences by spatial scale may be a rewarding approach. Some experiences may be related to the trip as a whole entity such as the weather, which is probably constant during a trip. Other experiences may relate to the road segment travelled with its specific street infrastructure or green structures. Finally, specific local or micro experiences such as annoyances in the form of potholes or conflicts with motorists, pedestrians or other cyclists as well as dynamic experiences such as reactions to queuing may also contribute. All these different experience fractions add to the current experience value or level. Dissecting these current experiences into their factors is definitely an exciting research task to be solved and modelled.

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Figure 11: Over-representation in OpenStreetMap in central Copenhagen (Vesterbrogade and HC Andersens Boulevard) Data Source: OpenStreetMap (Haklay & Weber 2008) The superimposed red lines represent lanes rather than simple links. Source: OpenStreetMap.

As a result of Paper 3, the need for further benchmarks of the quality of modelling was isolated. In the current context, it was hard to determine the reasons as to why the model explained reality only to a certain extent. Was it due to a poor road network, which was taken directly from OpenStreetMap or only subject to superficial manual editing? To what extent could the model be improved if the underlying road graph was a proper one with only one edge per relation and not up to six as shown in Figure 11, above. Are there spatial configurations where the agents, maybe due to a lack of spatial memory, get stuck? Is the model too simple to describe wayfinding? Finding answers to these questions might open the door to a series of potential research questions â&#x20AC;&#x201C; answering these could be relevant in order to be able to build strong agent-based models, which then might serve as a basis for valid scenarios of interactive, urban planning processes.

9.4.5 Different preferences, agent types and behaviours Currently, there is only one type of agent available in the model, the cyclist.

Their spatial preferences stem from GPS data collected by Harder et al.

(2011) and were processed and analysed by Skov-Petersen et al. (2013). The final product of this analysis comprises nine parameter weights of local decisions. All agents were treated alike; no stratification by, for example, age, speed or home location was performed. A future and more refined model could take the stratification of agents into groups into consideration, especially as the heterogeneity of agents is one of the basic pillars of agent-based modelling. Speed, again being modelled as a constant dimension, could be adapted to the grade of the terrain, special agent groups dependent on, e.g., age, and also crowding, as mentioned above. Again, more research into cycling behaviour regarding acceleration, deceleration, and movement speed

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in relation to age and cycling proficiency is required. The interesting aspect of crowding is discussed in the literature in connection with evacuation (Almeida et al. 2013; Løvs 1998).

The findings from Paper 1 could be implemented in the agent as two cost surfaces, one for enhancing and decreasing good experiences, and one for enhancing and decreasing bad experiences. The agent could then draw values from these surfaces in decision locations and incorporate them into the decision making process. In addition, experience values could be accumulated and evaluated on agent termination, which would allow experience scores to be assigned to routes, with the scores forming part of the scenario tools as described below.

9.4.6 The agent-based model as a scenario tool One of the most important features of models is that they can not only be used to simulate the status quo of the real world as in this piece of work, but also that they can be used for simulations with different preconditions, i.e. scenarios. In this context, the character of the road environment could be changed by, e.g. adding cycle infrastructure, changing the number of commercial units resulting in a higher number of pedestrians crossing the cycle infrastructure, adding green infrastructure, etc. Adding or removing housing and commercial units would change the number of agents entering or exiting the model and would, therefore, contribute to differences in road segment loads and crowding, which could lead to different spatial choices.

Taking these possibilities for modelling different scenarios into consideration, one can easily imagine the power of extrapolating experiential data collected in (Snizek et al. 2013) to the whole city as it is today or a different city. An interactive and transparent city planning process, which takes experience values seriously and can predict future citizens' experiences, would be a novel addition to the development of planning.

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10

Conclusion

This section concludes the results of the three papers and the two novel methods.

The purpose of Paper 1 was to develop a web-based collection method for cycling-related experiences and methods to determine the influence of urban factors on good and bad experiences.

