Dynamic scale approach for assessing bikeability with sensitivity for different user groups

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A Dynamic Scale Approach for Assessing Bikeability with Sensitivity for Different User Groups

Master’s Thesis

To obtain a Master of Science (MSc) of Applied Geoinformatics at the Interfaculty Department of Geoinformatics – Z_GIS

of Paris-Lodron-Universität Salzburg

Submitted by Jonas Langerød Rugtvedt Matrikelnummer: 01637271

Supervisor: Dr. Martin Loidl, University of Salzburg – Z_GIS Co-Advisor: Henrik Duus, The Norwegian Public Road Administration

Salzburg, March, 2019

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Abstract

Cycling promotes a healthy and active lifestyle, while having a low monetary cost and a high level of accessibility. Increasing the share of cyclists is therefore considered a crucial measure for improving the livability of cities. Though governments around the world are heavily investing in bicycle infrastructure, the decision-making behind the prioritization of projects is often unknown. Various frameworks have been established to quantify the level of accommodation for bicycling, frequently referred to as bikeability. Most of these approaches have however failed to address the diversity of needs and preferences among cyclists, while the analysis results often are limited by their spatial perspective and unit of representation. This project offers an alternative method for analyzing bikeability, incorporating a dynamic spatial scale framework, while simultaneously acknowledging the needs of different user groups. The project utilizes a web-map application to present the bikeability index, which is devised to provide planners and policymakers an evidence-based spatial decision support system, in order to make more educated decisions regarding bike planning.

The Grenland region of Norway serves as a testbench for applying the developed bikeability index. Expert knowledge in the form of individuals working with bike planning and safety was a fundamental part of the project workflow, through assessing the validity of the identified bikeability indicators, determining their level of impact for the different user groups, and evaluating the developed web-map-application. The integrated spatial scale approach provides a way for examining bikeability from a regional overview down to the local street level. Although the bikeability index results show little distinction between the level of bikeability for the defined user-groups, the framework offers potential for future development. During user testing, the bikeability web-map- application received high praise in terms of usefulness, and as a valuable tool for bicycle planning.

Keywords: bikeability, bikeability index, multi-scale approach, user-group sensitivity, bicycle infrastructure, spatial support decision system, expert knowledge, bike planning

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Acknowledgments

First and foremost, I would like to express my highest appreciation to my supervisor, Dr. Martin Loidl, for piquing my interest in the topic, endless guidance, and continued support during the thesis period. The past months have filled with challenges and meaningful learning, where Dr. Loidl’s mentoring has been instrumental in putting things in perspective and steering me in the right direction.

Additionally, I would like to thank my co-supervisor Henrik Duus and Olav Uldal from the Norwegian Public Roads Administration, for their collaboration and belief in the project. Henrik has in particular been very helpful through discussing fundamental concepts related to the topic, putting me in contact with other experts in the field, as well as in reviewing the results of the project.

I furthermore want to thank Ann Karin Halvorsen and Turid Vongraven at the Norwegian Public Roads Administration, for their help with acquiring data, positive affirmations and reviewing the technical aspects of the thesis. In addition, I want to give my appreciation to Per Harald Hermansen for his help with forming the survey text, as well as Robin Wendel for his technical guidance. Lastly, I want to recognize Miranda Heimstreet, for her support during hours of frustration and help towards reviewing my work.

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1. Introduction

Cycling is a healthy, sustainable, and accessible mode of transportation. Thus, increasing the number of cyclists plays an important role in improving livability and public health. As such, bicycle infrastructure has seen a surge in investment in recent years, but often, the decision-making behind the prioritization of projects and expected results is obscure. Bikeability is a concept used to describe how accommodating a town or area is for cycling, and has become a frequently used term in fields such as transportation and urban planning. Attempts to create measures of bikeability usually follow a similar approach; First by identifying the factors influencing peoples’ tendency to cycle, then weighting them according to importance before calculating an index score for road-segments or areas, and finally ranging them on a spectrum between desirable and undesirable for cycling. Often, this is done on specific spatial scales, analyzing bikeability for road-segments, administrative boundaries, or set resolution raster grids. These approaches use a form of aggregation, which limits the results by hiding underlying information. Additionally, prior methods focused on the “average cyclist” or commuters but failed to account for the diversity of people utilizing cycling as a mean of transport.

This project aims to address some of the shortcomings seen in previous research by developing a bikeability index with an integrated spatial scale approach, with sensitivity towards different user- groups. Established scientific knowledge will help to identify road related factors impacting bikeability, while expert-knowledge related to bike infrastructure and planning will assist in determining the level of influence for the various road features. The information acquired will be used to calculate the degree of bikeability for different spatial scales, from a regional overview, down to the local street- segment level. Finally, a web-map application will be developed to visualize the analysis results, which can help planners understand the nature of the current cycling network, while also assisting in identifying areas in need of improvement, providing a valuable resource for making informed decisions.

The following sections of the introduction provide an outline of the societal issues behind the scientific inquiry, an overview of past scientific research related to bikeability, and a discussion of the gap and deficiencies of current knowledge before presenting an alternative approach to the issue.

1.1 Societal Background

Cars and vehicular congestion are ever-increasing problems cities are facing around the world.

Traffic causes a variety of social, economic, and ecological problems. Even though measures have been taken to increase the share of vehicles using renewable energy, a clear majority of cars still run on fossil fuels, which contribute to the degradation of air quality and climate change. A report from the

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6 Federation Internationale de l'Automobile (FIA) estimates the share of global CO2 emission produced by private cars to 8,51%. The same report approximates an emission growth of between 30% and 110%

by 2050, depending on fuel prices and the level of urban growth (2015). Poor air quality is tied to a plethora of health issues, bearing huge costs for both individuals and societies (Winters, Brauer, Setton, & Teschke, 2013). Furthermore, an EU study estimates ecological and health-related expenditures caused by traffic to be €500bn a year (Cycle-Competence-Austria, 2018). Wasting time in congestion also carries a financial burden and loss of time, as evinced in a study performed by INRIX, estimating time spent in traffic cost Britain, Germany, and the U.S. 461 billion dollars a year(The- Economist, 2018).

