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.

IntroductionAIM: 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.

MethodsUsed 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.

Sample Snapshot

Variables of InterestTraffic on street (including parked and moving vehicles)Parking Roadway surface condition Roadway surface material Roadway grade Presence of pedestriansPresence of bicyclists

Land use Streetscape Number of lanes Presence of bicycle laneSidewalk Presence of traffic calming devices

RespondentsStudents enrolled and active in TechniCity (a massive open online course) were invited to participate in the StreetSeen survey.

Image PreferencesImages 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.

Sample of Favorite Images

Sample of Least Favorite Images

Choice ModelsChoice 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.

Model Results

Model Results, Cont’d.

Model Results, Cont’d.

Model with Region Specific Interactions (*)

*Selected results. As compared to N. America as the base case.

ConclusionsThe 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.

ContributionsThis 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.

Future WorkIncluding 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.

http://streetseen.osu.edu

Backup Slides

Variables of InterestTraffic on street (including parked and moving vehicles)

none

1-2 vehicles visible

3-5 vehicles visible

6-9 vehicles visible

10+ vehicles visible

Parking no on-street parking

parallel parking on one side

parallel parking on both sides

pull-in parking

parking lot

Roadway surface condition excellent

good

poor

Roadway surface material asphalt

concrete

brick

Variables of InterestRoadway 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)

manufactured home park

rural residential (homes are widely spaced apart)

rural commercial (businesses are

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)

mixed use (a mix of uses are visible)

high density (high rise buildings)

Variables of InterestStreetscape

no trees

street trees

trees set back from roadway

dense trees

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)

one way

Presence of bicycle laneSidewalk

none

one side

both sides

Presence of traffic calming devices

School crosswalk, textured crosswalk, traffic circle, speed humps

Win-Loss Ratios, Q Score