Mapping  Bicyclists’  Experiences  in  Copenhagen

Bernhard Snizeka, Thomas Sick Nielsenb, Hans Skov-Petersena a Department of Urban and Landscape Studies, Forest and Landscape, University of Copenhagen, 1958 Rolighedsvej 23

b Department of Transport Technical University of Denmark, Bygningstorvet 116B, 2800 Kgs. Lyngby

Abstract

This paper presents an approach to the collection, mapping, and analysis of cyclists’ experiences. By spatially relating located experiences to the availability of bicycle facilities and other aspects of the urban environment, their influence on cyclists’ experiences can be analysed. 398 cyclists responded and sketched their most recent cycle route and a total of 890 points to locations along the route where they had had positive and negative cycling experiences. The survey was implemented as an online questionnaire built on Google Maps, and allowed up to three positive and three negative experience points to be mapped 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. Thereby urban spaces can be mapped according to the potential promotion of positive or negative experiences. Further, the method might be applied to assess the effect of proposed changes to the urban design in terms of cyclists’ experiences.

Statistical analysis of the location attributes, traffic environments and conflicts, bicycle facilities, urban density, centrality, and environmental amenities indicates that positive experiences, or the absence of negative experiences, are clearly related to the presence of en-route cycling facilities, and attractive nature environments within a short distance of large water bodies or green edges along the route.

Keywords: mapping; experiences; cycling; urban planning; transport planning; traffic engineering

1. Background  

Encouraging motorists to stop using the car for daily urban trips in favour of the bicycle is one of the major challenges cities currently face in order to enhance the liveability of cities (Ewing and Cervero 2010). However, convincing motorists to choose the bicycle as the major means of transport is a significant challenge. Understanding the way cyclists perceive their environment as well as mapping and analysing these perceptions could be the key to designing positive cycling experiences which may well encourage more people to travel by bicycle, thereby contributing to sustainable urban environments.

Recently, several studies have treated cycling as a special phenomenon, which differs both from motorised as well as pedestrian traffic in a number of ways (Forsyth and Krizek 2011), (de Geus et al. 2008), (Bonham and Suh 2008), (Raford, Chiaradia, and Gil 2005). However, only a few of these studies deal with the quality of cycling in relation to the cyclists’ surroundings (Heinen, Maat, and van Wee 2011).

Technology-supported mapping of urban experiences

Affective computing systems are a recent development within emotional mapping. Nold (2009) developed an electronic device, which constantly measures a respondent’s state of

arousal via a galvanic skin device and his/her current location while walking in a city. By storing the measurements in a spatial database and overlaying several respondents’ tracks, unpleasant locations can be identified. In this way, a map can be constructed, which Leahu & Schwenk (2008) refer to as an arousal map that visualises the city’s psychogeography. This map could provide important background information for city planning and could be applied during the knowledge-gathering phase of the planning process. In Nold’s context, emotional maps are generated by means of both stated (semantic mapping of experiences) and revealed (geospatial recording of skin resistance) preference after having completed a particular trip. Zeile et al. (2009) developed a methodology for constructing what they call emotional neighbourhood portraits by applying a similar approach to Nold’s. Rantanen & Kahila (2009) presented Internet-based methods to gather, examine, and analyse local knowledge within what they called SoftGIS. Positive and negative quality spots were analysed regarding hotspots and clustering, which were used as the foundation for participatory planning processes. Emotional mapping and SoftGIS approaches are, however, still new in the context of bicycling.

Cyclists’ experiences

Cyclists’ experiences differ to some extent from those of motorists on the one hand and pedestrians on the other and are influenced by several factors: The existence and design of cycling facilities (Sener, Eluru, and Bhat 2009) (Dill et al. 2008) play a great role in both attracting cyclists in the first place and how they subsequently perceive safety and appreciate the route. Furthermore, other roadway characteristics such as physical characteristics, on-street parking and operational characteristics (Sener, Eluru, and Bhat 2009) as well as land-use (Winters and Cooper 2008) and proximity to retail establishments (Krizek and Johnson 2006) also contribute to the overall cycling experience.

In general, the literature on cycling experiences is limited. For example, the following themes have not been studied at all: spatially explicit experiences relating to land use, traffic intensity, pollution and noise, the number of pedestrians and cycling infrastructure other than bicycle paths and lanes. As these themes are considered important components of experience, further research is needed. This study conducts a geospatial approach to the mapping and analysis of cyclists’ experiences. The overall goal of this article is to determine whether the characteristics of elements and locations of the city are correlated to the likelihood of having a positive or negative cycling experience?

