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Traffic prediction.
Transportation digital twins.

QureTraffic supports operational decisions with a near-term traffic outlook. qureDT supports planning with transportation-network modeling and scenario analysis.

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The illustrations below use synthetic data. They explain concepts, not product screenshots, measured traffic, or project results.

QureTraffic · Traffic prediction

A forward view
for traffic operators.

Incidents and changing demand can affect a network before the full impact is visible locally. Short-term prediction gives traffic teams context for assessing what may happen next and considering a response.

Customer experience: Cranberry Township
Look ahead
Consider the near-term outlook alongside the latest traffic conditions.
Local impact
Assess how regional disruptions may affect roads in the area you manage.
Operator role
Use forecasts to support assessment while keeping operational judgment with your team.
Synthetic illustration

Reading a forecast

Traffic speed · normalized scale

A synthetic example, not measured traffic: normalized speed rises from about 0.3 to 0.6 in the observed period. A dashed forecast then trends toward 0.45, with an increasingly wide illustrative uncertainty band.
  • Observed
  • Forecast
  • Uncertainty

An illustrative range of possible outcomes, not a calibrated confidence interval or a claim of forecast accuracy.

qureDT · Digital-twin modeling

A network perspective
for planning decisions.

qureDT models how traffic moves through transportation networks over time. Dynamic traffic assignment and network simulation help planners compare alternatives and examine how changes affect the wider network.

Applications: SCAG and Pittsburgh
Network context
Represent transportation networks and travel demand for a planning question.
Traffic dynamics
Examine how traffic movement and congestion evolve over time.
Alternatives
Compare network conditions under different scenario assumptions.
Synthetic illustration

Reading a network

One corridor · one intersecting route

A simplified fictional road network with one main corridor and an intersecting route. The western approach is solid green for free-flowing traffic, the center is dashed amber for slower traffic, and the eastern bend is dotted red for congestion. These colors and patterns illustrate conditions only; this is not a simulation result.
  • Free-flowing
  • Slower
  • Congested

A schematic network for explaining scenario questions. Separate from the forecast illustration; no model connection is implied.

Distinct product roles

Prediction and simulation.
Separate products today.

QureTraffic and qureDT are separate MobiliDi.ai products for operational and planning decisions. Connecting digital-twin modeling to live prediction is a separate development effort.

Prediction–simulation integration is not implemented and is not an available QureTraffic feature.

Operations and planning

Match the product
to the decision.

  1. Operational decisions

    Use QureTraffic to assess how traffic conditions may develop in the near term.

  2. Planning alternatives

    Use qureDT to model a transportation network and compare scenario outcomes.

  3. Professional judgment

    Interpret model outputs in context and keep decisions with the transportation team.

See the applications