Mobile Network Data and its uses

What is Mobile Network Data?

Most people in the UK possess a mobile phone and carry it around with them.

 

These phones communicate constantly with mobile phone networks, providing information about where those people are going from and to, at what speed and where they are stopping.

 

This information can provide a rich source of data for decision-makers across a range of sectors - everything from transport and urban planning to minimising water wastage.

Mobile Network Data the Concise Guide

Everything you need to know about using MND to understand how people move in urban areas

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Key topics covered in this guide:

  • What is mobile network data?

  • How MND has become a critical data set for transport and urban planning

  • Strengths and limitations of MND

  • How MND complements other data sets

  • Current uses of MND

  • Potential future applications

 
Data visualisations

1. What is Mobile Network Data?

An invisible network of sensors

Mobile network data can be described as the exhaust fumes for the mobile network.

 

It is the data from the largest sensor network in the UK that nobody knows about: the mobile phone network.

 

Every mobile device acts as a sensor, providing information on its owner’s movements. It you were trying to create such a network of sensors, it would involve huge amounts of money.

 

By contrast, mobile network data exists already. It involves no additional costs, no maintenance and no additional infrastructure.

2. The History of Mobile Network Data

Since 2015, MND has played a critical role  in transport and urban planning 

Any major transport or urban planning infrastructure project requires a large-scale capital investment programme, usually by local, regional and central government.

 

To ensure that money is being spent appropriately, data needs to be collected to create an evidence base for the project. This data provides the answers to questions such as why is the project needed, what its impact is likely to be and where it should be located.

 

Before the advent of MND, the main means of collecting data was roadside interviews. The outcome of that was a relatively small amount of data, which was extrapolated to show how people moved through a city. The data was limited, difficult and costly to obtain and required multiple resources including those of the police, who were needed to stop cars for the interviews. The market was ripe for disruptive technology.

 

Instead of using roadside interviews, the normal exchange of information between a mobile handset and the network via signalling protocols could be used. In simple terms, a mobile network needs to know where you are to route calls and data to you. It does not need to know exactly where you are – GPS tracking would be required for that – but to within the area of a mobile network cell.

 

This technology did not develop until 2012 and was first used for a commercial purpose in 2015.

 

By developing advanced algorithms and applying them to the raw MND, a product was developed to replace roadside interviews for transport planners and engineering consultancies.

Initially, these groups were sceptical. They were concerned about whether the insights that this data generated would be consistent with the existing understanding of what constituted normal behaviour in cities. Some transport planners worried that the new technology might make their roles obsolete.

 

Luckily, the mobile network data affirmed rather than undermined the previous findings about how people move around cities.

 

However, the volume of data was transformed. Instead of being thousands of journeys, planners were able to review tens or even hundreds of millions of journeys. These yielded better and more robust insights and generated requests for more data.

 

For example, in Liverpool, MND showed that a large proportion of the shopping traffic involved people trying to find car parks. When shoppers were able to find a parking space, it was often on the wrong side of the city. The local authority used insights from MND to reduce congestion by enhancing the signage into the city centre to clarify where drivers could find available parking spaces.

 

It became possible to carry out studies that would have previously been impossible, for example on motorways and A-roads where it is not safe or practical to stop cars for roadside interviews.

 

For transport planners, mobile network data became the ubiquitous data set for movement analytics.

 

It is now beginning to be used in other areas, such as airports, ports. HGV, freight and population movement.


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Transport Data insights in to population

3. Turning Mobile Network Data into actionable insights 

Raw data is only the start of the process

MND can provide insights into the way people move on foot, in a vehicle or by train.

 

By identifying and analysing demand activity in innovative ways, MND is deployed to tackle complex spatial challenges in the sectors of Transport, Intelligent Mobility, Smarter Cities, Retail & Advertising, and the Built Environment.

  

The emergence of predictive analytics coincides with an increasing recognition across UK plc and within the public sector of the value of the data and the insights it can provide.

