Intelligent mobility infrastructure for cities of all sizes.
Glidonce was built by people who had spent careers in transportation engineering and urban planning — who understood both the massive opportunity to improve how cities function and the deeply entrenched systems that make change difficult. We started with a simple observation: cities have more data than ever about how people move, but almost no ability to act on that data in real time.
Our first product was an adaptive traffic signal controller that used machine learning to optimize signal timing based on real-time vehicle counts. The results in our first pilot city — 22% reduction in intersection delay, 18% improvement in arterial travel times — validated our approach and the demand for what we were building.
Today, Glidonce operates in 10+ cities across North America and Europe. With $5.2M in Seed funding from Prota Ventures, we are accelerating our expansion to new markets and building the MaaS integration capabilities that will define urban mobility in the decade ahead.
The principles guiding Glidonce's approach to urban mobility.
Every recommendation we make is grounded in data and validated outcomes. We measure what we claim, share our methodology, and hold ourselves accountable to measurable results.
Smarter cities should be better cities for everyone. We design mobility solutions that improve access for all residents, not just those in well-served high-income areas.
We work with cities as genuine partners, not vendors. Our success is measured by the success of the cities we serve, and we build lasting relationships rather than one-off deployments.
We build on and contribute to open mobility data standards (GTFS, MDS, SIRI) that enable the interoperability the mobility ecosystem needs. Proprietary lock-in is bad for cities.
Every mobility optimization we deliver reduces vehicle emissions through reduced idling, improved network efficiency, and support for active and electric mobility modes.
Mobility systems are dynamic. Our platform learns continuously from deployment data, improving performance over time in ways that one-time optimization cannot match.