Flow and volume analysis
Counts and classification by movement, direction, vehicle type and time — continuously rather than for a survey week.
AI Capabilities & Services
Traffic & Mobility Analytics
Model traffic flow and mobility patterns for smarter cities and operations.
The problem
Traffic decisions are made on counts taken in a week, a decade ago, at a junction that has since changed. Signal timings are set once and left; incidents are noticed when someone calls them in; and the effect of a change is argued about rather than measured. Meanwhile the cameras and sensors already installed produce more data than anyone reviews, and none of it is turned into a decision.
Who this is for
- City and transport authoritiesEvidence for a change before it is made, and its effect after
- Highway and infrastructure operatorsIncident detection and flow management in something close to real time
- Large-site and campus operatorsAccess, parking and internal circulation understood as a system
What people use it for
Incident and queue detection
Stopped vehicles, wrong-way movement, queue growth and congestion detected from existing camera feeds.
Signal and junction optimisation
Timings evaluated against observed demand rather than design assumptions, with the change measured afterwards.
Planning and simulation
Modelling the effect of a closure, a new access or a development before committing to it.
What it needs to work
- Existing camera feeds, or sensor and detector data where cameras are not available
- Network geometry — junctions, lanes, movements and signal plans
- Historical counts and incident records for validation
- A clear privacy position, agreed before any footage is processed
How it works
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Detect and classify
Vehicles and movements detected from the feed, classified by type and tracked across the frame.
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Count without identifying
Aggregate counts and turning movements. Identification is not required for traffic analysis and is not performed by default.
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Detect events
Stopped vehicles, queues past a threshold, wrong-way movement — with the alert carrying the clip that triggered it.
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Aggregate into patterns
By hour, day and season, so a decision rests on the pattern rather than on the day somebody happened to look.
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Feed the decision
Into the control room for live response and into planning for the change, with before-and-after measured the same way.
How we deliver it
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Site and feed assessment
Camera positions, angles and quality decide what is measurable. Some questions cannot be answered from the existing installation, and we say which.
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Privacy and approval
What is processed, what is retained, for how long and by whose authority — settled before deployment, not after.
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Pilot at a location
One junction or corridor, validated against manual counts so the numbers can be trusted before they inform anything.
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Extend and integrate
Further locations, integrated with the control room and the planning workflow.
Where it runs
- Edge processing at the camera, where bandwidth is limited or footage should not be transported
- On-premises control-room integration
- Private cloud for aggregate analysis and long-term pattern work
Security and governance
- Counting does not require identification, and none is performed unless you have a lawful basis and ask for it
- Edge processing keeps footage local where that is the requirement
- Retention set per feed, with clips kept only where an event justifies it
- Detection performance reported by condition — night, rain and glare are where these systems degrade
Timeline
One location, validated against manual counts, is the unit worth planning around. The variable is the camera estate: good angles and resolution make this quick, and poor ones mean the first honest recommendation is to move or add a camera rather than to model around a view that cannot answer the question.
What you receive
- Validated counts and classification per movement, against manual verification
- Event detection with alerting into the control room
- Pattern analysis by time, day and season
- Before-and-after measurement for any change you make
- A privacy and retention configuration documented per site
Related work
Published projects where we did this.
What this does not do
Detection quality depends on what the camera can see. Night, heavy rain, glare and shallow angles degrade it measurably, and we report performance by condition rather than a single figure taken on a clear afternoon. We also do not build identification or tracking of individuals into traffic work — counting does not need it, and adding it changes what the system is and what governs it.
Questions we are asked
Can you use our existing CCTV?
Usually. Whether a given camera can answer a given question depends on its angle, height and resolution, which is what the site assessment establishes — and where it cannot, we say so rather than producing numbers we do not trust.
Do you identify vehicles or drivers?
Not for traffic analytics. Counting, classification and flow need no identification, and we keep it that way by default because the governance burden of identification is entirely different.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
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Traffic & Mobility Analytics
Model traffic flow and mobility patterns for smarter cities and operations.