A new HE-funded project will use Aimsun Live decision support to mitigate the environmental impacts of road traffic.
Network Emissions/Vehicle Flow Management Adjustment (NEVFMA) is a proposal to investigate the impact of different traffic management tactics in real-time on the Oxford road network (within the ring road) and their implications on the A34 and A40). The proposal provides short-term air quality and traffic predictions, based on a network of sensors and dynamic simulation. The system will enable operators to simulate the effect of different traffic management strategies and select the best strategy to mitigate poor air quality.
In the last 5 years, air quality has become a key consideration for the UK government agenda, as illustrated by the increasing number of Low Emission Zones (LEZ), Ultra Low Emission Zones (ULEZ, including the first megacity ULEZ in London) and even the world’s first Zero Emission Zone (in Oxford, starting in 2020).
This localised approach to reducing emissions may have wider implications for nearby areas, for example, discouraging vehicles from crossing the city centre may over burden a nearby ring road with a net negative effect. A linked consideration is that as cities/regions are aiming to incentivise active travel by, for example, prioritising pedestrians over cars at traffic lights, this might have implications on the link roads. Clearly, a holistic approach is needed, where the impact of such local measures can be evaluated alongside their implications on the SRN.
Establishing a working and scalable Proof of Concept (PoC) for the holistic approach described above is what NEVFMA aims to do, although this is challenging as it involves a dynamic traffic management system with real time responses to live situations.
If we suppose that an area would like to dynamically change between prioritising walkability (and healthy travel) as a Business As Usual (BaU) situation, yet at times of high congestion would need to switch the traffic management to increase traffic flow (thus reducing congestion and enabling better use of vehicles), our solution is the following:
• Systematise clear trigger points, based on predictions of the implication of not taking any actions
• Create measurable and actionable KPIs that can be used for the evaluation of the system at traffic control centres
• Use real time data of both traffic and air quality (AQ data may be from on-vehicle sensors, infrastructure-imbedded sensors or satellite data)
• Establish clear tactics that are appropriate to the region (divergence routes, time-to-green etc) that are based on traffic modelling of their implications, which are also evaluated
• Disseminate information to drivers via reliable routes, such as highways authority’s social media, broadcast radio etc.
• Disseminate information to drivers via edge devices, such as traffic lights and DSRC communication