 

A recent Ofcom report has confirmed that, in the UK, a third of internet users see their smartphone as the most important device for going online. With more two thirds of people now owning a smartphone and using it for nearly two hours every day, it is becoming the hub of our daily lives.

 

Using mobile network data to capture movement patterns means that analyses can be based on a much larger sample size than traditional survey methods. It can also be combined with other data, such as GPS data for example.

Raw mobile network data itself won't give you actionable insights. It needs to be carefully filtered for the applicable geographic zones and timeframes, processed, mapped, expanded, converged and verified. It needs to be anonymised and GDPR compliant.

4. What can mobile network data be used for?

MND has a huge range of potential applications

MND is particularly suitable for transport, urban planning, rail, vehicle movement, population density, rail travel and airports. 

Further use cases that are currently being explored include geofencing football grounds to gain a deeper understanding of the behaviour, origin and destination of football crowds. Using MND for deeper insights into theme park visitors is also under consideration. 

 

MND is good for scale and giving the big picture. Using MND, 10m observations can be taken in two weeks, whereas GPS can record 1m observations in a year.

 

MND is less good for studies where accuracy is more important than the sample size.  MND gives accuracy of up to 50m in urban areas, whereas GPS provides accuracy of around 5m. However, it is possible to blend the two, for example to calculate speed.  Indeed, MND is often combined with GPS and other datasets.

a. Origin and destination - people movement

Where do people travel from and to?

Understanding where people are travelling from can provide insights into commuting, residential areas and shopping. Routes tell you a lot about the purpose of a journey. 

MND helps to form a picture of people’s movements over time. From where their phone sits at night and during the day, it is possible to infer home and work locations. It is best to get four weeks’ worth of data for transport planning. The more data someone uses, the more valuable the data is for them.

 [Introduction to the section .......    For each of these sections, cover things like

  • how this is measured

  • why it's important

  • where this information can be / is used (examples + links to sectors or case studies for example)

  • the impact or ROI of having this information]

Origin Destination of journeys in Hull

b. Population Density

Where are people now in any given time

MND can be used to view historic and near-real time dynamic population counts to understand the number of people around your city for any given time.

This is invaluable for planning for large-scale gatherings such as New Year's Eve and Hogmanay, pop concerts, football matches and protests / demonstrations. 

 

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citianalytics-PopulationDensity

c. Mode of transport

By what mode do people travel?

Modes of transport can be split into motorised (which comprises car, LGV, HGV, bus, motorcycle and rail) and active trips, which consists of walkers and cyclists. Any device travelling at high speed on the road network is identfied as motorised. 

 

Walking and cycling trips can be identified by their average speeds. It has been calculated that the average cycling trip takes around 24 minutes to cover 3.5 miles, while the average walking trip takes around 16 minutes to cover 0.7 miles.  These give rise to average cycling and walking speeds of around 3.89 and 1.17 metres per second.

 

It is possible to distinguish rail journeys from other motorised trips by analysing the frequency with which devices interact with cells around the rail network and train stations. This is cross-referenced with train timetables to identify which train(s) passengers are taking. As well as the first and last stations, MND can calculate station catchment areas by seeing where people leave home and go after leaving the station at their destination.

d. Journey purpose

Why are people travelling?

Discovering the purpose behind a journey is crucial. With this in mind, you can differentiate between commuters and visitors. It is also possible to analyse the day and night-time locations to understand whether a person is at home or at work, although this distinction is less clear-cut with the rise of working from home resulting from the Covid-19 pandemic.

 

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e. Transportation routes

What route are people taking?

Understanding routes can help with analysing emissions, congestion, the planning of new infrastructure and contributing to the Government's decarbonisation agenda. 

citianalytics-UnderstandingMovement.jpg

f. Real-time and Historic travel trends

What are the travel patterns at different times or locations? 

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citianalytics-RoadsRealTime-Historic-Corridors

g. Pollution reporting

What is the impact of travel on air quality? 

