Three years of developing AI for traffic signals - how far have we come, and what next?
Three years ago, we embarked on a three-year programme to develop an AI-based traffic signals optimisation system. We, Vivacity Labs, partnered with Transport for Greater Manchester (TfGM) and Immense Simulations for this Innovate UK co-funded project to build and trial a solution which uses AI to optimise traffic networks.
Along the way, we have presented our progress at the past two JCT conferences. Last year, we were excited to share that we had successfully deployed the system to three junctions in Greater Manchester. This year, we are rounding out this three-part series by sharing some of our key achievements of the past year, summarising our overall learnings, and discussing our further development and commercialisation plans.
Our results from real-world trials have proven that AI can be used to improve on existing systems by up to 30%. We are scaling up these demonstrations, and expect to quantify this more precisely and thoroughly in future, but these initial results are extremely promising.
For transport authorities today, congestion is only one of many different priorities. Improving air quality, prioritising active travel, and improving public transport reliability/uptake are all at the top of the agenda. SCOOT and MOVA have dominated traffic signal control in the UK for the last few decades, and while both have scenarios in which they work effectively, reducing congestion through coordination of multiple junctions (SCOOT) or optimising individual junctions (MOVA), they have both struggled optimising signal timings to improve air quality or to help other modes of transport.
Air quality optimisation with SCOOT has been trialled, but not rolled out at any scale. Bus priority in SCOOT, while well established, is a relatively blunt instrument, overriding optimisation for any other mode to provide late running buses with green signals, and thus degrading overall system performance. Meaningful prioritisation for other key modes, such as cyclists, is not widely available. Meanwhile, it is well known that performance of SCOOT degrades over time, often by up to 30% - but recalibration is manual and expensive and thus not viable for many authorities.
In this context, it is vital that transport authorities get access to better systems, which allow authorities to prioritise any objective according to their local policies.