Our sensor hardware provides continuous and anonymous streams of data on all forms of urban mobility across towns and cities. From pedestrian to cyclist, car to HGV, our artificial intelligence technology is able to continuously capture and classify live transport usage, 24/7.
Junctions & high street1
- Live classified counts & turning movements at key junctions.
- Understanding pedestrian footfall & desire lines on high streets.
- Measure impact of roadworks on flows.
- Quantifying and classifying HGV impact on traffic
- Utilisation of key active travel spaces.
- Justify government & maintenance grants.
- Quantifying cyclist traffic before installation to justify spend.
- Understanding behaviour during & after works.
Park & ride5
- Demand-side behaviour change through queue alerts on VMS.
- Promoting uptake of Park & Ride facilities.
- Identifying safety-critical events such as stopped vehicles.
- Providing real-time alerts for abnormal behaviour.
- Live traffic information integrated with VMS.
- Real-time prediction and queue alerting.
- Detection of wide range of unusual classes, including wheelchair.
- New classes can be added on request.
Trains, trams & metros9
- Counting boarding & alighting flows at stations.
- Identifying transit vehicles in other vehicular flows.
- Real-time, low-latency alerts for queues.
- Journey time monitoring to demonstrate impacts.
- Futureproof data collection for MOVA / SCOOT.
- Real-time through-flow measures
- Understanding utilisation of minor roads and junctions.
- Exploring active travel across rural networks.
- Traffic signal optimisation going beyond MOVA / SCOOT.
- Prioritise different modes, and optimise for air quality.
- Understand pedestrian desire lines before implementation.
- Understand behaviour of cyclists & other vehicles.
- Gather detailed origin-destination data around a central cordon.
- Understand journey times across the town/city centre.
- Assess impact of roadworks on journey times.
- Optimise time-of-day and duration of roadworks based on traffic patterns.