<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/2A3CCD59-F288-423D-902E-1B64507747CA" ns1:id="2A3CCD59-F288-423D-902E-1B64507747CA"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/0623C2D6-941D-48B2-8185-60370B9E0D62" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/DF436B39-0FEF-4E8C-9456-141ADCA18104" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/FAABF2EB-AEB2-4A67-A79E-39B8F8121932" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/DF436B39-0FEF-4E8C-9456-141ADCA18104" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2025-01-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/925FEDF7-34C5-4D7C-916B-234CF72A1B43" ns1:rel="FUND" ns1:start="2024-04-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10106617</ns2:identifier></ns2:identifiers><ns2:title>Hyperlocal AI Modeling for Open Emissions Tracking &amp;amp; Data Access (OET)</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Traffic congestion is a significant contributor to vehicle emissions, particularly in the urban environment. However, the data used to model congestion, so local authorities can decide on the best traffic management, is provided sporadically and is not often detailed enough to be very useful at a local scale. TUBR propose a new way of modelling data. Our methods mean that the small amounts of data available is sufficient to model traffic flows and emissions patterns over a wider area, for better traffic management and planning to reduce congestion and the resultant emissions.</ns2:abstractText></ns2:project>