In 2024, the Santa Barbara Council of Governments won a grant from Caltrans to use artificial intelligence to design a countywide bike map in collaboration with UC Santa Barbara and Simon Fraser University. The grant promised to teach AI to map bicycle infrastructure and develop a universal, regional wayfinding plan and would “revolutionize bicycle infrastructure mapping” according to a statement.

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Now that revolution will be put to the test.

This week, the Santa Barbara County Association of Governments (SBCAG) launched the map in English and Spanish and is seeking feedback from bicyclists to test the map’s accuracy.

“The map is only as good as the information behind it,” SBCAG Transportation Planner Peter Williamson said in announcing the project. “The people who know our streets and neighborhoods best are the people who live, work and ride here.”

The interactive map uses artificial intelligence trained on Google Street View imagery and OpenStreetMap data to estimate how comfortable people will feel bicycling on roads throughout Santa Barbara County. Riders are being asked to review the map and report missing bike lanes, incorrect route information, inaccurate comfort ratings, and other errors before the public comment period ends on August 14.

Unlike traditional bicycle maps that simply show where bike facilities exist, the AI model attempts to classify streets based on rider comfort by analyzing roadway characteristics including bike infrastructure, traffic volumes, speed limits, lane widths, and other features visible in street imagery and mapping data.

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For transportation planners, that could make the tool useful for identifying where riders are likely to feel comfortable—or where they are likely to avoid riding altogether. But only if the model matches reality.

As transportation agencies increasingly experiment with artificial intelligence, Santa Barbara’s project may become an early test of where AI can genuinely improve bicycle planning—and where it still needs human judgment.

The SBCAG project is expected to wrap-up, including publishing its final maps, in summer 2027.

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