Oxfordshire, UK – the dynamic model covers an area bounded by the M40 to the east, the A34 to the north and west, the southern bypass to the south with macroscopic extents representing the whole of the county.
An Aimsun Live deployment, with integrated air dispersion modeling; recycled and rebased static 2013 OSM, by conversion to a dynamic Aimsun Next mesoscopic model, before adapting 4 real-time data platforms, including air quality, and deploying Aimsun Live.
The aim of the project was to use emerging technology to holistically balance strategies to enhance public spaces (for example by increasing walkability) with strategies aimed at improving network capacity (increasing vehicle flow), to deliver better air quality and accessibility to all, across regional and strategic networks. The project first uses existing technology to prove the benefit of integration but is furthered by the Siemens-developed method of predicting MOVA to SPaT messages that can be sent to vehicles on approach to junctions, further improving fuel efficiency and network operation in future.
Aimsun developed, calibrated and validated a 2019-2020 model of the Oxfordshire region including typical day patterns for 24 hours and 10 different day types. As Covid-19 was in effect during the project, a pattern was developed to monitor the impact. The model refresh included updating signal timings, public transport and adding detail to the imported Oxford Strategic Model, such as curvature, lane detail and individual vehicle behavior.
Aimsun developed the NEVFMA toolkit by reusing existing Oxfordshire County Council and Highways England traffic monitoring infrastructure, including 197 real-time traffic flow monitoring locations. The system is expandable, so more sensors can be added as the monitoring is extended and new locations come online. A further 18 Zephyr® air pollution sensors (developed by EarthSense in collaboration with Siemens Mobility) were deployed to develop an air dispersion model that integrates with Aimsun Live. Aimsun Live uses the London Emissions Model to predict pollutants on the network over the next hour, for four alternative model scenarios, before EarthSense undertakes dispersion modeling by applying background and meteorological effects to the prediction. Regions are monitored in the model with KPIs indicating which strategy from the alternative scenarios can increase or lower the local pollutants.
The project seeks to prove that with a better understanding of operational network conditions, decision makers can plan and understand the impact of both theoretical network changes in an offline (Aimsun Next) environment and test on-the-day in Aimsun Live. As the model has analytical and simulated predictions, both recurring and unusual event responses can be tested in under 10 minutes giving operators confidence their decision will work toward improving air quality in the same peak period.