Vivacity Labs provides an AI‑powered computer‑vision sensor that replaces multiple legacy traffic detectors with a single edge‑processed device, delivering real‑time, multimodal data—including vehicle counts, speed classification, journey times, occupancy and near‑miss detection—with over 97% accuracy. The platform offers integrated analytics for traffic monitoring, road‑safety risk assessment, and signal‑control optimization, helping municipal transportation agencies obtain continuous, actionable insights while reducing deployment and maintenance costs.
Funding
$8.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Transportation planners and city authorities struggle with fragmented, manual data collection from legacy sensors, making it difficult to obtain continuous, multimodal traffic and safety insights needed for evidence‑based planning and funding justification.
Solution
Vivacity Labs offers a single AI‑powered computer‑vision sensor that captures high‑accuracy, real‑time data across more than ten vehicle and vulnerable road user classes. The sensor delivers continuous counts, speed classification, journey times, occupancy, near‑miss detection, and path tracking, all processed on‑edge to ensure privacy. Integrated analytics provide actionable insights for traffic monitoring, road safety risk diagnosis, and signal control optimization, reducing reliance on reactive crash statistics. The solution is compatible with existing traffic management systems and can replace multiple legacy detectors, simplifying deployment and maintenance while delivering over 97% classification accuracy validated by Transport for London and other authorities.
Target Audience
Primary customers are municipal transportation departments, regional planning agencies, and traffic engineering firms that need integrated traffic monitoring, safety analysis, and signal control solutions for urban road networks.
Features
- AI-driven computer vision sensor with edge processing for privacy‑by‑design, capturing 10+ classified modes including e‑scooters and VRUs
- Real‑time multimodal data streams: vehicle counts, classified speeds (85th percentile), journey times, and zonal occupancy
- Near‑miss detection using Time‑to‑Collision and Post‑Encroachment Time metrics, with anonymized video for root‑cause analysis
- Tracks and path visualization of vulnerable road users to identify infrastructure usage patterns
- Compatibility with major UTC controllers (SCOOT/MOVA, Yunex, Telent, Swarco) for signal timing optimization and loop emulation
- Over‑the‑air firmware updates to add new features and maintain future‑proof performance
- Validated >97% classification accuracy and 99.9% uptime across a global network of over 6,000 sensors