Neural network intelligence for urban traffic control systems

Our whole philosophy is on deeply integrating data at it’s most fundamental level, meaning that the systems can make the best decision possible with all available data. Signals do not need to be crashed when a priority call is made from a public transport vehicle when it is late. Black boxes do not need to be fitted to fire stations with limited pre-defined routes out of the station and existing network data can be integrated so whole network data can influence the decisions made about which approach or corridor to give more green time too.

We are entrepreneurial, spirited, afraid of no challenge, delivery or foe. Focused on the overall objectives of the clients, users and the business, not the detail of blockers, problems or constraints. offers the market place a different way of managing traffic signal control systems. Firstly, it is worth noting that like the existing systems, if an authority would like a single algorithm operating on a system wide basis, 24 hours, 7 days a week that is perfectly possible and acceptable thing to do. However knows there are situations where local conditions do not fit the “one algorithm” model and enables engineers to have the flexibility to pick and choose what should happen.

Using and building from the latest cutting edge artificial intelligence research, our service orientated architecture approach is adaptable and able to meld big data and ‘internet of things’ to make best use of Artificial Intelligence capabilities within the traffic signal domain. Wouldn’t it be great if you could use not only the local detector data, but also machine vision, ANPR, Bluetooth, through a neural network to decide when to move to adaptive control? takes the approach to use both machine learning and data mining to achieve the level of artificial intelligence required in an adaptive control environment. Machine learning employs data mining methods as “unsupervised learning” or as a reprocessing step to improve learner accuracy. Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while adopts a collaborative approach between signal engineers and machine.

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