Using the strength of mobile data to keep the M25 moving

Hilary Carter | 07 Nov 2016 | Comments

The most successful forms of innovation are created by making small changes to the way we think and approach a problem.

We often use or hear the term big data but perhaps are not fully aware of what this means. Mobile data is a form of big data and EE, with over 31m customers, generates billions of rows of data every day from their 2, 3 and 4G networks. As devices become more and more ubiquitous so too does the ability to understand behaviours, trends and patterns at a much larger scale and with greater detail than ever before.

Furthermore, as richer data sources become available and our understanding of how to engineer it we are able to gain greater in-depth knowledge of movements on our highways and rail networks, with greater detail than manual traffic counts and roadside interviews ever could.  This provides intelligence on the UK’s mobility.

Atkins is looking at how innovation can change the way we safely provide services on one of the busiest motorways in Europe – the M25. Currently, Connect Plus and Connect Plus Services are required to monitor journey times on 20 sections of the M25 and radial routes to London.

Between 2003 and 2008, 11 road workers were killed and 104 were seriously injured whilst working on UK roads. As a result, Highways England in an effort to provide a ‘safe and serviceable network’ worked with Atkins & EE – utilising Mobile Data Insights. This replaced the need for manual surveys which were time-consuming, provided limited information and posed a very real threat to health and safety.

This traditional approach to data collection is doomed to stagnation. Data Insights provide more accurate information to support operational and long term planning. Insights are derived by combining the network activity of the biggest mobile network in the UK and Atkins’ experience of modelling and capturing of transport and people movements.

The Atkins/EE collaboration supplies journey time data analysis as an independent metric, using anonymised and aggregated mobile phone data to derive the average journey time.

Crucially, this approach to mobile data analysis provides a safer and more accurate alternative, at a lower cost. Not only it is more efficient in terms of time and resources but it also provides an opportunity for new connections to be made.