SimplifAI

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.

Using a wide range of data about current and impending traffic flows, weather, the built environment and air quality readings, it is possible to predict an air quality problem within an urban region during a particular time period. What is not currently possible is to generate, in real time, specific strategies specifying changes in combinations of UTC assets (traffic lights, information signs, variable speed limits etc) that if carried out immediately are likely to avoid the air quality problem. This is the problem that we are attempting to solve, and if successful the feasibility phase has the potential to lead to widespread impacts. As well as the benefit on efficiency of strategy production, automation can lead to reduced errors in the process, and less reliance on in-house expertise.

Transport for Greater Manchester (TfGM) will benefit as they will gain a greater understanding of the sources and content of available data assets, and how they can enhance their service using this data. They will also increase their expertise in the construction of strategies for regional control of road traffic, and at the end of the project be able to utilise any tools delivered in helping to create the strategies for influencing flows of traffic through their region. In particular, they will be able to explore a means of influencing regional traffic flows in order to avoid compromising air quality standards. Partners BT, InfoHub and KAMfutures will have a greater understanding of the added value of heterogeneous data sets, their integration and enrichment, and the potential of centralised control strategies, to help in complex system management. Such is the ubiquitous nature of the problem of influencing regional flows of urban traffic, the products in terms of software tools and data sets which come about from the research have the potential to be sold commercially on a global basis.

UTMCs generally will benefit as they will have an examples of tools and methods that, taking advantage of a range of data, can be used in regional control. Starting with TfGM, the fusing of the data sets and development of the proposed technology will empower UTMCs to expand the service they provide to the community, and where that is used for air quality management, improve the quality of life for anyone using the Urban area. This would particularly help UTMCs where operator expertise is sparse. Drivers and the general public will eventually benefit as the use of the data and consequent more informed regional controls will better inform and control vehicles, better balance the network resource, and help to avoid pollution hot spots. Hence the research has the potential to improve the nation's health, by avoiding pollution build-up, and the nation's wealth, by commercialisation of any tools produced and data sets used to be sold in UTMCs worldwide. The timescale for impacts to be made will be approximately 3 years after the end of the feasibility, as the project will need to move through the demonstrator and full prototype stages before operational use.