Geospatial Analysis of Mobile Telecommunications Signals

Mobile Telecommunications

In large capital investment projects it is always important to gain access to reliable evidence to support investment decisions.

182M

Data records collected to assess the quality of mobile telecoms signals along the national railway network

35K

Kilometres of the railway network mapped with a quantitative and qualitative assessment of 4G, 3G and 2G mobile telecoms signals

65%

Of the railway network in the UK is planned to benefit from over £1.3B in funding for new mobile telecoms technology

Situation

Over the next five years the UK government plans to invest over £1.3 billion in new telecoms equipment to maximise mobile phone coverage across the railway network. Our client is responsible for providing the railway industry with telecoms connectivity that is essential to the safe, reliable and efficient operation of the national railway network.

Technology

In the process of planning for investments in new mobile telecommunications equipment our client needed to understand which geographical locations along the railway network could benefit most. In the past the client had mapped the network with specialist equipment mounted on the roof of a train carriage, but this information was biased because it did not provided insights into the strength and quality of the signal received by passengers within the train using different Mobile Network Operators (MNOs).

Analytics

Our team designed and built a self-service analytics tool based on crowd-sourced data from passengers travelling on the railway network. We combined open-source data from the Office of Communications (Ofcom) and proprietary data from the client. This combination of data sources provided us with a unique opportunity to map the strength and quality of mobile telecoms signals across 35,200 kilometres of railway track and 152,771 square kilometres of land. We used a combination of techniques, including statistical signal processing, clustering algorithms, geospatial and spatiotemporal data analysis to extract intelligence from the data and communicate evidence to the client in a simple and easy to understand format.

Results

By combining data from a variety of sources our team was able to build a solution to reliably assess the quality and strength of mobile telecoms signals received from specific MNOs in specific geographical locations across the United Kingdom. Our solution is helping our client gain an understanding about where new telecoms equipment is needed to improve the quality of mobile telecoms services for passengers travelling on the railway.

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