For most cities, the majority of infrastructure is designated for motor vehicles, reflecting an unwelcoming setting for pedestrians and cyclists, which decreases the level of livability (Stefánsdóttir, 2014). Additionally, transport-related expansions are repeatedly done on account of the natural environment. Many cities are therefore considered unattractive for active transportation, as people prefer the use of cars as their daily mode of transportation. Meanwhile, a survey of residents within the European Union shows that two-thirds of the adult population are not sufficiently active when measured towards daily health recommendations, highlighting the connection between current urban environmental setups and poor public health (Titze, Stronegger, Janschitz, & Oja, 2008). Attractive urban environments should, therefore, be a priority for governments around the world (Haskel, 2004 as cited by Titze et al., 2008). Increasing the share of people utilizing active transportation is seen as a corner-stone measure towards reducing personal car-use; while simultaneously counteracting traffic congestion, emission of climate changing gasses, and improving public health through increased physical activity and healthier city environments.

Biking is a mode of transport with relatively low costs that also provides flexibility regarding route choice and departure time (Akar & Clifton, 2009). During busy workdays, people often cite lack of time as an excuse for not exercising. By shifting from a passive mode of transportation to an active mode like cycling, it solves the time dilemma, while simultaneously increasing people’s level of activity.

Furthermore, cycling is an inclusive mode of transport, accessible to people of all ages. An EU study concluded that 50% of all car trips within the European Union is a distance shorter than 5km, an ideal distance for bike use (Titze et al., 2008).

Various governments have understood this potential and are looking for ways to increase the share of people utilizing cycling as a daily mode of transportation, while also seeing it as a measure to improve the livability of urban environments and creating sustainable transportation systems (Stefánsdóttir (2014)). Having adequate infrastructure and connected cycling networks are crucial for

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7 promoting bike use. Cities around the world are investing large amounts of funds in bike infrastructure, but in many instances, little knowledge exists about the effect of the measures being put in place (Loidl

& Zagel, 2014). Landis et al. argues against the lack of objectivity in bicycle planning, describing current practices as relying on subjective road evaluations and expert knowledge (1997). As measures to raise bike-ridership receive considerable resources, there is a need for objective tools that can efficiently assess the current situation of a bike network and identify gaps in infrastructure, which can help guide resource allocation and optimize the effect of measures put in place.

1.2 Scientific Knowledge

1.2.1 Bikeability

The body of academic literature shows a plethora of approaches attempting to identify the physical factors influencing peoples’ tendency to cycle. Terms used to describe this phenomenon, often used interchangeably, include bike -suitability, -comfort, -level-of-service or -friendliness. Having identified the inconsistency in the use of terminology, Lowry has proposed the following definitions:

• “Bicycle suitability: an assessment of the perceived comfort and safety of a linear section of bikeway (The term bikeway includes shared-use paths and any roadway where bicycle travel is permitted)”

• “Bikeability: an assessment of an entire bikeway network for perceived comfort and convenience and access to important destinations.”

• “Bicycle friendliness: an assessment of a community for various aspects of bicycle travel, including the bikeability, the laws and policies to promotes safety, the education efforts to encourage bicycling, and the general acceptance of bicycling throughout the community.” (p.

41, 2012)

Nielsen and Skov-Petersen on the other hand, describe bikeability as “the ability of a person to bike or the ability of the urban landscape to be biked” (p. 36, 2018), while Rimmer, Gray-Stanley, and Haugen describes it as “the resourcefulness of an area for bicycling, with sub-domains associated with bicycling paths (e.g., presence/absence, dimensions, materials, condition, accessibility), as well as bicycling promotion, education, and safety” (p. 3-4, as cited by Lopez-Bernal, 2010). The variety of terms, definitions, and applications reflect the complex relationship between the environment and people’s willingness to cycle. Most definitions relate to how features of the road environment affect the level of comfort, safety, and utility while cycling. For clarity, this project defines bikeability as a road or area’s degree of accommodation towards cyclists based on the perceived level of safety, comfort, and utility while biking.

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8 1.2.2 Bikeability Indexes

Previous methods for analyzing bikeability usually include a process for identifying factors influencing people’s tendency to cycle and weighting the factors according to importance, before calculating a bikeability score for individual road-segments or areas, ranging from desirable to undesirable for cycling. The various projects have slight variations in the indicators used, but usually provide empirical evidence for justifying their importance (Lowry, Callister, Gresham, & Moore, 2012).

Commonly used factors to describe bikeability include the slope of a street, traffic volume, the feeling of safety, and the presence of infrastructure such as designated bike-lanes. Expert knowledge, public surveys, or literature reviews are regularly used to identify the level of influence associated with the various factors. Most of the projects culminate in providing a bikeability map, which is intended to assist planners and policymakers in identifying areas in need of resource allocation (Landis et al., 1997).

The indexes are often tied to a specific spatial scale, visualizing bikeability for street segments, census districts, neighborhoods, different resolution raster grid-cells, or entire cities.

The following content gives an overview of previous research and their spatial perspective, listed according to the date of publication.

The Bicycle-Level-of-Service (BLOS) is a popular framework used across the U.S., which was initially developed by Landis et al., in 1997, to examine bicycle quality/level of service in American metro areas (Zhang, Brussel, van den Bosch, Grigolon, & van Maarseveen, 2016). The method focuses on the link between features of roadway segments and human perception towards the level of service (Landis et al., 1997). Various researchers have in recent years made modifications to the model, including Botma (1995), Dixon (1996), Jensen (2007), Petritisch et al., (2007), and in the Highway Capacity Manual (HCM) of 2010, provided by the Transportation Research Board of the National Academies (Lowry et al., 2012). HCM incorporates ten attributes, including the width of outside lanes, the width of shoulders, the proportion of occupied on-street parking, vehicle traffic volume, vehicle speeds, the percentage of heavy vehicles, pavement conditions, the presence of curbs, and the number of through- lanes (Lowry et al., 2012). The various attributes are then used to calculate BLOS, scoring each street- segment according to their level-of-service.

Produced at the University of Graz in 2007, the Bikeability and Walkability Evaluation Table (BiWET) examines the relationship between the built environment and human behavior (Hoedl, Titze, & Oja, 2010). People in the field register environmental attributes and characteristics along a bike route, based on four main categories: traffic safety, the attractiveness of surroundings, land-use, and infrastructure. This method depends on spreadsheets for data collection, while recording observations

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9 for every 10m increment. Unlike other methods, this approach does not incorporate a GIS component, relying on spreadsheets for both data collection and analysis.