2. Methodology  

In order to build spatial explicit models of an individual’s perception of his/her surroundings, data collection methods had to be developed. Within the literature, quite a diverse range of data collection methods can be found. Methods, where respondents actually draw their routes, be it on paper or via computational representations, are quite rare: Raford and Chiaradia (2005) who asked respondents to draw routes on paper being one example. A rather uncommon, specialised and distributed system for real time data acquisition which makes it a tool for revealed preference studies is described in (Eisenman et al. 2009), whereby a sensing system collects data on pollution level, allergen levels, noise levels and the roughness of terrain. Parkin et al. (2007) employ video clips which are shown to respondents as a means of conveying information on different road environments.

In this section, we describe a methodology that through collecting data leads us to an understanding of the relations between stated experiences and urban elements. An interactive, web-based questionnaire was developed. Up to three locations, a number, which was found a reasonable number for the respondents to end answering the questionnaire, of positive or negative experiences, as well as the route on which they were experienced, could be entered. Post processing and data cleansing were applied to the raw data. Every point was then related to elements of the urban environment as well as to selected route metrics. Finally, in order to establish which urban features resulted in significant positive or negative

experiences, every point, positive or negative, was related to the locations on the routes, which did not generate positive or negative experiences. In the next section, we present the map-based questionnaire, explain the process of data cleansing and processing and justify our selection of urban elements and route metrics.

2.1 The  online,  map-­‐based  questionnaire  

The collection of spatial data via online media is becoming more and more common within research. Simple, postcode-based collection was soon replaced by online questionnaires that were built around or contained map-based components (Bearman and Appleton 2012). Today, the availability of Application Programming Interfaces (APIs) enables software developers to build map applications based on different map technologies such as Google Maps or maps from OpenStreetMap1.

Figure 1: Part of the online, map-based questionnaire in Danish. Translation: ‘Positive and Negative experiences on your bicycletrip’; 1. Draw your route; 2. Pinpoint positive experiences; 3. Pinpoint negative experiences; 4. Save and exit. A respondent's route is shown in blue, while two positive experiences are indicated by green markers and three negative experiences are indicated by red markers, one of which is described.

A web and map-based application based on the Google Maps API was designed and implemented in connection with a comprehensive online questionnaire survey about cyclists’ preferences conducted in Copenhagen2. In this questionnaire, respondents were asked to, 1) draw their most recent route, 2) designate three locations where they had had positive experiences and, 3) identify three locations where they had had negative experiences. In addition, the respondents had the opportunity to classify the locations by selecting from a list of given classes and finally adding comments. In total, 554 positive and negative locations were registered by the system and stored for further data processing, which is described below. Responses containing fewer than three good or/and bad points were accepted. The respondents were prompted for located experiences indicating the focus on features of the 1 “OpenStreetMap is a free editable map of the whole world. It is made by people like you” (Anon. 2012). 2 This questionnaire was developed within the bikeability project, see more at http:/www.bikeability.dk

urban environment, but all experiences could be recorded – and a possibility for adding text explanations was part of the survey.

2.2 Data  processing  A series of processing steps had to be performed so that the raw data, which had been generated by the web application, could be analysed.

A quick visual sweep showed that some of the lines drawn were apparently the result of the respondent’s inability to operate the web application. These lines were either very short, did not, partially or as a whole, follow the transportation network or their segments overlapped each other to a certain extent.

The routes which obviously had been erroneously entered were removed from the dataset as well as the points related to the routes.

Figure 2: Example of an erroneously drawn route (blue)

Figure 3: Erroneously mapmatched route (red line), respondent-drawn route (green dots), OSM transport network

The area of analysis was defined to be composed of the municipalities of Copenhagen and Frederiksberg, Denmark – the latter being totally enclosed by the municipality of Copenhagen. Following the removal of erroneously entered lines those lines exceeding the municipal boundary were cut to the boundary, those being completely outside of the municipality were removed from the dataset.

Map matching of lines

As the precision of the drawn lines varied significantly due to the respondents’ differing abilities, each route was projected onto the road network, a procedure called map matching. A road network, which had been extracted from OpenStreetMap, was used. The resulting routes, which were identical to parts of the transport network, were then ready for further analysis. Data describing cycle-related infrastructure (bicycle paths, segregated cycle paths or no bicycle infrastructure at all) were extracted from OpenStreetMap. Most routes returned by the map matcher were usable, although some, especially those on roads with several lanes (see Figure 3), were returned erroneously. To ensure the highest quality possible, each route was inspected and any apparent map matching errors were compensated for.

