May 25, 20215 min read

Smart City Solutions Through Combined Air Pollution & Traffic Technologies

The Project : Network Emissions/Vehicle Flow Management Adjustment (NEVFMA)

Network Emissions/Vehicle Flow Management Adjustment (NEVFMA), a Highways England funded project through Innovate UK joined up EarthSense, Aimsun, Siemens Mobility and Oxfordshire County Council to find innovative ways of improving air quality across Oxfordshire by influencing traffic flow to reduce vehicle emissions.

The project, led by leading providers of software for traffic planning, simulation, and prediction, Aimsun used combined traffic and air quality technologies from Aimsun Live and Zephyr® air quality monitor measurements integrated with EarthSense’s MappAir® 10 metre city dispersion model to provide solutions to real life traffic problems.

Making use of scenario planning and response plans, the project aimed to guide traffic management strategies for the implementation of a prospective Zero Emission Zone (ZEZ) in Oxfordshire.

How do the Technologies Come Together?

Transport, emissions, and air quality are factors which are known to go hand in hand. EarthSense specialise in air quality monitoring services to provide commercial businesses and local authorities with insights into air pollution, and Aimsun are a leading software developer of smart transport modelling services. With intrinsic links between air quality and transport still an ongoing global issue, NEVFMA employed each of their technologies to provide solutions to transport issues by influencing traffic flow, reducing vehicle emissions, and optimising air quality.

Aimsun’s transport model, Aimsun Live, uses inputs from live field data, traffic counters, data capture devices and signal timings to produce a traffic model of road networks to reflect real time traffic behaviour. This traffic data is ingested with historic traffic flow information to produce predictions about what is likely to happen in the road network based on past traffic experience. Aimsun Live also uses the London Emissions Model (LEM) to produce emission estimates, also known as tailpipe emissions, for each link throughout the road network or city. Aimsun Live allows its users to compare and identify the most effective traffic management strategies against a ‘do nothing’ or scenario approach, enabling informed decisions to dynamically manage traffic.

EarthSense services help to identify air pollution levels with Zephyr® air quality monitors, MappAir® modelling and the MyAir® web application. By deploying Zephyr® monitors, air pollution measurements provide live information for real-time air quality levels which are supplemented by MappAir® for additional context to measurements. Viewed, analysed, and downloaded through MyAir®, the complete service quantifies and provides visualisations of pollution sources and dispersion around localised sites or wide scale areas to enable a holistic understanding of air quality.

In the context of NEVFMA, both Aimsun and EarthSense services were employed. Aimsun Live was utilised to deliver a model of Oxfordshire’s road networks for information regarding traffic build-up and peak congestion periods throughout the day and predictions of upcoming traffic problems, allowing for mitigation strategies to be identified in advance. EarthSense air quality data provided visualisations of how nitrogen dioxide (NO2) flows and disperses around Oxfordshire’s buildings and urban canyons, historic data and up to 3-day pollution forecasts. Integrated into the model were measurements from a Zephyr® network with mobile and static units deployed around the county to provide real-time ambient air pollution information for nitrogen dioxide (NO2), ozone (O3), nitric oxide (NO) and particulate matter (PM2.5). This data was viewed through the MyAir® web app, enabling its users to analyse and download traffic and air quality data.

Through employing both air quality and transport data, this helped to identify real-time air quality and traffic behaviour throughout Oxfordshire. The next steps were to combine each data set to produce a solution to reduce emissions by influencing traffic flow.

Producing Real-life Scenarios & Response Plans

Aimsun developed a real-time bilateral link to send tailpipe emission estimates from the LEM model and traffic volumes from Aimsun Live to the MappAir® model. EarthSense used this to enhance the data to a high level of resolution and to create multiple predictions of future air pollution levels against a variety of real-life traffic scenarios, which are subsequently sent back to Aimsun. Utilising Aimsun Live, real-time Zephyr® data and MappAir® pollution dispersion information, Aimsun received an understanding of how the road network in and around Oxfordshire was contributing to the current levels of air quality.

A collation of real-time information inputs, integrated prediction systems, operator customisation and control was then utilised to create variety of traffic and air pollution scenarios. Aimsun ran each traffic and air pollution dispersion prediction under normal estimates and compared them against timescales for 15, 30, 45 and 60 minute intervals, providing various views of the road network. By creating 4 variants for 4 alternative time periods and combining and sharing each of the estimates, Aimsun could identify which prediction would work best to reduce the high volume of traffic creating tailpipe emissions and contributing to elevated air pollution levels.

In addition, the model used a KPI driven strategy with different zones, including a global KPI and zone KPIs, to highlight which traffic management strategy worked best against a ‘do nothing’ strategy. In Oxfordshire, the study area named zone 2 compromised of an air quality management area (AQMA) and was the given location which would benefit from evidence based traffic management strategy, identified by Aimsun as part of the NEVFMA project. Once the optimal scenario had been identified for the given zone, this could then be implemented into a real life scenario to influence live traffic flow and reduce emissions in Oxfordshire. Overall, 65% of the days with a traffic management strategy identified and implemented was better than a ‘do nothing’ response, helping to improve air quality by an average of 5% in Oxfordshire.

How the Technologies Can be Used as Part of a Smart City Strategy

Smart cities typically have systems for traffic and air quality, but NEVFMA maximised the use of already available traffic and air quality technologies to bring all areas together in one system for the first time. Through employing this first to market innovation as part of a smart city strategy, the NEVFMA project enables the likes of traffic operators, smart city planners and local authorities to implement strategies to better understand air quality and traffic in cities. The model enables its users to make informed decisions based on available data which can be implemented in real life scenarios to improve traffic flow and optimise air quality levels.

For example, the model’s combined air quality and traffic predictions can be used as part of a smart city strategy to implement Low Emission Zones (LEZ), Ultra Low Emission Zones (ULEZ) or Zero Emission Zones (ZEZ), such as that under way in Oxfordshire. By identifying times and days likely to experience high levels of traffic build up contributing to elevated air pollution concentrations in specific areas, the model can be employed to make informed decisions about how best to respond to these issues to reduce tailpipe emissions having an impact on the affected site. Once the strategy has been identified, the zone can be implemented in real life with ongoing mitigation strategies, reducing the risk of air quality reaching an unsafe level and the likelihood of those nearby worsening or developing adverse health impacts, such as asthma or COPD.

The model also enables its users such as transport managers and planners within a smart city to react efficiently when traffic issues arise, like car accidents or road closures. NEVFMA showcases how a range of strategies impacts pollution dispersion and traffic flow for up to 60 minutes ahead. Once these have been identified, users can look for the response which reduces air pollution and enhances traffic flow to react quickly against a ‘do nothing’ strategy. Once the strategy has been highlighted, such as a contraflow strategy to allow traffic to flow down a specific road, the evidence-based response can be implemented in real life to reduce traffic build up and tailpipe emissions.