Created in 2007, the Portland Cycle Zone Analysis was developed to support the Portland Bicycle Master Plan. By analyzing 32 spatial features over a geographic region, the method utilizes a zonal approach, delineating areas based on similar attributes. The analysis utilizes existing conditions related to land use, topography, population, employment density, roadway infrastructure, and other network attributes, to calculate the individual zones and their level of bikeability (Birk et al., 2009, as cited in Lopez-Bernal, 2010). The weighting of the individual factors is done using local knowledge. The resulting maps provide local planners and policymakers a metric for analyzing existing conditions, assisting users in prioritizing infrastructural investment and helping maximize the effect of new measures. According to Lopez-Bernal, this method is one of the most comprehensive approaches for examining bikeability and has been thoroughly calibrated regarding results and weighting (2010).

The Mapping Bikeability Project, by Winters et al., is a tool for identifying positive and negative areas for cycling (2013). An opinion survey, focus group, and a travel behavior study were all used to identify factors that impact bikeability. Important features identified include bicycle facility availability, facility quality, street connectivity, topography, and land-use. The data is used to aggregate bikeability indices, which include the density of bicycle facilities, separation from motor vehicle traffic, connectivity of bicycle-friendly roads, slope, and density of destination locations. A 10x10m raster grid is the foundation for both calculating and visualizing the analysis results.

BikeScore® is a framework for rating bikeability, based on the environmental characteristics of a city, features used include cycling density, cycle infrastructure, topography, desirable amenities, bicycle sharing programs, and road connectivity. Results range from 0 – 100, 0 being somewhat bikeable, while a score of 100 translates to a “Bikers Paradise” (Walk-Score, 2019). Each city receives an overall score, while interactive maps show bikeability throughout areas as a continuous surface. Winters et al.

conclude in their studies that the BikeScore method might be a valuable tool for research and planning related to bicycle infrastructure (2016).

In the paper titled “Development of a Bikeability Index to Assess the Bicycle-Friendliness of Urban Environments,” the authors establish a bikeability index for mid-sized European cities (Krenn, Oja, &

Titze, 2015). The project utilizes GPS trip data from 1000 participants in the city of Graz, Austria, to identify environmental features that impact bikeability. The method relies on GIS data to quantify the various indicators before calculating bikeability for a 100x100m cells-size raster. Different from other methods, the algorithm incorporates the influence of surrounding cells. The resulting bikeability map intends to help planners identify gaps in a city’s bicycle infrastructure.

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10 The Copenhagenize Index is a benchmarking tool for testing bike-friendliness in cities, assisting governments in their endeavors to promote cycling as a “feasible, accepted and practical form of transport” (Copenhagenize-Design-Company, 2017). James Schwartz and the Copenhagenize Design Company developed the ranking system in 2011, gathering inspiration from livable city rankings such as the ones provided by Monocle and The Economist. The index uses fourteen variables, with additional bonus points for good initiatives, extra effort, or extraordinary results regarding cycling. The indicators include advocacy for cycling, the presence of bicycle culture, bicycle facilities, bicycle infrastructure, bike share programs, gender split, modal share for bicycles, modal share increase since 2006, the perception of safety, politics, social acceptance, urban planning, traffic calming, cargo bikes and logistics.

On a request from the Cyckleframjandets, a Swedish organization working towards improving cycling environments in Sweden, Koucky and Partners developed the “Kommunvelometer” as an objective analysis tool for measuring progress towards improving cycling in Swedish municipalities (Koucky&Partners (2018)). Criterias include the length of cycling infrastructure, investments towards cycling infrastructure, investments towards information and marketing, the number of cycling activities sponsored by the municipality, political plans, goals for cycling, and initiatives towards measuring the share of cyclists. The Kommunvelometer releases annual reports describing the bikeability scores of each Swedish municipality. (Cykelfrämjandets, 2017).

In a study entitled “Bikeability in Basel,” the authors have created a method for analyzing bikeability for road network segments, junctions, and 100x100m grid cells (Grigore, Garrick, Fuhrer, & Axhausen, 2018). Their model quantifies and compiles features such as gradient, AADT, bike infrastructure, speed limit, and greenery, as a travel cost for traversing the cycling network. The underlying assumption for the work is the cyclists attempt to minimize distance while simultaneously maximizing cycling quality.

The paper points to the lack of a scientific consensus towards the concept of bikeability, but that most of the definitions relate to the level of safety, comfort, attractiveness, and distance.

The cycling advocacy group Bike Ottawa publishes a series of cycling maps through their website. One of the maps visualizes the level of traffic stress (LTS), for every road segment in the city. LTS is a function of features such as the presence of cycling ways, highway type, the number of street lanes, maximum road speed, the presence of street parking, and road surface type. The results are presented through four categories, LTS1 being suitable for children, LTS2 being a low level of stress, LTS3 a medium stress level, while LTS4 translates to a high-stress level. The information used to calculate the index relies on data from OpenStreetMap and Mapillary.

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1.3 Gap in Knowledge

Table 1.1 Overview of approaches to bikeability and corresponding spatial scale

Project Spatial Scale Author(s) Year

Bicycle-Level-of-Service (BLOS)

Street-segment Landis et al. 1997

Portland Cycle Zone Analysis

Aggregated zones (polygons) Birk et al. 2007

Bikeability and Walkability Evaluation Table

Street-segment S. Hoedl, S. et al. 2007

Kommunvelometer Municipality Koucky & Partners 2010

The Copenhagenize Index

City Copenhagenize Design

Company and Schwartz

2011 BikeScore® City score and continuous surface for

area

Winters, et al 2012 Mapping bikeability: a

spatial tool to support sustainable travel

10x10m raster grid M. Winters, et al. 2013

Development of a Bikeability Index to Assess the Bicycle-Friendliness of Urban Environments

100x100m raster grid Krenn, Oja, Titze 2015

A GIS Based Bicycle Level of Service Route Model

Cycling Routes (based on BLOS) Zhang et al. 2016

Bikeability in Basel Street-segment (edge) and grid Grigore et al. 2018 Level of Traffic Stress

(LTS)

Street-segments (edge) Bike Ottawa 2018

*BLOS has been further developed by a variety of researcher into slight variations of the original framework

Although the previously discussed indexes are powerful tools for examining bikeability, they are limited by their spatial resolution. As seen in table 1.1, most of the models rely on the aggregation of data, which leads to degradation of details and variability. Even though street segments are to some degree a generalization of a road environment, they provide a significant level of detail. It can, however, be a problematic unit of representation when attempting to get an overview or compare areas. As an example, looking at the bikeability score of an entire city might give clues towards the overall situation, but proves useless towards highlighting areas or streets that are underserved regarding bicycle lanes and street lights. This issue has strong connotations to the modifiable area unit problem (figure 1.1) as the spatial scale used for sampling information impacts the nature of the results. As described by Loidl et al., the results of such analysis are influenced by the spatial scale and level of aggregation chosen, leading to possible biases in results and derived conclusions (2016). Lopez- Bernal elaborates on the one-dimensional spatial scale issue, pointing to the lack of bikeability models that address the overall features of a network, previous work focusing on discrete roadway segments.