Processing the points

The dots entered by the users were then projected onto their respective routes. Dots with a distance of over 100 metres to their respective projection onto the road network were deleted from the dataset.

A set of points was generated in order to compare points of experience – whether positive or negative – with points of non-experience and thereby investigate the significance of the experiences in relation to the urban elements described in section 2.3 below. These 86,332 so-called non-experience points were laid out on the spatially corrected routes with a distance of 50m.

Values pulled from a series of relevant GIS layers, discussed below, were then attributed to the data layer containing the experience points.

In the following section, we discuss each of these layers by referring to the existing literature in the field in order to justify the existence and relevance of each factor and to develop a framework of how these indicators can be related to the recorded experiences.

About 4,700 respondents completed the questionnaire. The interactive map questionnaire presented in the current paper was only shown to about 66% of the respondents. 625 routes were drawn and 1,677 dots were entered into the system. Erroneously drawn routes were removed which left 554 routes. After omitting the routes that lay outside the municipal boundaries of Copenhagen and Frederiksberg, only 409 routes remained. After having performed map matching and manual correction, 398 routes were left in the dataset. The number of points decreased to 890 after removing those which did not have corresponding routes and those that were located further than 100m from their respective routes as well as those which lay outside the city boundary. In total, 65 % of routes and 57% of the points were valid. The number of respondents that did not complete the map questionnaire was not recorded.

It has to be noted that the method presented here might be slightly skewed towards an emphasis on the positive results. On their daily selection of routes, bicyclists are likely to avoid negative experiences. Thereby the registration of negative experiences along the chosen route only relates to experiences on routes perceived to be apt to travel on. Overcoming this shortcoming would require a methodology where bicyclists experienced or rated ‘un-expereinced’ places.

No additional information like the time of day the trip was begun at or its duration was recorded. Therefore differences in perception of the urban space between night and day were disregarded from.

2.3 A  framework  for  relating  cyclists’  experiences  to  urban  phenomena      The objective of this section is to discuss a series of indicators and to relate these to elements of the urban space as well as to route characteristics. Within the analysis, a series of thematic GIS layers were produced which delivered values for the dots. In addition, a series of calculations regarding directionality and distance were performed.

a) Cycling facilities and level of service for cyclists

Roads with cycle facilities

Sener et al. (2009) incorporated two variables related to cycling facilities: on-road cycle lanes and shared roadways. Based on the cycling facilities available in Copenhagen, we have classified the cycling infrastructure in the following manner: street with no cycling facilities; cycle path by the side of the road (separated from motor traffic by a curb); cycle lane on the road (separated from motor traffic by markings on the road); off-road path exclusively for cyclists, and finally off-road path shared by cyclists and pedestrians. The road network and cycle facilities were extracted from OpenStreetMap.

Distance to nearest cycle rack

In Copenhagen, about 4,100 cycle racks are distributed throughout the city. The importance of cycle facilities at travel destinations to cyclists has been highlighted in several studies (Heinen, Maat, and van Wee 2011). Cycle racks may provide a positive experience as they offer cyclists a place where they can safely store their often expensive cycles.

Number of cycle racks within 100 metres

The provision of cycle racks, as previously discussed, improves cycle infrastructure. Therefore, they make a positive contribution to cycle infrastructure and are regarded as an initiator of positive experiences. Geodata, which described cycle racks, originated from the municipality of Copenhagen and was downloaded from the municipality's homepage3. There were between zero and 32 bicycle racks within a distance of 100 metres to the experience point.

Distance to the nearest traffic lights

Traffic lights are a source of delay, although they also increase road safety for cyclists (Zeile, Höffken, and Papastefanou 2009; Rietveld and Daniel 2004). In this study, they are considered as sources of delay and are therefore proxies for negative experiences.

b) Street types

Street types, as a proxy for traffic volume and speed, perceived safety, noise and pollution, have a significant impact on route choice and how cyclists value their environment and the trips themselves.

Within the current context, this variable was split into the following eight classes: primary roads, secondary roads, residential streets with detached or semi-detached housing, residential streets with multi-storey housing, cycle paths exclusively for bicycles, mixed paths for cyclists and pedestrians, and others. Data for this variable were also taken from OpenStreetMap, as this data source was the most up to date.

