Urban traffic management and air quality project

ITSReviewAnnual2015

Urban traffic management and air quality The Urban Traffic Management and Air Quality project (uTRAQ) is a European Space Agency demonstration project developed under the Advanced Research in Telecommunications Integrated Applications 20 Programme. TRL is leading the uTRAQ project, which will lay the groundwork for a new system that uses traffic and air quality data modelling to predict traffic flow scenarios based on transport patterns, traffic management regimes, and emission profiles. uTRAQ aims to: • Demonstrate and promote how space applications can be used for the management of traffic and air quality; • Provide city-wide data for traffic and air quality for the support of policy decisions at an operational level; • Develop a new operational service in close participation with local authorities for managing their traffic and air quality through broad participation by key factors such as local authorities, SMEs, and other industrial businesses; • Demonstrate a manual/automatic monitoring and decision support tool to aid traffic management tools 60 ITS REVIEW Annual Review 2015 at an operational level, considering a range of policy level objectives. uTRAQ is a collection of integrated software components that enhance an existing UTC system, in this case SCOOT, to optimise traffic signal timings for both improved air quality and traffic flow, instead of just for optimal traffic flow. uTRAQ’s modular approach integrates data sources both known (traffic data) and new (air quality, meteorological data) to a traffic management system. Each module serves a specific purpose and is used in the collection, filtering, processing and analysis of the various data feeds in the coordination of the following process: 1. Calculation of potentially beneficial traffic control strategies (in addition to the likely outcome of the ‘do-nothing’ scenario); 2. Simulation of these strategies using traffic and air quality modelling; 3. Identification of an optimal strategy, based on local policy, performance measures and user interaction; and 4. Passing the optimal strategy to the existing UTC system for implementation on street. 5. On-going internal validation of the system outputs to ensure convergence between modelled and real world data. Air quality and traffic management strategies are generated through the use of simulation models. Unlike older traffic simulation models such as SATURN, Aimsun is able to deal with very large networks in a short time; in fact it is so fast it can simulate whole cities in 2-3 minutes, or near real time. See Figure 1 for the overall system architecture. The University of Leicester is building project the air quality (AQ) module, which identifies the AQ data feeds: real world forecasts, ‘now-casts’ and the emissions profiles created from the traffic model for each strategy. This combination of feeds will enable uTRAQ to identify points, routes or areas where AQ is a problem, based on the measured air quality; where it could be a problem, based on traffic model forecast; and finally the outcome of a ‘do-nothing’ scenario. The performance indices (PI) are user-defined metrics, based on traffic and air quality. In the case of journey time / predictable and consistent journeys, the PI will provide feedback on strategy, comparing current traffic operation and current air quality and if the strategy is expected to improve or worsen the real world situation. Air quality forecasts for a given urban area need to account for traffic emissions, local non-traffic sources, and regionally imported pollution. The regional import of pollution is a challenge to estimate, being dependent upon complex meteorology and emission profiles. Earth Observation data is required to assess regional meteorology and atmospheric composition. For the uTRAQ architecture, the MACC service is used to assimilate a substantial number of data products, including wind-vectors, temperature, pressure, and ozone. Assimilated data is then used to model the dynamics and chemistry of atmospheric composition and deliver a surfacelevel concentration of pollutants, particularly nitrogen dioxide. For many medium-sized cities and towns, the proportion of imported pollution can be a substantial contribution to regulatory levels, and therefore local abatement strategies need to adapt rapidly to external influences. As such, the background pollution  Dispersion of roadside pollution in Leicester city centre as predicted by dispersion model. Here the buildings and trees are impacting the venting and tend to trap pollution in street canyons (high concentration in red and low concentration in blue) © University of Leicester


ITSReviewAnnual2015
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