Even though he wanted to bike commute from his Capitol Hill home to the University of Washington, Jared Hwang often took transit because he struggled to find a good bike route. Apps like Google Maps and Strava might suggest hilly, busy streets simply because they have bike lanes. He even headed to Reddit to crowdsource ideas.
“I was like, surely, this cannot be the best way to do things,” said Hwang, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “This data is out there. We know where bike lanes are, what the roads are like, what the speed limits are. We should be able to easily access all this information at once.”
So Hwang and a team of UW researchers built BikeButler, a demo web app that lets users find personalized bike routes in Seattle. Cyclists plug in their origin and destination — just like in other mapping apps — and can then create personalized routes by adjusting eight sliders.
For instance, a cyclist can move a slider between “low speed limits” to “high speed limits” or between “lots of greenery” to “no greenery.” The app generates route options based on those preferences. Users can then flip through images from segments of the routes and weigh the pros and cons of taking different streets. Notes on each segment tell users how it aligns with their preferences — for example, a three-block stretch might have low speed limits and good roads but no bike lanes.
The team presented its research April 17 at the Association for Computing Machinery Conference on Human Factors in Computing Systems in Barcelona.
Researchers initially worked with four participants to understand how cyclists tend to plan their routes. Based on that, they built a prototype of BikeButler. For the basic street layout and other info, they pulled data from OpenStreetMap and government data sets. But those didn’t have information on more subjective qualities.
For those, researchers turned to Google Street View. They used a visual language model, or VLM — a type of artificial intelligence — to analyze street images and rate subjective attributes like greenery and pavement quality. The team had the VLM rate the level of greenery on streets and then compared this with two researchers’ ratings. The humans agreed with each other about as much as they agreed with the VLM — about 60% of the time. Future research might try to gather individual users’ greenery preferences to offset this discrepancy.
Once they’d mapped most of Seattle, the team tested the prototype with 16 participants.
“Overall the response was really positive,” Hwang said. “We found that people do, in fact, have contextual preferences. A cyclist riding for fun on a Saturday might want a safer, greener route compared with their fast work commute. People intuitively know this, but it hadn’t been established through research.”
Researchers say future work might integrate feedback from the user study, such as the ability to drag routes to change them slightly and an option to take fewer turns. The team is currently studying how to quantify cyclists’ preferences around intersections and turns.
The researchers note that the quality of BikeButler’s recommendations is constrained by the recency and accuracy of the data it uses. For instance, a new bike lane might not yet appear on a map, or it could appear in OpenStreetMap but not Google Street View. Also, since the team planned this as a proof of concept, BikeButler is limited to Seattle, though it could be expanded to other areas.
“I’m a lifelong biker and bike commuter,” said senior author Jon Froehlich, a UW professor in the Allen School. “What excites me most about Jared’s work is how it points to a future where we receive route choices individualized to our preferences. So whether I’m biking with my two young children, or riding for groceries, I can find a route for that context.”
Co-authors include John S. O’Meara, a student at Issaquah High School and intern in the Allen School; Zeyu Wang, a UW doctoral student in urban design and planning; and Jasmine Zhang, a UW student in the Allen School. This study was supported by the National Science Foundation.
For more information, contact Hwang at jaredhwa@cs.washington.edu.