StreetSeen: Factors Influencing the Desirability of a Street for Bicycling
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In what is one of the first visual preference surveys using Google Street View through a free tool StreetSeen (http://streetseen.osu.edu), adult students viewed a series of paired slides of image of city streets. Participants were asked to choose which image from the pair they preferred based on which street they would prefer to ride a bicycle. Subsequent analyses showed that differences in continent of the respondent impact preferences. This research demonstrates the extent to which certain segment-level factors such as presence of trees along the street, width of the street, presence of sidewalks, and other features are preferred using discrete choice models. The models reveal that increasing vehicle traffic, number of lanes, streetscapes with dense trees, and presence of parking lots decrease the probability of being chosen. Having sidewalks, presence of pedestrians, trees set back from the street, and traffic calming devices are positively associated with respondents’ preferences. The results related to trees may relate to perceptions of safety. For example, dense trees close to a street may limit visibility along a roadway. The models also reveal significant differences in preferences based on respondents’ locations. We conclude that this method is effective in capturing information about bicycling preferences. The survey methodology and analysis techniques introduced in this study can help city planners design streets that are preferred by bicyclists.
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StreetSeen: Factors Influencing the Desirability of a Street for Bicycling
- 1. StreetSeen: Factors Influencing the Desirability of a Street for Bicycling Jennifer Evans-Cowley & Gulsah Akar, City and Regional Planning, The Ohio State University TRB 93rd Annual Meeting January 12-16, 2014 Washington, D.C.
- 2. Introduction AIM: understand the street characteristics that are most important to support cycling Bicyclists face various choices of links to travel from their origins to destinations. Street characteristics contribute to individuals’ bicycling choices Understanding street characteristics can lead to street design that is preferred by bicyclists.
- 3. Methods Used Free Tool: http://streetseen.osu.edu Anyone can use to create, collect data, and analyze a pairwise visual survey using geo-tagged images from Google Street View Images from Columbus, Ohio, metropolitan area. Images were categorized based on specific segment-level attributes.
- 5. Variables of Interest Traffic on street (including parked and moving vehicles) Parking Roadway surface condition Roadway surface material Roadway grade Presence of pedestrians Presence of bicyclists Land use Streetscape Number of lanes Presence of bicycle lane Sidewalk Presence of traffic calming devices
- 6. Respondents Students enrolled and active in TechniCity (a massive open online course) were invited to participate in the StreetSeen survey.
- 7. Image Preferences Images scored based on the fraction of times that they were selected over other images, correcting by the “win” and “loss” ratios of all images with which they were compared.
- 8. Sample of Favorite Images
- 9. Sample of Least Favorite Images
- 10. Choice Models Choice models are estimated to analyze the effect of each street feature on individuals bicycling choice. As each observation is the choice between two images, binary logit models are estimated taking into account the characteristics of both chosen and not chosen images.
- 11. Model Results
- 14. Model with Region Specific Interactions (*) *Selected results. As compared to N. America as the base case.
- 15. Conclusions The models reveal that increasing vehicle traffic, number of lanes, streetscapes with dense trees, and presence of parking lots decrease the probability of being chosen. Having sidewalks, presence of pedestrians, trees set back from the street, and traffic calming devices are positively associated with respondents’ preferences. The models also reveal significant differences in preferences based on respondents’ locations.
- 16. Contributions This work provides a mechanism to understand the tradeoffs among various attributes in a clean, quantitative framework. The survey methodology and analysis techniques introduced in this study can help city planners design streets that are preferred by bicyclists.
- 17. Future Work Including other segment-level factors. Including questions regarding respondent specific factors which are known to affect cycling decisions (for instance being a beginner, intermediate or expert cyclist, frequency of biking, etc.) Aiming larger samples from different locations to provide a more robust study. Testing preferences for walking along a street.
