StreetSeen: Factors Influencing the Perception of Safety at Intersections

Jennifer Evans-Cowley, Gulsah Akar and Brittany Kubinski City and Regional Planning, The Ohio State University

ACSP ConferenceOctober 30, 2014

Philadelphia

IntroductionAIM: understand the intersection characteristics that are most important to pedestrian safety

Pedestrians face a mix of intersection conditions as they walk to a location.Intersection characteristics contribute to individuals’ perception of safetyUnderstanding intersection characteristics can lead to street design that is preferred by pedestrians.

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 the Ohio State University campus. Images were categorized based on specific intersection attributes.

Sample Snapshot

Variables of InterestCrosswalkType of intersectionCondition of roadNumber of lanesTraffic control devicesCrossing aidsVehicles visiblePedestrians presentBicycles presentDistance crossed

Stopline setbackCurb cuts/rampsCondition of crosswalkSpeed limit

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

Africa 6 76 2.8%Asia 19 224 8.2%Australia 6 69 2.5%Europe 45 612 22.6%North America 106 1,480 54.6%

South America 11 140 5.2%

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 intersection feature on individuals 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

Pavement markings (Base case: No markings)

Ladder Markings, faded 1.400 8.24 30.22

Ladder Markings, Well Maintained 1.981 11.92 37.88

Parallel Markings, faded 0.210 1.78 5.23

Parallel markings, well maintained 0.792 6.86 18.81

Stop sign present 0.506 5.09 12.38

Number of Lanes (Base case 1 lane)

2 lanes -0.705 -4.64 -16.92

3 lanes -0.340 -1.69 -8.4

4 lanes -1.310 -8.31 -28.75

Curb extensions present 0.440 1.69 10.82

Pedestrian crossing signal present 0.660 4.92 15.92

Model Results, Cont’d.

Cars Visible (base case no cars)

Low (2 or fewer) -0.545 -5.72 -13.3

Medium (3-5 cars) -0.867 -8.6 -20.4

High (6-9 cars) -0.546 -4.0 -13.32

Pedestrians present 0.280 3.23 6.96

Bicycles Present 0.344 2.94 8.51

ConclusionsThe models reveal that increasing vehicle traffic and number of lanes decrease the probability of being chosen.Having pavement markers, stop signs, pedestrian crossing signals, presence of pedestrians and cyclists are positively associated with respondents’ preferences.

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 intersections that are preferred by pedestrians.

Future WorkAnalyze pedestrian experiences – ie length of walking per dayAiming larger samples from different locations to provide a more robust study.Testing preferences for other visual preferences.

http://streetseen.osu.edu