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12 He goes on to argue for the need of a holistic approach for understanding bikeability (2010). From examining the scientific literature, there exists no integrated approach that produces and displays results on multiple spatial scales. A multi-scale approach is therefore needed to incorporate data from various sources and types, producing results that can seamlessly visualize bikeability for streets and areas.

Furthermore, previous research often focuses on the “average” cyclists or fails to address the needs of different road users. The people who utilize biking as a mode of transportation are highly diverse, in the form of age, ranging from children to elderly, varying in skill level and fitness. An increasing use of e-bikes adds another user-group to the cycling scene, for which the environmental factors differ in impact. Analysing bikeability for the average cyclist disregards essential information, which should be taken into consideration when planning bicycle infrastructure.

1.4 Aim of Project:

1.4.1 Research Objectives

The core research-objective of the project aims at creating a scientifically grounded framework for analyzing bikeability on various spatial scales, with sensitivity towards different user- groups, providing an objective tool for assisting in bicycle planning. By creating a method for examining bikeability from a street-segment perspective to a city level, it is in the author's intention to address the previous gap in knowledge concerning the single-spatial-scale approach. Additionally, the project will consider the differences in needs among cyclists, which previous studies fail to incorporate.

To accomplish the goals of the project, previous scientific methods will be fused to create a more holistic approach for understanding bikeability in urban environments.

Figure 1.1 Visualization of the Modifiable Area Unit Problem (MAUP). The size the enumeration unit (in this example the grid resolution) dictates the trend showed when sampling the underlying phenomenon.

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13 Central objectives of the project include:

• Reviewing previous scientific research to identify indicators of bikeability.

• Utilizing expert knowledge to weight the importance of the identified indicators with regard to different user-groups.

• Generating a bikeability index based on the indicator-based assessment model (IBAM) presented by Loidl & Zagel (2014).

• Producing a web-map-application, to interactively visualize the result, while also establishing a tool for planners and policymakers to understand bikeability and its influencing factors.

It is essential to underline that the project will focus on features of the physical environment, identified in previous literature to be descriptive towards bikeability. The approach will not include social factors such as motivation, health status, interests, and social support.

1.4.2 Workflow

Figure 1.2 shows a simplified workflow diagram for the project. While the first two steps already have been discussed, the next sections will elaborate on the following steps in the workflow.

The project emphasizes the use of dialog with experts in the field, implementing feedback loops to obtain a satisfactory result.

Utilizing GIS

As creating a bikeability index requires large amounts of data and computation, the project will rely on geographic information science (GIS). GIS provides functions to consolidate data, enrich features, spatial analysis and visualization, which are essential tools for the overall workflow. In today’s highly digital society, initiatives such as open-data and crowd-sourced information provide apt

Figure 1.2 Simplified workflow

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14 opportunities for compiling information related to bikeability. GIS therefore serves a vital function in this research, being used to harmonize the spatial data needed to represent the indicators of bikeability, as a tool for calculating the bikeability index, and to communicate results through a web- map-application.

Identifying Indicators of Bikeability

Identifying the factors influencing people’s route choice while traveling by bike is central to creating a robust bikeability index. In this process, the project will rely on previous scientific research.

Examples of indicators previously used include the level of bicycle-related infrastructure, traffic safety, hilliness, and the presence of green features. The use of expert-knowledge is an essential step in this process, to verify the validity of the chosen bikeability indicators.

A Multi-Scale Approach

The project will utilize a multi-scale approach to address the one-dimensional spatial scale issue. The edge-based network will be the fundamental building block in the workflow, holding information related to individual street segments, such as traffic-speed, traffic-volume, road type, and other descriptive attributes. The network will with all its underlying information be used as input to calculate bikeability for the various spatial scales, as seen in figure 1.3. The approach utilizes a mix of spatial reference levels, including an equal area reference units such as a hexagon grid, irregular boundaries in the form of census districts, school zones and urban areas, and street segments as the most local scale. By using hexagon grids of various resolutions, it helps assist the interpretation of bikeability on various scales. Incorporating administrative units such as census districts and school

Figure 1.3 The multi-scale approach to bikeability

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15 neighborhoods, provide the opportunity for rapid comparison between zones. As an example, by calculating bikeability for school zones, the results can be used to examine the factors impacting bike ridership in specific areas and assist the prioritization of funds. By using a multi-resolution approach, it gives the end-user the opportunity to examine bikeability and its adhering factors on different levels, being able to zoom from an overview level, down to individual street segments.

Producing a bikeability index

Loidl & Zagel describes through their research an indicator-based assessment model that incorporates a variety of road related information tied to bicycle safety (2014). Even though the model examines safety risk, it gives the opportunity to customize the indicators and weights, making it a tool which can be used to assess bikeability. Through his work, “Interactive Network Assessment Tool Using ArcGIS API for JavaScript.”, Wendel provides a technical approach for implementing the indicator based framework, which will serve as the foundation for modifying the index to reflect bikeability (2015).

Incorporating sensitive towards different user-groups

Previous bikeability methods aim at describing bikeability for the “average cyclist,” but as cycling is a mode of transport for a variety of people, all with individual predispositions for cycling, this becomes problematic. Representing the needs of all road users is a nearly impossible task, but it is the author's intention to show how bikeability can be analyzed with a sensitivity to different user-groups.

Weighted indicators will, therefore, be employed to reflect the characteristics of select cyclist groups.

Expert knowledge plays a critical function in this process, assisting to determine the impact of each bikeability indicator with regard to the various user groups.

Visualizing the results and intended end-use

A web-map-application will be developed to communicate the results in an interactive and informative manner. The application will simulate a spatial decision support-system for bike planning, with intended end-user being planners and policymakers. By interactively presenting the results and contextual information, it can identify areas or roads with low levels of bikeability, which can support decision making regarding fund allocation. It furthermore provides the possibility of examining bikeability on different levels of government, from a national level, in visualizing cities in need of funding towards bike infrastructure, down to municipal councils, attempting to identify streets in need of improvements. User testing will play an integral part in the web-map-application development, by providing feedback on the level of utilization and usability, which will then be used to make improvements.

