Aggregated braking, speeding and distraction data from opt-in phones and devices lets transportation agencies map risky corridors, target countermeasures and measure before-and-after impact.
Operators responsible for road networks, corridors, intersections, school zones, work zones, drivers, behavioral events, safety programs and transportation agencies cannot continuously inspect every operating condition or developing fault. Periodic checks leave gaps between readings, while isolated local indicators provide limited history and context. A late response can cause downtime, safety exposure, equipment damage, or incomplete compliance evidence. Teams need timely measurements, clear exceptions, and a defined operating response.
AI Road Network Behavioral Risk and Safety Analytics combines connected field data with application software. Opt-in mobile apps and IoT devices collect aggregated braking, speeding, distraction and other driving measurements with geospatial context. StreetVision combines behavioral analytics and geospatial intelligence to map emerging risk, compare locations, assess hotspots and measure change over time. Agencies prioritize enforcement, education, traffic calming, control improvements and infrastructure investment, then evaluate before-and-after impact.
Teams configure the monitored assets, users, and operating rules. Opt-in mobile apps and IoT devices collect aggregated braking, speeding, distraction and other driving measurements with geospatial context. Smartphones, connected vehicles and supported IoT tags use configuration-specific network paths; no single exact transport applies to the full workflow. StreetVision combines behavioral analytics and geospatial intelligence to map emerging risk, compare locations, assess hotspots and measure change over time. Agencies prioritize enforcement, education, traffic calming, control improvements and infrastructure investment, then evaluate before-and-after impact.
Vendor documentation: vendor website
Evidence level: L1 · Vendor-stated: how evidence levels work
More proactive safety planning: Finds dangerous behavior before another crash occurs.
Better-targeted investment: Directs limited resources to measured high-risk locations.
Wider network visibility: Uses aggregated connected measurements without roadside sensor deployment.
Defensible impact reporting: Quantifies whether safety programs changed risk behavior.
Visualizes speeding, hard braking, distraction and supported precursor behaviors.
Compares risk across specific network locations.
Highlights emerging risk before relying only on historical crashes.
Supports enforcement, education, calming and infrastructure decisions.
Tracks behavior change around safety programs over time.
Supports configured areas and contextual layers such as crashes or work zones.
Ask the AI Solution Architect: compare this solution against alternatives, check connectivity for your region, and get integration guidance. Included with IoT Apps Enterprise.
Fit, availability or a detail on this page. Goes straight to Cambridge Mobile Telematics, no RFP needed.
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