QureTraffic
Traffic prediction for operational decisions. Help traffic teams look beyond the latest conditions and assess how congestion may develop.
Explore QureTrafficQureTraffic helps transportation teams anticipate changing traffic. qureDT supports digital-twin modeling and scenario analysis for planning. Two MobiliDi.ai products for better-informed decisions.
An independent Carnegie Mellon University spin-off.
Two products with distinct roles: anticipate near-term conditions and assess network alternatives.
Traffic prediction for operational decisions. Help traffic teams look beyond the latest conditions and assess how congestion may develop.
Explore QureTrafficDigital-twin modeling for planning decisions. Represent transportation networks and compare how traffic behaves under different scenarios.
Explore qureDTQureTraffic’s first client.
A prediction relationship dating to 2018.
Cranberry Township, Pennsylvania, is QureTraffic’s first client. The Township shared its short-term traffic prediction experience and lessons learned in a national webinar hosted by the National Operations Center of Excellence.
The presentation reflects Cranberry Township’s experience with traffic prediction, not an endorsement of MobiliDi.ai. Recordings are linked, not embedded.
qureDT applications
Digital-twin modeling supports transportation planning by representing a network and examining how traffic moves through it.
Southern California · Regional planning
Regional traffic modeling and scenario comparison for Southern California.
Explore the SCAG applicationMetropolitan network modeling
A regional network perspective for transportation planning.
Explore the Pittsburgh applicationFocus on the operational or planning need and the information required to assess it.
Present forecasts and scenario results clearly, with the context needed to interpret them.
Give transportation teams useful information while keeping decisions with the people responsible.