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1.5 Study Area

The urban part of the Grenland region of Norway will act as a study area for testing the bikeability analysis framework. Grenland covers three municipalities: Bamble, Porsgrunn, and Skien, which has a combined population of around 107 000 (Norwegian- Statistics, 2018a), making it the ninth largest metropolitan area of Norway. Figure 1.4 visualizes the area of interest, which in total covers 64km².

The Norwegian government has in recent years created legislation to limit the rise of personal car-use in cities, in order to combat traffic-related pollution, reduce congestion, and to improve air quality. Some of the strategies used include investments towards better public transportation, implementing road tolls, and removing parking spots in city centers. A valuable tool in this strategy is “bypakker” which in English translates to city-packages. The packages are agreements

between counties, municipalities, the national government, and government organizations such as the Norwegian Public Roads Administration and the Norwegian National Rail Administration. Bypakke Grenland, which is the agreed upon city-package for the Grenland region, has during its first budget phase 2,67 billion NOK (28 million EUR) to be distributed on various projects. Central to the agreement is the reduction of climate gas emissions from traffic, creating a more livable city environment with less car-use, improved mobility, more attractive public transportation, a better and safer environment for cyclists and pedestrians, and establishing a more accessible transportation system (Bypakke- Grenland, 2015).

Regarding the modal share for cycling, the goal of the Bypakke Grenland is to double the share of cyclists from 4% to 8% by 2025 (Bypakke-Grenland, 2018). The region is already seeing advancements towards this goal, traffic counts estimating the share of cyclists to 6% in 2017, while in June 2018, the number of cyclists at certain points had increased by 20% from the year before.

Figure 1.4 Overview of the study-area 1:300 000

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17 Each project uses the following criteria for prioritizing improvements to the road system:

• Decreasing travel time for prioritized road users (pedestrians, cyclists, public transportation, and commercial transport).

• Benefitting a large share of the population in the area.

• Improving the connectivity of the overall transportation system.

• Increasing the feeling of safety and positive experiences while using the road system.

• Helping reduce the share of personal car use.

• Helping increase the concentration of land use.

The proposed bikeability index and web-map-application can be of significant support in assessing the bikeability of the current network, while also to identify areas and roads in need of improvements. By adding an extra layer of objective information for planners and policymakers, this framework can help improve the bikeability of the region, which is an integral part of “Bypakke Grenland.”

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2. Methods

2.1 Software Used

The use of geographic information systems (GIS) was a central component for most of the practical processes in the project. Software used includes ArcGIS Pro v2.2 (ESRI, 2018b), ArcGIS Desktop (ESRI, 2018a) and QGIS 3.2 (QGIS-Development-Team, 2018).

In particular, the ArcGIS Pro Model Builder was heavily utilized to increase efficiency. Saving workflow models in an ArcGIS Toolbox, allowed for easy re-use, sharing, and transparency in the methods used. It also created the ability to iterate through multiple input datasets and quickly alter the input parameters. As some of these steps are rather intricate, the models incorporate Python (Python-Software-Foundation, 2018) and ArcPy scripts. The model tools created are found in Appendix I.

Various models were created, with the aim of:

1. Consolidating, processing, enriching, and storing data, mainly road features and attributing information.

2. Generating the various indicators tied to bikeability and their sub-values.

3. Calculating a bikeability index for the various spatial scales and the defined user-groups.

Figure 2.1, is a screenshot from a typical work situation in ArcGIS Pro, illustrating the toolbox with the various models on the right, the model builder workspace in the bottom of the picture, a map view

Figure 2.1 A view of the ArcGIS Pro work environment

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19 in the center, and a selection of datasets on the left side of the image. The web-map-application was developed using ArcGIS Online (ESRI, 2019) and its web-app-builder framework.

2.2 Indicators used to Measure Bikeability

The following sections describe the nature of the bikeability indicators identified, their scientific anchoring, and quantification process. The sub-values of each indicator must be numerically represented to calculate an overall bikeability index. During this process, the indicator-based assessment model by Loidl & Zagel was used as a point of reference, while also incorporating knowledge from other scientific research (2014). The importance of each indicator can easily be measured and compared by normalizing the sub-values of each indicator on a range between 1 and 10. A value of 10 indicates a high positive influence on bikeability, while a 1-value translates to a strong negative influence.

2.2.1 Bicycle Infrastructure

The presence of bike infrastructure is the factor most frequently mentioned when describing the bikeability of a street or area. This infrastructure is commonly cited as being composed of , bicycle lanes, mixed-used paths, or designated bike paths. These features allocate space for cyclists, distancing them from motor vehicles. Various scientific projects have examined the importance of this separation and its relation to the feeling of safety while cycling (Akar & Clifton, 2009; Grigore et al., 2018; Hoedl et al., 2010; Titze et al., 2008; Wahlgren & Schantz, 2012; Winters et al., 2013). A survey-based assessment of a university campus in Maryland identified that the lack of bike lanes and paths was discouraging towards cycling, as people felt unsafe biking in the same space as motor traffic (Akar &

Clifton, 2009). A different study described how individuals would utilize streets with bike lanes over unmarked streets that had lower levels of traffic (Lopez-Bernal, 2010). Krenn et al. observed how cities with more copious amounts of bike lanes had higher numbers of cyclists, correlating the presence of bicycle infrastructure to improved bikeability (2015).

Research by Stefánsdóttir shows that proximity to traffic has a negative influence on cyclists’ emotional well-being (2014). Even though bicycle lanes are useful for separating motorized and non- motorized traffic, they still function in close proximity. Thus, complete separation through designated bike paths is highly valued by cyclists (Krenn et al., 2015). The research by Akar & Clifton reaffirmed the previously made observations, as they found that in some instances, individuals would extend their daily commute by up to 20 minutes to use an off-road bicycle path (2009). Further, a study in Portland, Oregon found that often cycle-lanes alone are not sufficient to increase the proportion of people cycling. The report suggests that the creation of bike boulevards is a better alternative (Lopez-Bernal, 2010).

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20 Table 2.1 provides an overview of the different sub-values and scores for the bike infrastructure indicator. The scoring of each value reflects findings in the scientific literature. All forms of bike infrastructure have a relatively high positive influence on bikeability. Bike lanes provide separation from motor vehicles, but cyclists still find themselves near traffic. Mixed-use paths also score high, but the presence of both forms of road users can cause conflicts. Bike paths obtain a perfect score as they are specifically designed for cyclists.

Table 2.1 Scoring of the bicycle infrastructure indicator.