Distance to the closest group of bus stops

Bus stops and bus stop groups are part of the urban public transport system. In the context of this study, bus stops are regarded as sources of negative experiences as pedestrians crossing the cycle lane may pose a threat to cyclists and may also delay them.

Distance to nearest intersection

The distance to the nearest intersection tells something about the position of the current experience point to a potential obstacle on a cyclist’s route. Intersections can act as temporal obstacles in that the cyclist has to give way to vehicles or other cyclists who are approaching from the left or has to wait for a green light. In this context, as the direction of the drawn routes was impossible to establish, the relation of the experience point to the nearest intersection was not given, i.e. one could not determine whether the intersection had just been passed or was being approached. In this study, the distance to the nearest intersection varied from 1 metre to about 400 metres. Intersections are regarded as contributing to negative experiences. The data for the calculation of intersections was extracted from OpenStreetMap.

c) Urban density and centrality

3 http://www.kk.dk/Borger/ByOgTrafik/CyklernesBy/CykelFaktaOgViden/Cykeltal/CykelData.aspx

Distance to town hall

The distance to the town hall was taken as an indicator of the traffic environment in general. Copenhagen, with its medieval urban core, exhibits different traffic environments. Within the city centre, and thereby close to the town hall, a net of small streets displays the typical pattern of medieval cities. Here, cycle infrastructure is rare, combined with low vehicle speeds due to the narrowness of the streets. Around the city centre, Copenhagen has a typical 1900s block structure with a high level of cycle infrastructure. At the fringe of the city, single housing is concentrated around roads with cycle infrastructure at collector roads. The streets within the housing areas are characterised by low traffic speeds. In this study, the distance from the experience points to the town hall varied from about 50 metres to 8000 metres.

Number of companies within a distance of 100 metres to the experience point

Most companies have more than one employee and therefore create urban life, i.e. a higher number of pedestrians in the vicinity of their location. These pedestrians might cross the cycle infrastructure to and from bus stops or parked cars, while they may also be in search of a shop and thereby pose a potential threat to the cyclists. The data for this indicator was extracted from The Central Business Register (CVR)4. The number of companies ranges from 0 to 753.

Number of retail units within a distance of 100 metres to the experience point This measure is a subset of the one above. Retail generates urban life and thereby people who will cross the cycle infrastructure and therefore it is considered to be a negative influence on cyclists’ overall experiences. The number of retail units ranged between 0 and 61.

d) Water and green areas

Distance to closest water or green area

In the current study, the location of water and green areas in the vicinity of cycle infrastructure is regarded as something, which contributes to a positive experience. This indicator is composed of the distance from the road’s centreline to layers describing parks, cemeteries, heaths, forests and commons from the topographical map of Denmark, TOP10DK5.

Percentage of green of route segments

In order to calculate this indicator a buffer of 50 metres around each road segments was constructed and within the percentage of forests, parks, cemeteries, heaths and wetlands calculated.

e) Route-related measures

Deviations from the direct line

By measuring the distances and the angles between route segments and the direct line, i.e. the line which the crow flies between the origin and the destination, one gains information about the directness of the route. This measurement is seen as an indicator for

4 See http://www.cvr.dk 5 See http://www.geodatabiblioteket.dk/images/stories/specifikationer/doc/spec_320.pdf

how complex a route is and thereby indicates whether the cyclist went straight towards the destination or chose to turn left or right several times, thereby taking detours resulting in delays. The importance of directness for cyclists’ choice of route has been documented in several studies (Dill 2009; Aultman-Hall, Hall, and Baetz 1997).

The distances to the direct line varied between 0 and about 5,000 metres.

The angle between the current segment and the line from the experience point towards town hall.

The Town Hall is a proxy for the city centre and this variable explains the direction of the road segment upon which the experience point is located. The lower the value, the more directly the cyclist approaches or travels away from the city centre. Higher values (up to 90 degrees) indicate that the current segment of the route leads the cyclists in a radial direction. The role of directionality vis-a-vis the urban centre for urban experiences and spatial behaviour have been highlighted in several studies within urban design and geography including Kevin Lynch’s study of the city image (Lynch 1960).

3. Results  

The experience data obtained from the web-based, map-enabled survey instrument can be mapped as an independent basis for assessment and analysis. The addition of geographical data layers to experience points and route segments allows further statistical analysis of the environmental experiences. Maps and environmental correlates of cyclists’ experiences are presented in the following.

Figure 4: Geographic distribution of positive (to the left) and negative (to the right) spots; the municipalities of Copenhagen and Frederiksberg are shown in grey.

