Santa Barbara County Pilots AI for Bike Network Planning

AI-Assisted Bicycle Network Design Takes Shape

Santa Barbara County is at the forefront of using artificial intelligence to reimagine bicycle network planning. In 2024, the Santa Barbara Council of Governments secured a grant from Caltrans to develop a countywide bike map utilizing AI. This initiative, a collaboration with UC Santa Barbara and Simon Fraser University, aims to train artificial intelligence to not only map existing bicycle infrastructure but also to devise a universal, regional wayfinding plan. The project was heralded as a potential revolution in bicycle infrastructure mapping, and this year, that potential is being put to the test.

This week, the Santa Barbara County Association of Governments (SBCAG) launched its interactive AI-generated map, available in both English and Spanish. The organization is actively soliciting feedback from bicyclists across the county to assess the map’s precision and identify any discrepancies.

Understanding Rider Comfort

Peter Williamson, SBCAG Transportation Planner, emphasized the critical role of community input in the project’s success. “The map is only as good as the information behind it,” Williamson stated. “The people who know our streets and neighborhoods best are the people who live, work and ride here.”

The interactive map employs an AI model trained on extensive datasets, including Google Street View imagery and OpenStreetMap data. This AI analyzes various roadway characteristics to predict how comfortable cyclists will feel on different routes throughout Santa Barbara County. Features considered by the model include the presence of dedicated bike infrastructure, traffic volumes, speed limits, lane widths, and other elements discernible from street-level imagery and mapping data.

This approach moves beyond traditional bicycle maps, which typically only indicate the existence of bike facilities. The AI model aims to classify streets based on a rider comfort index, a crucial factor in encouraging more people to cycle. For transportation planners, such a tool could be invaluable in pinpointing areas where cyclists are likely to feel secure and welcome, as well as identifying routes that might deter potential riders.

Seeking Public Input for Refinement

The success of this ambitious project hinges on the AI model’s ability to accurately reflect real-world conditions. As transportation agencies increasingly explore the capabilities of artificial intelligence, the Santa Barbara project is poised to become an early indicator of AI’s effectiveness in enhancing bicycle planning. The initiative also raises questions about the balance between AI-driven analysis and essential human judgment.

SBCAG is calling on cyclists to engage with the map and report any inaccuracies. This includes identifying missing bike lanes, correcting route information, flagging inaccurate comfort ratings, and noting any other errors. The public comment period for providing this feedback is open until August 14.

Future Implications for Urban Mobility

The Santa Barbara County AI bike mapping project is expected to conclude in the summer of 2027, with the publication of its final maps. If successful, this pilot program could significantly influence how bicycle mapping and planning are conducted not only in California but potentially in other regions globally. The findings could provide a blueprint for leveraging AI to create more cyclist-friendly urban environments, fostering active transportation and improving public health.

Key facts

The implementation of AI in urban planning, particularly for active transportation, represents a significant step towards data-driven decision-making. By analyzing granular data and seeking direct user feedback, projects like this in Santa Barbara can help create more responsive and effective urban infrastructure. The insights gained could inform future investments in cycling facilities, street design, and traffic management, ultimately contributing to more sustainable and liveable cities. The emphasis on rider comfort as a key metric is particularly important, as it addresses a common barrier to cycling adoption.

Source: Streetsblog SF, https://cal.streetsblog.org/2026/07/09/can-ai-help-plan-better-bike-networks-santa-barbara-county-is-about-to-find-out