Loredana Dazzo
Assessing street attractiveness for bicycle route choice : an agent-based approach to investigate travellers' preferences.
Rel. Cristina Pronello, Valentina Rappazzo, José Veiga Simão. Politecnico di Torino, Master of science program in Territorial, Urban, Environmental And Landscape Planning, 2016
Abstract
Introduction
Cycling mobility is a sustainable mode of transport that nowadays governments are trying to promote in order to increase the use of non-motorized vehicles and reduce traffic- related air pollution in urban areas. Bicycling policies try to encourage more people to cycle mainly providing new bicycle paths, but what do inhabitants really need to consider bicycles a good alternative to cars? Which are the best routes for cycling and do bicyclists' preferences change for different users? An in-depth study on travel behavior and route choice represents an essential starting point to understand travelers’ attitudes to cycle. This research aims at assessing the street attractiveness for bicycle route choice, by investigating factors that mostly influence cyclists' preferences when choosing the route. The assessment of these attributes represents a first step for the development of a route choice model that simulates the transport demand.
This research starts during an internship at the Institute of Transport Research of DLR, German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt), in Berlin.
The first chapter of the study presents a literature review on the most relevant attributes influencing bicycle route choice and reports previous studies on route choice models.
The second chapter presents the research objectives and the methodology used to evaluate how much relevant are factors influencing bicycle route choice in Torino and how preferences of different groups of users change depending on the characteristics of the route. For this purpose a survey has been carried out through a questionnaire designed with the group of transport research of Politecnico of Torino, coordinated by Prof. Cristina Pronello, whose results represent the base for the travel demand modelling. Chapter 2 also reports a methodology tested in an urban area in Berlin to visualize the effects of factors on the street network using ArcGIS, defining an approach suitable to work with both linear and zonal data, in order to identify the most attractive streets for cycling.
Chapter 3 reports the results of this study including both the outcomes of the geoanalysis of factors influencing bicycle route choice and the statistical analyses carried out using the data from the survey conducted in Torino.
Finally, the discussion compares the results with the relevant literature and the conclusions summarize the main results of the research.
Relators
Publication type
Subjects
Course of studies
Chapters
TABLE OF CONTENTS
Introduction
1. Literature review
1.1. Factors influencing bicycle route choice
1.1.1. Stimulating and hindering factors 1.1.2. Relevance of macro-factors on bicycle route choice
1.1.3. Preferences related to groups of users 1.2. Bicycle route choice models
2. Objectives and methodology
2.1. Geoanalysis of factors influencing bicycle route choice by ArcGIS
2.2. The questionnaire design
2.2.1. Travel habits and use of bicycle
2.2.2. Revealed preferences and stated preferences
2.2.3. Preferences related to the most habitual bicycle trips
2.2.4. Assessment of the relevance of factors influencing route choice
2.3. Target population, sample selection and administration of the questionnaire
2.4. Data analysis design
3. Results
3.1. Geoanalysis of factors by ArcGIS
3.1.1. Results of the land use entropy index 3.1.2. Visualization of influences of factors on the street network
3.1.3. Hierarchy of links for bicycle route choice
3.2. Factors and macro-factors influencing the route choice
3.2.1. Description of the sample
3.2.2. Travel habits of users
3.2.3. Comparison between revealed and stated preferences
3.2.4. Perception of macro-factors related to users' routes
3.2.5. Evaluation of factors' effect on bicycle route choice
3.2.6. Definition of latent factors through Factor Analysis
4. Discussion
Conclusions
References
Appendices
Appendix I - Geocomputation of a land use entropy index by ArcGIS
Appendix II - Transfer of punctual, linear and zonal data to the graph
Appendix III - Assignment of impedances to the factors based on their relevance
Bibliography
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Websites
BBBike
http://www.bbbike.org
Computation of the entropy index
http://planningandactivity.unc.edu
Cluster analysis
http://www.statisticshell.com
Environmental Atlas Berlin
http://www.stadtentwicklung.berlin.de
Factor analysis
http://www.psychometrica.de
https://researchrundowns.wordpress.com
http://smishra.faculty.ku.edu
http://stats.stackexchange.com
http://www.statisticshell.com
https://www.youtube.com
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