Figure 4 presents the distribution of the positive and the negative experiences reported by the survey participants. It should be noted that most of the dots follow roads or cycle tracks with a high load of cycle traffic, and reflect the spatial distribution and largely radial character of

cycle travel in the area. Clusters of points are observable in some areas, e.g. along a recently established green trail on derelict railroad terrain, on most bridges across the harbour or the lakes separating the medieval city centre from the rest of the city; as well as along some circumferential routes.

The statistical analysis of environmental correlates was based on a logistic multinomial regression model explaining the probability of a positive experience versus no experience, and the probability of a negative experience versus no experience. The model was fitted based on a model search among the variables presented in the methodology section under continuous testing for multi-collinearity and robustness of the result. The optimal model was chosen based on an assessment of the level of explanation, conceptual soundness and statistical significance. Variables with non-significant effects were excluded from the model.

   B   Exp(B)   Sig.  

Positive  expe

rience  

Intercept   -­‐2,519    

0,000  

Primary  road  (0,1)   -­‐0,954   0,385   0,000  

Secondary  road  (0,1)   -­‐0,510   0,600   0,000  

Residential  street  (0,1)   -­‐0,008   0,993   0,961  

Cycling  facility  en  route  (0,1)     0,436   1,546   0,001  

Cycling  facility  is  a  separated  cycle  path  (0,1)   0,400   1,492   0,004  

Distance  to  intersection  (ln)   -­‐0,034   0,966   0,444  

Distance  to  signalled  intersection  (ln)   -­‐0,048   0,953   0,291  

Distance  to  bus  stop  (ln)   0,179   1,197   0,004  

Companies  within  100m  (ln)   -­‐0,113   0,893   0,000  

Distance  to  town  hall  (ln)   -­‐0,177   0,838   0,008  

Distance  to  water  body  (ln)   -­‐0,162   0,851   0,000  

Percentage  of  green  edge  on  route  segment  (ln)   0,006   1,006   0,781  

Distance  to  ‘flight  of  crow’  route  (ln)   -­‐0,115   0,891   0,000  

Negative  expe

rience  

Intercept   -­‐0,732    

0,342  

Primary  road  (0,1)   -­‐0,729   0,482   0,020  

Secondary  road  (0,1)   -­‐0,278   0,757   0,083  

Residential  street  (0,1)   -­‐0,492   0,611   0,008  

Cycling  facility  en  route  (0,1)     -­‐0,398   0,671   0,006  

Cycling  facility  is  a  separated  cycle  path  (0,1)   0,199   1,221   0,459  

Distance  to  intersection  (ln)   -­‐0,166   0,847   0,004  

Distance  to  signalled  intersection  (ln)   -­‐0,235   0,791   0,000  

Distance  to  bus  stop  (ln)   -­‐0,128   0,880   0,058  

Companies  within  100m  (ln)   0,085   1,089   0,080  

Distance  to  town  hall  (ln)   -­‐0,263   0,769   0,001  

Distance  to  water  body  (ln)   -­‐0,047   0,954   0,317  

Percentage  of  green  edge  on  route  segment  (ln)   -­‐0,097   0,908   0,003  

Distance  to  ‘flight  of  crow’  route  (ln)   -­‐0,033   0,968   0,407  Table 1: Multinomial logistic regression model explaining the probability of positive or negative experience points against all experience spaces/potential experience points derived from the respondents’ cycle routes. B column presents regression coefficients and Exp(B) the corresponding change of odds. (0,1) after the variable name indicate a binary variable. Fully scaled variables are included as Ln transformed variables to account for non-linearity. N= 87222 (of which 554 positive; and 336 negative experiences). Cox & Snell R-Square=0.04; Nagelkerke R-square=0.037; P=0,000 (Chi-square).

The resulting regression model indicates significant correlations between the urban environment factors included in the study, but achieves a limited level of explanation of positive and negative versus no experiences (R-square). The limited ability of the model to

explain the recorded experiences can be explained by the many personal and situational factors of relevance to experiences, as well as the limits of the approach in that it only observes environmental factors and amenities. The environmental correlates are, however, still relevant as significant effects/correlations exist which can form part of the conditions which influence the experiential outcomes (see (Naess 2006)).

The model results for the probability of a positive experience point to a significant contribution from the road environment, cycling facilities, environmental factors, factors that can be interpreted as annoyances and congestion and finally deviations from the most direct route.

