This project seeks to investigate the feasibility of the integration of a range of data sets about an urban area to enable transport operators to manage traffic flows more effectively, with particular application to the control of transport-determined air quality levels. The data sets include time varying data about traffic (average speed, flow rate), weather (wind speed, direction, temperature) and air quality (NOx concentrations), as well as static data about route topology, geography and infrastructural assets.
These data sets will be enriched to enable the use of an automated approach to derive regional strategies for urban transport operators, so that traffic flows can be influenced strategically, and in real time, through an urban region. Currently the creation of regional strategies by traffic operators is a time consuming manual process requiring a high level of experience and expertise, and is aimed specifically at minimising delay for traffic during exceptional events (such as road closures or large concerts).
The project will study the feasibility of a novel, two stage approach:
(i) exploring a new way of enhancing and enriching transport and environmental data feeds by adding semantics in the form of meta data and ontological context, to improve the clarity and usability of the data;
(ii) enabled by (i), provide automated support for the real-time creation of regional transport plans (strategies) that urban transport operators can enact.
The success of this study, and consequent exploitation of the technological advances, would lead to UTMCs being empowered to address the urban challenge of ensuring satisfactory air quality within urban areas, and will be able to inform specific road user groups (e.g. cyclists) of air quality conditions on their route choices. More generally, UTMCs will have access to technology to support the creation of regional strategies to address other challenges, such as dealing with road closures, multiple resource effecting scenarios, or balancing flows on the network, within real-time.