- 19. Backup Slides
- 20. Variables of Interest Traffic on street (including parked and moving vehicles) parking lot Roadway surface condition excellent none good 1-2 vehicles visible poor 3-5 vehicles visible Roadway surface material 6-9 vehicles visible asphalt 10+ vehicles visible concrete Parking no on-street parking parallel parking on one side parallel parking on both sides pull-in parking brick
- 21. Variables of Interest Roadway grade flat and straight hilly and straight (where a grade change is clearly visible) flat and curved (where a curve in the roadway is clearly visible) Presence of pedestrians Presence of bicyclists Land use vacant/not visible (no structures are visible from the street view) widely spaced apart) suburban residential (homes have a 25 foot are larger setback) suburban commercial (strip commercial) in-town residential (single family homes that are close together) medium density residential (apartments and townhomes) medium density commercial/industrial (businesses are located close together) manufactured home park mixed use (a mix of uses are visible) rural residential (homes are widely spaced apart) high density (high rise buildings) rural commercial (businesses are
- 22. Variables of Interest Streetscape no trees one way Presence of bicycle lane street trees Sidewalk trees set back from roadway none dense trees one side Number of lanes Special Road type alley narrow two way (a narrow roadway, without markings, typically in a residential area that is intended for two way traffic) both sides Presence of traffic calming devices School crosswalk, textured crosswalk, traffic circle, speed humps
- 23. Win-Loss Ratios, Q Score
Notes de l'éditeur
- Increasingly cities are promoting bicycling for both recreation and daily transport. Cities have pre-existing street networks that may or may not be able to accommodate additional bicycling infrastructure. Cities are heterogeneous and vary in the suitability of roadways for the purposes of bicycling. Bicyclists face various choices of links to travel from their origins to destinations. Cities may offer different combinations of bicycle infrastructure, such as dedicated multiuse paths, bicycle boulevards, roads with sharrows, and bicycle lanes combined with routes where there is no bicycle infrastructure. For instance, the shortest path could require principally traveling on a high-traffic and high-speed-limit road that has on-street parking. The longest might be a multiuse trail. One other choice may include a blend of primarily residential streets and a multiuse path. It is important to understand the effects of street characteristics that contribute to individuals’ bicycling choices in order to make informed investment decisions and design streets that are preferred by bicyclists.
- There are a variety of methods for measuring attributes in the built environment, such as visual surveys. Visual surveys ask people to rate images on a scale or choose an image over some other paired images (29-42). These visual surveys are intended to capture uniqueness in the built environment.In the past building visual surveys was time consuming and difficult. Over the last few years Google, Nokia, and other companies have undertaken extensive efforts to collect panoramic imagery of streets. This is obtained through multiple directional cameras at a consistent height of approximately 8.2 feet. GPS units capture the positioning (1). second bullet -- Images were immediately eliminated from the study if the conditions were unfavorable, such as a view in the rain or a fuzzy image. third bullet -- For example, a no-traffic condition in a rural, suburban and urban context.
- 59 images in the pairwise survey
- A total of 260 people whose latitude and longitude could be detected participated, contributing 15,759 votes. Each participant contributed an average of 60 votes. After all votes were collected each latitude and longitude was coded to determine the country and continent of each vote using latlong.net. Table 1 shows the distribution of votes across regions. -- Talk about technicity class.
- Top images respondents chose are residential streets with trees along them and a few parked cars.
- The images respondents were less likely to choose were the ones with five or more lanes and significant traffic visible
- The calculated Q scores give information on the most and least desirable streets on which to bicycle, however, they do not reveal information on the effects of each single street feature. For instance, is it the number of lanes that make one street desirable, or the presence of traffic calming devices? Although the answer may be a combination of both, which features have the highest impact and how do these impacts vary across different survey regions? To be able to answer such questions, discrete choice models are estimated.
- Having pedestrians generally increases the probability of choosing an image The effect is less for respondents from Asia and South America. Having sidewalks on one side of the road will increase the choice probability more in Asia. An image with dense trees is less likely to be chosen, and this affects the respondents from Europe even more. the effect is the opposite for respondents from Africa. Respondents from Africa have slight preference for having dense trees. Being close to a parking lot decreases the probability of being chosen, and this effect is significantly higher for respondents from Africa, Asia, and Europe as compared to North America. Having a street with a curve increases the choice probability for North American respondents, and even more for respondents from Australia, while a street with a curve is less likely to be chosen by respondents from Africa.
- This research demonstrates that pairwise surveys can be effective for understanding preferences for bicycling.
- It provides a virtually limitless number of images based on a limitless set of attribute data that can be collected from snapshot streetview images. This provides an innovative contribution that simplifies the process for researchers.
- Including other segment-level factors.Testing preferences for walking along a street. Including questions regarding respondent specific factors which are known to affect cycling decisions (for instance being a beginner, intermediate or expert cyclist, frequency of biking, etc.) may enhance future studies by revealing how effects of certain street features vary across individuals with different characteristics. Studies could be undertaken with homogeneous samples with equal familiarity with the kinds of situations represented to understand how people who are familiar respond. Additionally, using multiple evaluators of each image to rate each attribute prior to deploying the survey would address the reliability and validity of the categorization of each image’s attributes. Future studies could include larger samples of people from different locations to provide a more robust study.Future studies could also integrate demographic questions and individual perceptions to better understand the respondents including such questions may also help identify the underlying reasons for differences across continents and possibly countries; for instance the preferences of respondents from a country where the bicycling mode share is high and the culture is well-established versus a country where bicycling is perceived as dangerous and not very common. To be able to test the differences across countries, more data will be required to achieve substantial samples from each location.