Ensure Accurate, Real-Time Curb Data and Strengthen Collaboration to Support Scalable, Data-Driven Curb Management Systems.
Lessons Derived from a Field Deployment in San José’s Greater Downtown.
San Jose, California, United States
SMART Curbs: City of San Jose’s Curb Digitization and Management Pilot
Summary Information
Downtown San José is facing growing pressure on its limited curb space as delivery vehicles, transit, cyclists, pedestrians, and businesses all complete for access. Outdated curb management practices have led to inefficiency, congestion, and safety risks, such as blocked bike lanes and sidewalks. To address these challenges, the City launched SMART Curbs, a curb digitization and management pilot between September 2023 and June 2025, to test new tools and establish an innovative model for modern curb management. The pilot served as a proof-of-concept to establish a strong foundation for long-term, data-driven curb management and provided valuable insights for cities pursuing similar efforts. The pilot focused on San José’s Greater Downtown, with 140 miles of curb scanned using Light Detection and Ranging (LiDAR) technology, deployed 164 cameras and 23 sensors, and launched the NexCity platform with curb data specification (CDS)-compliant application programming interface (API) to integrate real-time data. The pilot proved that large-scale curb digitization was achievable and provided critical lessons for scaling.
The following are some of the lessons learned in this study.
- Maintain accurate and up-to-date inventory data. The dynamic nature of downtown operations requires an ongoing process to keep curb data current. Establishing regular updates and automated synchronization will ensure that information remains accurate and reliable.
- Select appropriate camera technology. Solar-powered, snapshot cameras limit the visibility of real curb activity in this pilot. Implementations should prioritize hardwired, continuous-stream cameras to capture short-term events and provide comprehensive monitoring.
- Advance artificial intelligence (AI) capabilities. Current AI tools were limited in identifying different curb users. Continued development of object detection and predictive analytics will be critical to support proactive, real-time curb management.
- Foster strong vendor relationships. Effective technology deployment depends on clear communication, shared accountability, and adherence to timelines. Establishing structured coordination with vendors will help manage first-of-its-kind technology more efficiently.
- Develop a comprehensive data foundation. Missing or inconsistent baseline datasets caused delays during pilot deployment. Future phases should begin with validated, consolidated data on assets such as streetlight poles and meters to improve system integration and planning.
- Encourage collaboration and peer learning. Working with other cities through national partnerships provided valuable opportunities for shared learning and problem-solving. Ongoing collaborations help establish consistent standards and accelerate progress in curb management innovation.