2.2.2 Road Category

Road category is another factor influential to bikeability. The designated road class translates to the function of the road, as a route for through traffic or regional transport, such as highways, or for local level transportation, like municipal roads. The road type can also act as a proxy for estimating the speed limit and daily traffic of a street. For example, arterial roads are often dominated by substantial amounts of traffic and high travel speeds, which cyclists tend to avoid, while residential streets are better suited for cyclists due to their calmer conditions (Stefánsdóttir, 2014; Winters et al., 2013) Table 2.2 provides an overview of the various sub-features and scores. Roads with limitations towards cyclists, such as highways, are classified as restricted. Sidewalks, stairs, and walk-ways where biking is not allowed, but walking the bike is possible, are considered partially restricted. Both the restricted and partially-restricted roads receive very low bikeability scores. The scoring of the other road types is dependent upon their level of accommodation for cyclists based on characteristics such as the estimated level of traffic, posted speed limit, and road surface type.

Bicycle Infrastructure Score

Bike path 10

Mixed use 9

Bike lane 7

No bike infrastructure 1

No value (null) 0

Road Category Score

Calmed Road (example, bike boulevard) 10

Private road 7

Municipal Road 6

Forrest road 7

Trail 4

Large trail 4

County Road 3

Partially Restricted (walk-ways, sidewalks, stairs) 2

European Highway 1

National road 1

Restricted (forbidden to bike) 1

No value (Null) 0

Table 2.2 Scoring for the road category indicator.

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21 2.2.3 Daily Mean Level of Traffic (AADT)

Low levels of traffic have been positively correlated with biking, while high levels are considered a hindering influence. (Akar & Clifton, 2009; Hoedl et al., 2010; Krenn et al., 2015; Wahlgren

& Schantz, 2012; Winters et al., 2013). A study of Austrian students observed a strong association between traffic safety along bicycle paths, and the likelihood of using bikes as a mode of transportation (Titze et al., 2008). Traffic also has negative connotations regarding noise and pollution, and degrading the aesthetic impression while biking (Stefánsdóttir, 2014). Additionally, routes with large shares of trucks, buses, and cars driving at high speeds are particularly discouraging for cycling (Lopez-Bernal, 2010). Some of the variations of the “Bicycle Level of Service” (BLOS) assessments incorporate the share of heavy vehicles as a variable, including the approach provided by Zhang et al., (2016).

To incorporate these features in the model, the daily mean number of vehicles (AADT), as well as the daily share of long-vehicles such as trailers and buses, are used as bikeability indicators. The share of heavy traffic is measured as a percentage of the average daily traffic. As visualized in table 2.3, the highest scores have a low AADT range, while the range widens for the lower scores. This is done to emphasize how substantial a difference between an AADT value of, for example, 100 and 600 cars is for cyclists, while roads with traffic levels numbering in the thousands correlate very negatively with cycling. The "Heavy Traffic" indicator uses a similar logic for scoring its sub-values.

Table 2.3 Scoring of the AADT infrastructure indicator. Figure 2.4 Scoring of the Heavy Traffic indicator.

2.2.4 Traffic Speed

The designated speed limit of a road is an indicator that strongly relates to traffic and the feeling of safety. Studies show how high traffic volumes and traffic speeds degrade the feeling of comfort while cycling, as cyclists prefer routes with lower speed limits and AADT to distance themselves from air pollution, noise and interactions with motor vehicles (Pikora, Giles-Corti, Bull,

The Share of Heavy Traffic (%) Score

0 10

> 0 and < .5 9

>= 0.5 and < 1 8

>=1 and <2 7

>=2 and <4 6

>=4 and <6 5

>=6 and <10 4

>=10 and <14 3

>=14 and <20 2

>=20 1

no value 0

Daily Mean Traffic (AADT) Score

0 10

> 0 and < 250 9

>=250 and <500 8

>=500 and <1000 7

>=1000 and <2500 6

>=2500 and >5000 5

>=5000 and >7500 4

>=7500 and <10000 3

>=10000 and <20000 2

>=25000 1

no value 0

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22 Jamrozik, & Donovan, 2003; Stefánsdóttir, 2014; Wahlgren & Schantz, 2012). In scoring the indicator sub-values (table 2.5), higher traffic speeds translate to a lower bikeability score.

Table 2.5 Scoring the speed limit indicator.

Speed Limit (km/h) Score

>=20 10

30 9

40 8

50 7

60 6

70 5

80 4

90 3

100 2

110 1

null value 0

2.2.5 Slope

The slope of a road influences the level of comfort while biking. For example, cycling uphill requires extra effort. Previous indexes incorporate hilliness as a factor that negatively impacts people’s willingness to cycle (Wahlgren & Schantz, 2012; Winters et al., 2013). Both studies performed by Krenn et al. and Stefánsdóttir show that high gradients have a negative association with bikeability, while flat topography has a positive influence (2015; 2014). There is, however, research that has found a positive correlation between cycling and hilliness, but this is related to biking for recreational purposes (Titze et al., 2008). In today’s urban environment, for many, hilliness is becoming less of an issue, as electric bikes solve this challenge.

Using a bi-directional slope incorporates the influence of both uphill and downhill gradients.

In evaluating the slope of roads (table 2.6), slight downhill and relatively flat roads are given a high score, while steeper uphill and downhill roads receive a low mark. High negative gradients cause higher speeds, which increases the risk of collisions (Grigore et al., 2018). This approach draws inspiration from the methods utilized by Loidl & Zagel (2014).

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23

Table 2.6 Scoring the slope indicator.

Gradient Score

-1.5 to -3 10

1.5 to -1.5 9

-3 to -6 9

1.5 to 3 5

3 to 6 4

-6 to -12 4

6 to 12 2

>=12 1

<=-12 1

No value 0

2.2.6 The Width of Road Lanes

When biking in the presence of traffic, having adequate space and separation from motor vehicles is essential to avoid dangerous situations. The width of traffic lanes, therefore, influences the level of comfort while biking, as wide lanes provide vehicles sufficient space to pass without getting too close to cyclists. (Akar & Clifton, 2009). Lane-width is a factor incorporated in numerous indexes, including the BLOS framework, Geelong Bikeplan – Stress Level Concept, Bicycle Safety Index Rating, and the Stress Level Concept by Sorton and Walsh (Lopez-Bernal, 2010). In evaluating the different values, larger widths are associated with a higher score, as they leave more room for both cyclists and motor vehicles to coexist. Table 2.7 provides a complete overview of the values and scores for the road lane width indicator.

Table 2.7 Scoring the lane width indicator.

Lane Width Score

> 5 10

> 4 9

> 3 7

> 2 5

<= 2 1

Missing Value (Null) 0

2.2.7 Road Surface

A factor often overlooked or deemed insignificant towards bikeability is the road surface type.