Cycling on primary or secondary roads reduced the probability of a positive experience, while cycling with the availability of cycling facilities, especially a separate ‘Copenhagen style’ cycle path, increased the probability of a positive experience. A greater distance to a bus stop is positively correlated with the probability of a positive experience, which reflects the conflict between cyclists and bus passengers crossing the cycle infrastructure. Most major roads in Copenhagen are equipped with bicycle paths adjacent to the sidewalk and conflicts with busses only take place when buspassengers crosses the bicycle paths to enter a bus. The negative correlation displayed for the variable ‘companies within 100m’ expresses density and trip destination density in the immediate environment of the cyclist route – and therefore congestion by all modes of travel and associated conflicts. The negative effect of distance to the town hall appears to be somewhat counter intuitive to this, although the same effect applies to the probability of a negative experience, while both reflect the convergence of most cycle routes in town centres and contribute to the probability of central locations being recorded as negative experiences by the survey participants.

The correlation of distance to large water bodies such as the lakes, which surround central Copenhagen or the harbour, indicates that attractive environments and views make a significant contribution to positive experiences.

The distance to the direct line of the cycle route (as the crow flies) indicates that detours contribute negatively to the probability of a positive cycling experience.

The model results for the probability of a negative experience on a cycle route reflect the reverse probability of a positive experience with some additions and exceptions. As for positive experiences, cycling along a primary road is related to a lower probability of a negative experience, a result that may reflect the general character of these environments as linear connectors, with large volumes of separated traffic. Cycling on residential streets is also negatively correlated to the probability of a negative experience, and in the absence of a similar correlation with the probability of a positive experience; this is likely to reflect lower traffic volumes and speeds. Similarly, the availability of en-route cycling facilities contributes negatively to the probability of a negative experience, but positively to a positive experience, which reflects the importance of comfort and right of way for a positive cycling experience.

The distance to intersections and signalled intersections are both negatively correlated with the probability of a negative experience. Thus, the closer to intersections or signalled intersections, the higher the probability of a negative cycling experience. This very likely reflects the dangers and conflicts encountered at intersections.

The ‘greenness’ of the cycling environment measured as the percentage of green areas on the route segment is negatively correlated with the probability of a negative experience, which together with the correlation between distance to water bodies and the probability of positive experiences, indicates that aquatic and nature environments both contribute to better cycling experiences.

The questionnaire was distributed to cyclists within the municipalities of Copenhagen and Frederiksberg. It should therefore be noted that the analysis presented here is based on cycling routes and their segments within these two municipalities. Factors such as the aquatic/nature environmental cues in particular, or other attributes may be associated differently with cyclists’ experiences in other and especially less urban environments.

4. Conclusion  

The aim of this paper was to map cyclists’ positive and negative experiences and to analyse the correlation of these experiences with environmental qualities and attributes as an input to planning aimed at promoting cycling. Data on cyclists’ routes and experiences were collected based on an online map-based questionnaire, and the locations of positive and negative experiences were statistically compared with locations on the routes without classified experiences.

Statistical analysis of the location attributes, traffic environments and conflicts, bicycle facilities, urban density, centrality, and environmental amenities indicates that positive experiences, or the absence of negative experiences, are clearly related to the presence of en-route cycling facilities, and attractive nature environments within a short distance of large water bodies or green edges along the route.

Factors, which contribute to negative experiences, are bus stops, high traffic densities along the route, as well as signalled and non-signalled intersections. Bus passengers are often in conflict with cyclists, as they have to cross cycle paths to enter buses, while high urban densities imply congestion and related conflicts on the sidewalks as well as cycle paths. The effects of intersections very likely reflect the annoyance of delays as well as the conflicts and dangers associated with the crossings.

It is noteworthy that cycling on large roads is generally associated with a lower probability of any significant experience, positive or negative. In the Copenhagen/Frederiksberg case study area, the large roads make up the main ‘arterials’ of the cycling network and are thus frequently traversed to get from origin to destination by bicycle. Experiences are more likely to take place outside these corridors, probably when the linear and familiar route segments are interrupted and additional features require attention, and especially when approaching the central parts of town.

The results of this study may be applied to develop a cycling environment surface based on predicting cycling experiences from environmental and infrastructure variables. In particular, the mapping of hotspots of certain experiences, both positive and negative, would be interesting for planning purposes. Analysis and visualisation of experience data may aid planning processes and improve the distribution of planning funds.

Computer-based cycle models could also use the study’s results as an input to model experience both on a larger scale as well as on an individual level. Therefore, psychogeographical cross-sections of cyclists’ routes may be very relevant for the improvement of cycle infrastructure.

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