StreetSeen: A Tool for Analyzing Visual Preferences
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StreetSeen http://streetseen.osu.edu provides users with a tool to understand visual preferences through pairwise surveys. This presentation demonstrates a study of pedestrian preferences for intersections.
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StreetSeen: A Tool for Analyzing Visual Preferences
- 1. 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 Conference October 30, 2014 Philadelphia
- 2. Introduction AIM: 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 safety Understanding intersection characteristics can lead to street design that is preferred by pedestrians.
- 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 the Ohio State University campus. Images were categorized based on specific intersection attributes.
- 5. Variables of Interest Crosswalk Type of intersection Condition of road Number of lanes Traffic control devices Crossing aids Vehicles visible Pedestrians present Bicycles present Distance crossed Stopline setback Curb cuts/ramps Condition of crosswalk Speed limit
- 6. Respondents Students 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%
- 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 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.
- 11. 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
- 12. 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
- 13. Conclusions The 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.
- 14. 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 intersections that are preferred by pedestrians.
- 15. Future Work Analyze pedestrian experiences – ie length of walking per day Aiming larger samples from different locations to provide a more robust study. Testing preferences for other visual preferences.
Notas del editor
- 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.
- 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.