Intelligent traffic flow
Traffic lights controlled by artificial intelligence (AI) could help to ease congestion on Leicester’s roads and improve the city’s air quality.
A project, led by De Montfort University Leicester (DMU) with researchers from Leicester University will see if AI and satellite data can help to manage traffic.
Researchers will examine information from the city’s Star Trak system – which tracks buses and feeds information on their status to passengers via electronic message boards at bus stops – in order to analyse traffic flow through the city at various times.
They will use the data to create computer models that will test how effective artificially intelligent systems are at controlling traffic lights in order to help traffic run more smoothly.
Leicester University will provide earth-observation satellite data as well as ground-based environmental sensors to monitor air quality.
This information will be used to see when measures need to be taken to reduce air pollution in particular parts of the city during times of heavy traffic build-up.
Researchers aim to gather evidence to start a full-scale trial in the city in the future.
DMU’s Dr David Elizondo, the project co-ordinator, said: ‘We are linking with the Star Trak scheme and using computational intelligence to make predictions about what the traffic situation will be like in the next half an hour to an hour.
‘We can then use that information to see what the best ways are to optimise traffic lights and ease congestion.’
Prof Paul Monks, of Leicester University’s Department of Chemistry, said: ‘Bringing together air pollution measurements for space and sat nav technology for intelligent journey planning is truly novel. The team is using two cutting-edge technologies to make sure people will have a better environment to live in. Space observation is a powerful tool for chemical weather forecasting.’
The project will also explore how to turn people’s mobile phones into personal sat navs. Using the technology, bus passengers could opt in to receive updates that tell them not only when the next bus is due, but if another bus would get them there quicker.
Dr Elizondo added: ‘The dynamic journey planning will, in time, offer public transport users the use of a “passenger’s sat nav”.
With live data from bus positioning systems, such as the Star Trak system and other traffic data sources, we will use artificial intelligence to re-determine the optimum route through the public transport network that takes account of delays and congestion.
‘This would give passengers more options and help them make the best use of their time; for example, why stand at a bus stop for 45 minutes if you could stay at home and only leave when you know the bus is just around the corner?’
The project is funded by £30,000 from the Higher Education Collaboration (HEI) Grant from the Transport innovation Network (iNet).