Mobile Network Data Demystified
We often get people asking us to explain what Mobile Network Data or MND actually is and to explain how it is then used to provide valuable insights to help people such as city planners to make informed decisions.
What is Mobile Network Data?
Mobile devices are constantly communicating with their mobile network and this communication results in data.
Mobile Network Data uses the resulting data to identify trends in people movement across a variety of different modes of transport thereby providing valuable insights to aid decision making on planning that involves the location and movement of people.
How does it work?
It works by the normal exchange of information between a mobile handset and the network via signalling protocols. A mobile network needs to know the location of your mobile phone to send you calls and data, such as emails and WhatsApp messages. Your phone also needs to connect to that network to be able to send messages back or make outbound calls.
How can it provide actionable insights?
The raw data itself cannot be used commercially as it needs to be anonymised to protect the privacy of the users of the mobile devices. Once this has been done by the mobile network the data has to be carefully filtered and combined with other data sets and algorithms to provide insights such as the most common origins and destinations for people visiting a city.
What insights can it provide?
Mobile network data can identify modes of transport, such as people travelling on foot, in a car or heavy goods vehicle or by train by calculating the speed of transport and, in the case of rail, by examining the interaction of phones with mobile cells on the rail network.
For trains, this can be extended further to calculate how full a train is, what trains people have taken, and where they have been before arriving at the station and leaving it at the end of their journey.
MND can determine the origin and destination of people’s journeys and the routes taken, which is extremely useful for city planning to help minimise congestion, delays and emissions. When used to identify trends, it can even determine the top-level purpose of the journey, be that work or pleasure for example, providing valuable information for companies such as retailers and local authorities alike.
As well as identifying movements of people, mobile network data can also identify when they are static. This can help clarify whether someone is at home or at work, although that distinction is harder to make with the increase in home and hybrid working following the pandemic. It is also helpful for planning in relation to large gatherings such as events, protests and even New Year’s Eve celebrations.
Mobile network data is available on both a real-time and historic basis, giving planners the insights they need to make informed decisions.
What are some of the use cases?
Although MND has been used to date mostly in transport and urban planning, its applications extend far further than that.
In transport, it can be used to forecast demand for services with real-time updates on train and station capacity. For HGV transport, it can help hauliers and local authorities understand movement to increase efficiency and reduce environmental impact. It is also useful in electric vehicle planning, for example in helping firms understand where to install electric vehicle charging points based on where and when people travel. In a similar way, it can help determine the best locations for Park & Ride facilities and outdoor advertising based on insights into users’ travel patterns and preferences.
In urban planning, it can help councils design buildings and roads based on how people move around urban spaces while aiding them with real-time traffic management to minimise congestion and delays.
MND also has a role to play in planning one-off events, providing insights that can be used as the basis of decisions on timing, road closures and diversions.
Further use cases are currently being explored, including assisting venues such as football stadia and theme parks in deepening their understanding of the origin, destination and behaviour of visitors.
What is the Future for Mobile Network Data?
Currently, many people have never heard of MND or have never seen it in action. As awareness grows, it is likely to be deployed in a range of contexts. The potential applications are practically limitless.
Just as apps have enabled people to devise a far wider range of uses for mobile devices than their manufacturers could ever have envisaged, MND is likely to be used in more and more ways in the future.
It can also be combined with other forms of data such as that provided by GPS and sensors, leading to an even broader range of applications.
To see what mobile network data looks like, you can download some sample data.
Want to learn more about MND? Read the Ultimate Guide to Mobile Network Data.