Road surfaces have distinct levels of smoothness and resistance, causing a certain degree of vibration.

Large vibrations and resistance negatively impact the level of comfort while cycling, as more energy is required to obtain forward progress (Bíl, Andrášik, & Kubeček, 2015; Landis et al., 1997). The scoring of surfaces seen in table 2.8 mirrors the framework provided in the paper “How comfortable are your

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24 cycling tracks? A new method for objective bicycle vibration measurement” (Bíl et al., 2015). Asphalt and concrete score high due to low resistance and limited vibrations, while cobblestone provides an uneven surface with a high degree of vibrations. Soft surfaces, such as trails, score below average due to the lack of traction and surface unevenness.

Table 2.8 Scoring the road surface type indicator.

Road Surface Type Score

Asphalt/Concrete 10

Gravel 7

Soft 4

Cobble Stone 1

Missing value (null) 0

2.2.8 Street Parking

Street parking has been shown to be a troubling element for biking. Cyclists prefer routes without cars parked on the side of the road (Wahlgren & Schantz, 2012). Studies have furthermore explored how cyclists will make detours to avoid on-street parking in favor of bike paths (Tilhaun et al., 2007, as cited by Akar & Clifton, 2008). The disturbance produced by cars moving in an out of spaces, as well as the pedestrian traffic tied to the parked cars can cause dangerous situations, impacting the level of perceived safety for cyclists (Landis et al., 1997). The BLOS assessment is one of the indexes that incorporates on-street parking as a variable, measuring the frequency of on-street parking spots and driveways per road mile. In the project, street parking is represented as a Boolean value for street segments, with no parking receiving a positive score, while the presence of street parking results in a negative value.

Table 2.9 Scoring the street-parking indicator

Street Parking Score

No 10

Yes 1

2.2.9 Street Illumination

The presence of street lights has been found to contribute positively towards bikeability.

Having an illuminated street helps navigation, and increases the feeling of safety after dark (Pikora et al., 2003). Furthermore, in a survey performed by Akar & Clifton, the lack of street lights made people feel unsafe while riding due to concerns of crime and theft (2009). The presence of street lights, therefore, receives top marks, while no street lights receives a negative score (table 2.10).

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Table 2.10 Scoring the street lights indicator.

Street Lights Score

Yes 10

No 1

2.2.10 Street Connectivity

In examining the urban environment, the configuration of streets can stimulate movement. In general, the higher the connectivity of a street network, the easier it is for people to move from one location to another, helping to reduce travel time (Lowry et al., 2012). Large number of street junctions provide more opportunities regarding route choice, and increases the level of practicality, which is essential when cycling (Nielsen & Skov-Petersen, 2018; Pikora et al., 2003; Wahlgren & Schantz, 2012).

The work of Winters et al., confirms these findings, observing a positive association between cycling and higher densities of intersections (2013). On the other hand, a low level of connections decreases connectivity and practicality, which has a negative impact on cycling (Titze et al., 2008). Connectivity in the workflow is measured as the number of intersections per kilometer within an area, such as a polygon or census district (table 2.11). Intersections are defined as junctions in the road network, where three or more street segments converge.

Table 2.11 Scoring the connectivity indicator.

Table 2.12 gives a summary of the selected indicators, a condensed list of arguments for their significance, and their connection to previous scientific work.

Street junctions per kilometer Score

> 7 and =< 8 10

> 6 and =< 7 9

> 5 and =< 6 8

> 4 and =<5 7

> 3 and =<4 6

> 2 and =<3 5

> 1 and =<2 4

> 0,5 and =< 1 3

> 0 and =< 0,5 2

0 1

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26

Indicator Arguments Scientific References

Bicycle Infrastructure

• Frequently mentioned as an essential factor influencing bikeability

• Includes bike-lanes, bike-paths, and mix-use paths.

• Provides separation from motor vehicles and increases the feeling of safety

• Can allocate spaces for cyclists and pedestrians, reducing conflict and increasing the perception of safety for pedestrians

Krenn et al. 2015; Hoedl et al. 2000;

Winters et al. 2013; Bernal-Lopez, 2010;

Landis et al. 1997; Akar and Clifton, 2008;

Stefansdottir, 2014; Titze et al. 2008;

Pikora et al. 2003; Wahlgren and Santzhes, 2010; Grigore et al. 2018; Bike Ottawa

Daily Mean Traffic (AADT)

• Low levels of traffic are positively correlated with biking, while high levels of traffic are hindering influences

• The level of traffic is associated with the perception of safety

• High traffic has been shown to reduce the aesthetic experience while cycling

Krenn et al. 2015; Zhang et al. 2016; Landis et al. 1997; Winters et al. 2013; Bernal- Lopez, 2010; Akar and Clifton, 2008;

Stefansdottir, 2014; Titze et al. 2008;

Wahlgren and Santzhes, 2010; Grigore et al. 2018

The share of heavy vehicles •Routes with large shares of trucks, buses, and cars driving at high speeds are particularly discouraging for cycling

Bernal-Lopez, 2010; Landis et al. 1997;

Zhang et al. 2016; Grigore et al. 2018

Road Category

• Road category can be used as a proxy for describing the bikeability of the road, often correlating with the level of traffic and speeds limits

• Highways often have higher designated speeds and levels of traffic, providing an unwelcoming environment for cyclists.

• Residential streets are often calmer and better suited for cycling

Stefansdottir, 2014; Krenn et al., 2015;

Winters et al., 2013; Bike Ottawa

Speed Limit

• The designated speed limit has shown to influence bikeability, as higher traffic speeds degrade the feeling of comfort while cycling

• High speeds create a high level of noise, decreasing the aesthetic experience while cycling

Titze et al., 2008; Landis et al., 1997; Hoedl et al., 2010; Krenn et al., 2015;

Stefansdottir, 2014; Wahlgren & Schantz, 2012; Zhang et al. 2016; Grigore et al.

