WM5G Capacity Manager2021-06-10T13:21:05+00:00

Project Description

WM5G – pilot of 5G enabled dynamic network capacity manager

Harnessing the power of 5G and machine learning to maximise the capacity of transport networks


leadership icon

Thought leadership on technology and logistics


Department for Digital, Culture, Media and Sport (DCMS) and the West Midlands Combined Authority (WMCA)


Ongoing since March 2020

The challenge

Britain’s roads are increasingly congested, which wastes huge amounts of time, money and carbon.  Over 80% of passenger journeys are by road and traffic volumes are still rising steadily, despite the pandemic.

The scope for building new roads is minimal so we need to optimise our use of the ones we have.  There are more than 2.5 million roadworks on UK roads every year, and network managers also have to deal with the impact of accidents, bad weather, special events and security alerts.

Currently, transport managers are trying to tackle 21st century patterns of travel and network dynamics with 20th century tools.  They have limited ways to shape strategic responses and severe limitations in their ability to monitor the network and to refine tactical responses.

Ultimately, network managers lack access to reliable, real-time data to inform either immediate decisions or strategic analysis of the problems.

The solution

Under Director John Fryer and Associate Director Claire Woodward, blacc is leading a consortium of industry experts to create a ‘5G Enabled Dynamic Network Capacity Manager’ for the West Midlands city-region.  The other members of the consortium are Immense, one.network, University of Warwick and O2.

This is a unique, cutting edge pilot with enormous potential to save time, boost productivity and reduce harmful emissions (both local pollution and greenhouse gases).  The project’s ambitious goal is to produce a scalable, productised solution that will enable road authorities across the UK and internationally to plan and respond to changes in network capacity.

Key elements of the solution include:

  • An Innovative solution to exploit 5G traffic sensing data for dynamic traffic management. We will augment this new data source with more traditional data sources to construct a robust data repository that will be the basis for our computational solutions.
  • A suite of modelling-based queries and scenarios for decision makers to model a range of traffic management strategies.  These will primarily be based around ‘what-if’ scenarios for planned or unplanned incidents.
  • An intuitive user interface provides real-time insights and predictive modelling to the user, empowering action.
  • An approach where 5G roll-out can provide increasingly performant and customer-focused tools

The result

The most innovative and powerful outcomes of the pilot flow from the interface between extensive, accurate and real-time data from 5G with machine learning technologies and other analytical and modelling tools:

Increased capacity

  • Larger and more timely data for more powerful machine learning
  • A more comprehensive view of the network

Lower latency & faster communication

  • Real-time inference using fast data streams
  • Effective use of cloud/servers for scalability and efficiency of machine learning and agent-based simulations

Dynamic management and intervention on the network

  • Efficient modelling enables up-to-date predictions,  modelling congestion and planning for road closures

5PRING Commercial Application Accelerator 

  • Aim is to help organisations of all sizes harness the power of 5G to deliver growth & innovation
  • O2 is investing more than £6m to deliver a further 115 5G cell upgrades across the West Midlands

This project is ongoing, yet the clients and the consortium have already agreed the key prerequisites and success criteria for the completion of the pilot:

Mark Corbin, Key Route Network Manager at TfWM, has commented as follows on the size of the prize:  “The development and deployment of this tool has the ability to completely transform our approach to congestion management and deepen our understanding of real time capacity in the operating environment.”