Collecting experiential spatial data and relating them to urban features as developed in Paper 1 is a suitable method for finding answers to which of these features have a greater or lesser influence on good and bad experiences. The results are easily interpreted and easy to present on maps.

Therefore, the results can be easily explained to laypeople. In addition, the method is elastic, i.e. the data catchment component can easily be replaced by different ways of collecting data such as web 2.0 approaches or in-situ data acquisition devices (Nold 2009a).

Paper 2 and Paper 3 presented the design, construction and results of an agent-based model of cycling in Copenhagen. The model was validated and GPS tracks were simulated and 24 hours of cycle transport were simulated.

This agent-based model is a reasonably simple and intuitive way of approaching a complex modelling task based on a wayfinding component consisting of local parameters stemming from GPS data and a global, directional component. The component of the agent responsible for wayfinding, the Behavioural Edge Choice Matrix (BECM), is suited to being parameterised by weights extracted statistically from GPS data and enriched road networks. The directional parameter is easily exchangeable in order to be calculated differently from today. The other entities of the model rely on standard data such as road networks – stemming from OpenStreetMap (Haklay & Weber 2008) and other freely available data sources – and the National Transport Survey.

Despite the rather low explanation figures, I still believe that this method can be used to model cyclists’ behaviour. Refining and improving the data sources upon which the model is built as well as reworking some or all of the components of the BECM may prove this assertion to be correct.

Finally, two methods that can help to ensure the quality of agent-based models were developed and documented. It can be concluded that these methods were significantly responsible for ensuring the credibility and quality of the model. Method 1 is an interesting step towards a standardised model result storage solution. Method 2 is an implementation of the widely used test cases within software development and suits – quality assured - the BECM and, therefore, the whole model.

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Table of figures

Figure 1: The building blocks of the agent-based model and the respective papers of this dissertation (NMP … New Method Paper) ...... 21 Figure 2: Two kinds of Emotion Maps; on the left, an image of the paperbased, emotion map of Stockport, England; on the right, an Emotion Map built on Google Maps, which shows the emotions of different respondents' trajectories. Both images are courtesy of Christian Nold. ........................................................................................................ 24 Figure 3: Taxonomy of wayfinding tasks. Taken from (Wiener et al.

2009) ....................................................................................................... 26 Figure 4: Sequence Diagram taken from (Bersini 2012). The model's entities (world, agent, site1, sites, site2) are shown on the abscissa; the time is represented on the ordinate downwards; the arrows show interactions between entities ......................................................... 36 Figure 5: A screenshot of the SprawlEffect (Shoko and Smit 2013) model implemented as part of the Urban Suite in NetLogo. On the left, the buttons and gauges used in order to control the model and change parameters. On the right (in green), the modelling geography with built-up areas represented as blue houses. ............................................ 39 Figure 6: A screenshot of a model implementation of system dynamics of rabbit populations implemented in Repast, taken from (Argonne National Laboratory 2013).......................................................................... 40 Figure 7: Delineation of the study area and the 119 Counting Locations 2010-2012 set up by the City of Copenhagen......................................... 44 Figure 8: Geographic distribution of good (left) and bad (right) spots; the municipalities of Copenhagen and Frederiksberg are shown in grey. .... 48 Figure 9: Frequency of GEH values of model results vs. real world traffic counts. GEH Values of < 5.0 show good accordance between real world loads and those modelled. The data is based on 199 counting location selected by the City of Copenhagen. ......................................... 50 Figure 10: Emotional and descriptive map of route. Taken from Henshaw and Mould (2001) .................................................................................... 54 Figure 11: Over-representation in OpenStreetMap in central Copenhagen (Vesterbrogade and HC Andersens Boulevard) Data Source: OpenStreetMap (Haklay and Weber 2008) The superimposed red lines represent lanes rather than simple links.

Source: OpenStreetMap. ........................................................................ 61

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