Aimsun leads delivery of NEVFMA, the UK’s first fully operational, county-wide predictive model for the Highways England strategic road network in Oxfordshire. The model predicts not only traffic but also emissions, with a real-time, traffic-linked pollution dispersion model.
Aimsun is proud to lead the delivery this month of a large-scale model of the county of Oxfordshire that will use real-time traffic simulation to reduce congestion and harmful, traffic-related emissions.
Now running live, the model was delivered for the Network Emissions/Vehicle Flow Management Adjustment (NEVFMA) project, funded by Highways England and delivered in partnership with EarthSense, Siemens Mobility and Oxfordshire County Council. NEVFMA uses the Aimsun Live solution to generate short-term predictions for traffic and nitrogen dioxide (NO2) dispersion to help traffic centre operators make the most effective traffic management decisions.
The simulation of predicted NO2 pollution levels from EarthSense’s MappAir dispersion model integrated with Aimsun Live provides the first real-time, traffic-linked dispersion model. This system allows the user to visualise pollution and tackle emissions in official government Air Quality Management Areas alongside other key regions of interest.
Aimsun’s solution uses dynamic simulation to allow the model to generate predictions for individual vehicles in under four minutes. These vehicle predictions are the basis for calculating the concentration of the predicted NO2 using forecasts of meteorological and pollutant data in the upcoming four minutes. Siemens Mobility has integrated the EarthSense air quality sensor with the county’s traffic signal infrastructure, such that it can be easily retrofitted to existing signal heads, providing validation of on-ground pollutants and enabling traffic control interventions to be based on air quality levels.
The system is designed to analyse traffic strategies, allowing up to three alternate scenarios with changes to signal timings, traffic restrictions or other network changes to be compared in the same window. Traffic managers in the county’s traffic control room can then choose the optimal mitigation strategy to reduce congestion and emissions.
Alastair Kitson, Regional Head of Professional Services at Aimsun, comments, “We use four 15-minute rolling horizons to predict the impact on emissions 60 minutes in the future. These are used to present clear and easy to understand KPIs for each scenario, providing confidence to the decision maker who chooses which strategy to adopt. At full chat, the system turns around 16 prediction horizons within four minutes. For a mesoscopic model of this size, simulating individual vehicles, that’s pretty quick!”
“We have developed ways to import most model types and data feeds, so that we can recycle existing authority resources,” adds Aimsun UK Managing Director, Gavin Jackman. “This makes the system good value and economical for Highways England and councils, particularly as it offers continued analytics on the incoming data feeds, real-time monitoring and decision support, county-wide modelling consistency and endless sub-networking for local improvement schemes.”
The analytics performed on the data take ten different types of day into account; this allows the Live system to adapt to different strategic and local road network demands such as ‘holiday weekend’ or ‘busy weekday’, or even a rainy winter’s day when car use is highest. This type of analysis counters missing highs and lows in short-term surveys, and when taken offline, can also provide a great base for applications such as scenario planning, Oxford’s Local Plan, development planning and building business cases.
“The system is flexible and extendable for the strategic road network and designed to grow locally in line with Oxfordshire County Council’s plans to bring more detectors online,” says Kitson.
Llewelyn Morgan, Head of Oxfordshire County Council iHub, says, “We are always trying to explore how we improve and manage air quality. The NEVFMA project will provide unique insight into the potential of using real time air quality data to influence how we plan and actively manage our highways network.”