Mobile network data for Transport
Citi Logik have developed custom datasets using MND for over 100 transport and urban planning projects in the UK and abroad.
With a team of experts in population movement, transport modelling and big data, we offer a flexible, transparent approach to enable clients to exploit the huge potential of mobile network data.
Our transportdata capability provides insight on the following:
Where do people travel from and to?
Our journey generator determines where trips start and end. Rail trips can be split into ‘station to station’ and ‘first mile/last mile’ legs.
By what mode do people travel?
Our algorithms detect the mode of each trip - rail, slow mode, HGV or ‘other road-based’ mode. We also identify how many people are stationary.
Why are people travelling?
Our pattern recognition infers the purposes of each trip.
What route are they taking?
We snap the trajectories of each trip to the corresponding road or rail route. We are currently developing a ‘snap to flight’ offering for the aviation sector.
Data is vital for city and transport planners
The role of mobile network data in understanding travel patterns and road use
City and transport planners, operators and policymakers need to understand the interplay of road infrastructure, travel patterns, travel times and congestion. This is critical to plan work patterns, public transport operations and urban regeneration against a backdrop of rising populations and urbanisation - two trends exerting increasing pressure on cities' infrastructure.
Advanced demand estimation models incorporating big data sources including mobile network data can provide these insights, helping guide decision-makers on investments in road infrastructure construction, maintenance and transport operations.
More reading: "The future for Mobile Network Data in transport".
Mobile network data is cheaper and provides more information
The rise of mobile network data
The use of anonymised mobile network data (MND) to understand mobility patterns has become increasingly popular in recent years. Traditional survey methods are expensive, potentially disruptive, prone to human error and provide only a limited snapshot in time.
MND is considerably more cost effective and can provide information on population and vehicle flows both historically or in real-time. MND has a valuable role as an integral data source providing insights to better inform decision making in the development of transport planning models.
transportdata projects
We take a consultative approach, tailoring our data solutions to meet the specific needs of each client.
All projects include:
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Comprehensive pre-sales technical engagement to ensure a smooth project
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Up to three progress meetings/workshops
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Report detailing methodology and assumptions
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Report describing the verification checks undertaken
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Support line for queries post data delivery
Examples
How mobile network data can transform transport planning
Understanding travel movements: North of England
Citi Logik has been providing data and insights to support a strategic transport improvement programme across the North of England. The dataset has been used to understand the following:
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Station-to-station movements for rail journeys
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‘True’ origins and destinations of journeys whose primary mode is rail
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How rail travellers travel to and from railway stations, both in terms of mode of transport and ultimate origin/destination
Helping a London airport gauge coach route demand
Citi Logik helped a busy London airport build MND origin and destination matrices. This enabled them to understand the potential demand for coach routes to and from the terminals, both within the airport catchment and outside of it.
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Static, slow-mode and motorised trips
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Purpose categories of journeys whose primary mode is rail
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How rail travellers travel to and from railway stations, both in terms of mode of transport and ultimate origin/destination
transportdata Summary
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Bespoke multi-modal mobile phone datasets
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12 month archive for UK customers
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Used in over 100 transport and urban planning projects
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Developed by transport modelling experts
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R&D opportunities welcomed
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Option to visualise datasets within
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