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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. 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. 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. 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.
  4. 4. Sample Snapshot
  5. 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. 6. Respondents Students enrolled and active in TechniCity (a massive open online course) were invited to participate in the StreetSeen survey.
  7. 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. 8. Sample of Favorite Images
  9. 9. Sample of Least Favorite Images
  10. 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. 11. Model Results
  12. 12. Model Results, Cont’d.
  13. 13. Model Results, Cont’d.
  14. 14. Model with Region Specific Interactions (*) *Selected results. As compared to N. America as the base case.
  15. 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. 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. 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.
  18. 18. http://streetseen.osu.edu
  19. 19. Backup Slides
  20. 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. 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. 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. 23. Win-Loss Ratios, Q Score

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