MND can also be used to monitor travel movements and can be combined with sensors to  assist with pollution monitoring. Transport planners use the data to influence where HGVs go in a city.

 

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citianalytics-PollutionWatch

h. Occupancy

How many people are occupying trains, stations or venues? 

MND can monitor the usage of stations without reference to ticket sales and rail journeys. It is often possible to use a web portal to display the occupancy level within a station in real time. MND is used from a mobiel network to understand movement into and out of a station. This approach removes anyone who is within the area over extended periods, such as station and retail staff and people living and working next to the station. 

 

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OD-NetworkRail.jpg

i. The impact of delay

How much disruption will there be as a result of delays?? 

Following a major event that causes a delay, transport firms and planners often engage in what if scenarios. They like to analyse after the event how to improve how things are run.

 

sollicitudin massa vel sapien aliquam, a rhoncus leo iaculis. Suspendisse at elit feugiat, ultricies est vel, pellentesque enim. Vivamus placerat lacus vel ante cursus egestas. Nunc quis neque ornare, venenatis tellus eu, suscipit leo. Proin at tempor urna, vitae molestie orci.

 

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j. Travel forecasting

How much traffic will there be and where? 

Traffic forecasting has become sharply in focus due to the changing commuter behaviour brought about by the pandemic. 

The mode of transport and the times commuters choose to travel are expected change, at least for a period of time. 

 

Changes in commuter behaviour have triggered changes in how transport planners approach their work.

 

People are looking to delay updating their models and do not want to use data from 2019 because they are waiting to see what the new normal is going to look like.

 

However, waiting for a return to normality is not without risks. The risk is that planners are using an outdated model. Relying on existing data to make decisions about roadworks, highway planning, traffic flows and even cycle lanes will prove to be costly and statistically unreliable. Understanding the dynamics of how a city lives and moves in the new world is a whole new challenge.

Robust data can give transport planners and local authorities the evidence they need to win the support of residents and other stakeholders. With the right data, councils can easily explain and prove to their residents why they are making certain decisions or changes around road usage.

k. Location planning

Where is the best location for ......? 

By understanding the movements of people and traffic, MND can offer invaluable insights into the best locations for everything from residential developments to Park & Ride facilities to electric vehicle charging points. 

Data for City Planning

5. Mobile Network Data applied

How do mobile network data analytics fit into management and planning?

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a. Urban Planning

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

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b. Rail

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

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c. Civil Engineering

How is mobile network data used in ....? 

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d. Transport Planning

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

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e. HGV Movement / Trade planning

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

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f. Electric Vehicle (EV) planning

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

Quisque sollicitudin massa vel sapien aliquam, a rhoncus leo iaculis. Suspendisse at elit feugiat, ultricies est vel, pellentesque enim. Vivamus placerat lacus vel ante cursus egestas. Nunc quis neque ornare, venenatis tellus eu, suscipit leo. Proin at tempor urna, vitae molestie orci.

 

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g. Park & Ride

How is mobile network data used in ....? 

Introduction to the section .......    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus tellus dui, blandit non molestie in, ornare id tortor. Sed in pharetra est, non semper lectus. Morbi fringilla, lorem nec viverra cursus, ipsum nibh facilisis enim, tristique interdum nisi eros sed nunc. Aenean nec tempor eros.

 

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h. Housing developments

How is mobile network data used in ....? 

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Dorset County Council Case Study

6. The Future for Mobile Network Data

Conclusion

MND is still seen as experimental by some markets because it has not previously been applied in them. Many people have never heard of it or seen it in action.  

 

The challenge for the future is to make this data more broadly accessible and easy to access in a timely fashion. 

 

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API's from Citi Logik

THE CALL TO ACTION SECTION

Find out more or get in touch etc etc

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Citi Logik API quick and easy access to data
 
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Look at some sample data

Mobile network data insights can help you to accelerate your decision making process.

Why not take a look at some sample data to see how it could help to make your next project a success.

Case Studies

Case Studies

See how our data has helped other organisations.