RoadInsite
Illustrative urban transport corridor at blue hour

Capabilities

The right intelligence.
For your next decision.

Transport understanding, analytical depth and practical AI—connected around the work your team needs to do.

Discuss your challengeExplore AI co-workers
Transport corridor concept · illustrative artwork
01 / ANALYTICS

Understand how your network works.

Bring scattered transport data into a clear account of performance, patterns and change. We work from the decision you need to make and the evidence available.

  • Corridor travel times, reliability and delay
  • Signal performance and traffic-volume analysis
  • Data quality review and reproducible reporting
Start a conversation about your data
Illustrative comparison of observed and modelled patterns
Illustrative profile · actual measures depend on your data.
02 / PLANNING

Make change easier to examine.

Compare defined demand, mode-share or network scenarios with transparent assumptions. Explore the sensitivity of the results before drawing conclusions.

  • Performance–demand relationships
  • Road-closure and network-change comparisons
  • Scenario dashboards and technical interpretation
Discuss a scenario
Conceptual transport network with connected corridors and observation points
Conceptual network · links do not represent a real location.
03 / COMPUTER VISION

Turn footage into transport evidence.

Computer vision can help structure what a camera observes. We explore detection, classification and movement analysis with a clear evaluation and review process.

  • Vehicle and pedestrian observation workflows
  • Survey summaries and candidate-event review
  • Testing across camera views and operating conditions
Discuss video analysis
Schematic observation positions, movement traces and counting line
Illustrative detections and traces · not measured survey results.
04 / AI & KNOWLEDGE

Give information a useful job.

Connect language models, approved knowledge and analytical tools to support repeatable transport work. Build the review point into the workflow from the start.

  • Source-linked project knowledge retrieval
  • Draft briefings and technical reporting
  • Agentic workflows with defined permissions and review
Explore AI co-worker roles
Data, analysis and evidence connected through a reviewable workflow
Connected inputs, analytical work and a reviewable output.
05 / DATA ENGINEERING

Build on evidence you can inspect.

Reliable analysis begins with understanding the data. We examine structure, coverage and consistency, then prepare the records and methods needed for the work.

  • Data integration, preparation and validation
  • Repeatable processing and clear data lineage
  • Documented assumptions and limitations
Discuss a data challenge
Illustrative comparison of observed and modelled patterns
Illustrative comparison · data checks precede interpretation.

A practical starting point

Tell us the decision.
We’ll explore the evidence.

Bring a project question, a recurring analytical task or a dataset that is proving difficult to use. We can help define a realistic scope and the evidence needed to move forward.

For emerging AI workflows, we start with a bounded pilot, clear acceptance criteria and a reviewable deliverable.

Discuss your challenge

Bring us your transport question.

Let’s work out what the evidence can tell you.

Discuss a project