2018; Bike Ottawa Slope

• The slope of a road influences the level of comfort while cycling, as extra effort is required when cycling uphill, while flat terrain and biking downhill can impact bikeability positively

• Steep downhill roads cause high speeds, which increases the chance of accidents and collisions

Titze et al., 2008; Winters et al., 2013;

Krenn et al., 2015

Stefansdottir, 2014; Wahlgren & Schantz, 2012; Grigore et al. 2018

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27 Road Surface Type

• Different surface types have distinct levels of resistance and vibrations, with high vibrations decreasing the level of comfort for cycling

• Uneven surfaces require extra effort

Landis et al., 1997; Bil et al., 2015; Bike Ottawa

Lane Width • Sufficient separation between motor vehicles and cyclists is essential in the feeling of safety and comfort while cycling

Akar & Clifton, 2009; Lowry et al. 2012;

Bernal-Lopze, 2010; Landist et al. 1997

On-Street Parking

• Street parking is a disturbing element for bikers, thus cyclists prefer routes without cars parked on the street

• Cyclists will make detours to avoid routes with on-street parking in favor of bike paths

• Cars moving in and out of parking spaces, as well as pedestrian traffic tied to the parked cars is seen as an element of disturbance for cyclists, impacting the level of perceived safety

Akar & Clifton, 2008; Lowry et al. 2012;

Wahlgren & Schantz, 2012; Landis et al.

1997; Grigore et al. 2018; Bike Ottawa

Presence of Street Lights

• The presence of street lights has been found to be positively correlated with cycling, as it increases the feeling of safety after dark

• Lack of street lights made people feel unsafe, due to concerns of crime and theft

Akar & Clifton, 2009; Pikora et al. 2003

Street Connectivity

• Greater connectivity gives additional opportunities regarding route choice, increases practicality, and reduces travel time

• Low levels of connectivity decrease practicality

• Higher densities of intersections are positively associated with cycling

Wahlgren and Schants, 2012; Pikora et al.

2003; Winters et al. 2013; Titze et al. 20??;

Zhang et al. 2016; Nilsen and Skov- Petersen, 2018; Lowry et at., 2012

Table 2.12 An overview of the indicators selected, their significance, and use in previous research.

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2.3 Weighting of Indicators

The previously identified indicators vary in level of significance, distinctively impacting people’s feelings of comfort, safety, and utility. As an example, when it comes to route choice, the presence of bike infrastructure might be of more importance than avoiding on-street parking, as it provides complete separation between cars and bikes. Ascertaining the impact of the various indicators in relation to different user groups must, therefore, be done to create a robust bikeability index.

2.3.1 Defining Cyclist User-groups

Previous studies have regularly focused on the “average cyclist,” but as people have different predispositions for biking, it can be problematic to generalize all cyclists into one group. Creating representative groups is a highly complex task, as individuals are different regarding; physical form, cognitive abilities, and experience. However, it is in the author's intention to show how bikeability analysis and bike planning can incorporate sensitivity towards different road users.

The project has defined four user-groups:

• Experienced cyclists. Intended to include people with sufficient physical form to operate a bicycle as a daily mean of transport, as well as possessing the cognitive ability to maneuver complex traffic situations.

• School Children. Includes children of school age, who might have little experience or ability for comprehending complex traffic situations.

• People with reduced mobility. Is meant to encompass individuals that due to limited

physical fitness, cognitive abilities, or other disadvantages limit a persons capacity for cycling.

• E-bikers. Challenges such as hills and long travel distances have for many people been answered by using electric bikes.

2.3.2 Utilizing Expert Knowledge

The project relies on expert knowledge to determine the level of influence for each indicator in the four user-groups. Experts consulted included personnel from the Norwegian Public Roads Administration, who work closely with bike infrastructure planning, bike safety, and related tasks, as well as people of similar positions at the county and municipal government. The process relied on an online survey, in this instance LimeSurvey (Schmitz, 2019), to gather relevant information. As the experts consulted are located in Norway, the survey was created using Norwegian. The survey contains an introduction-page that explains how a route choice for cycling is a complex topic, connected to a multitude of factors. It furthermore highlights the projects focus on the impact caused by the road

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29 environment and its influence on the feeling of safety, comfort, and utilization, which is instrumental towards the decision of where to cycle.

Additionally, the page describes how these effects vary based on the individual. The participants are then asked to rate the influence of each indicator, based on four described user groups. The introduction page also underscores how the project focuses on biking as a mean of transport, to for example, school or work, and not for leisure activities such as exercise. Lastly, there is a notice of the anonymity given to all participants. Figure 2.2 shows a screen-shot of the introduction page.

In answering the questions related to the influence of the different indicators, each factor is to be scored between 1 – 10, 1 constituting little influence, while 10 is a factor with very high impact.

Figure 2.3 provides an example of the survey questions related to the bikeability indicators.

Figure 2.2 A screen-shot of the survey home page.

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30 The ten indicator related questions are phrased as follows:

1. How vital is established bicycle infrastructure when it comes to route choice for cyclists? Bike infrastructure includes features such as cycleways, mixed-use paths, and bike paths.

2. How large of an influence does the traffic level along a street have on route choice when biking to school or work?

3. To what extent does the presence of busses, trailers, and trucks have on whether a road is used for cycling?

4. How important is the illumination of streets when deciding to use cycling as a mode of transportation?

5. What impact does road speed-limits have on choosing to bike to school or work?

6. How large of an influence does the roads designated category have on whether to cycle along a road or not? Examples of road categories include highways, residential streets, private roads, and forest roads.

7. How influential is slope towards the suitability of a road in terms of cycling? Consider both uphill and downhill slope.

8. What level of significance does the road surface type (gravel, cobble-stone, asphalt, etc.) have on a road being accommodating for cycling?

9. How important is road-width towards the suitability for cycling?

10. To what extent is on-street parking influential towards route choice when cycling?

The eleventh question of the survey is tied to the background of the participants, asking for information regarding workplace/institution, and how many times a week on average does the participant cycle. The question also inquires for the user's email address, which is marked as voluntary

Figure 2.3 A screen-shot of one of the survey questions.

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31 and is provided in case further questions towards their responses are needed. Question twelve acts as a form of self-evaluation, asking the respondent to rate their level of competence, to gather more information about the survey respondents’ level of expertise. The question answers are pre- determined, and the user can choose from:

• I am an expert on this topic

• I have good knowledge of this topic

• I have some knowledge/I am new to this topic

• I have no knowledge concerning this topic

• Other (write in text-field)

The survey concludes with an optional comment field.

2.3.3 Deriving the Indicator Weights

Twenty-five people of various affiliations completed the survey, most of them working for different levels of government, as seen in table 2.13. Regarding the self-evaluation of skill level (table 2.14), the level of expertise is quite high, with most people possessing good knowledge of the topic.

Table 2.15 offers an overview of the results from the indicator related survey questions. It provides the mean value for each indicator, as well as the standard deviation, which can be used to examine the variation of answers given.

Table 2.13 Overview of the survey response affiliation. Table 2.14 Survey respondent self-evaluation.