Oxfordshire Traffic Management for New Mobility


Oxfordshire County Council wanted to explore emerging technology opportunities which harnessed the potential of upgrading the quality and range of digital data used to modernise its Traffic Network Control Centre. Through the GovTech Catalyst, Oxfordshire sought to develop a traffic management system suitable for traditional vehicles and ‘new mobility’ technologies transforming the movement of people and goods in Oxfordshire. Their aspiration was to develop a system that could model and manage new mobility systems including connected and autonomous vehicles, electric vehicles, drones, dockless bikes, electric bikes and other forms of micromobility.



City Science were commissioned to undertake a feasibility study for a system that would accommodate integration between CAVs and traditional traffic and collaborative optimisation across the whole region. The objectives of the system included:

  • Identify data gaps and the minimum viable infrastructure needed to close them
  • Develop effective ways to utilise open source data and be able to share data insights between different teams and organisations
  • Produce metrics adaptable to the Council’s policies, including changes in key performance indicators such as accessibility, reliability, incident response, air quality or modal shift
  • Ensure compliance with data privacy regulations.

City Science Response

We conducted detailed technical research to establish the feasibility of our new modelling and optimisation system. This included:

  • User Centric Design Review: We tested the core customer proposition through holding interactive stakeholder workshops with local authority users from Somerset, Birmingham, West Midlands, Devon, Southend-on-Sea alongside Oxfordshire. This feedback allowed us to glean key themes and refine the system to better suit users’ needs.
  • Technical Feasibility: We assessed the technical feasibility of the system architecture with a focus on assessing the challenges and solutions to maximise its openness whilst concurrently retaining security and privacy of the data. This included a literature review covering CAV use-cases and algorithms and a review of existing UTC / UTMC technology and integration points.
  • Sandbox Microsimulation Model Development: We developed a sandbox model drawn initially from the Oxfordshire Strategic Saturn Model into an open source microsimulation package using SUMO. Its development was supplemented by real world data to confirm reasonable behaviour. Delays, fuel consumption and carbon emissions from the SUMO model were then fed into the development of an evolutionary algorithm.
  • Evaluation of Benefits: The benefits of the system were developed and quantified to ascertain its anticipated value to Oxfordshire County Council. This demonstrated that the use of new evolutionary algorithms were able to identify delay reduction strategies with potential to reduce waiting times by 11%.
  • CAV Use-Cases: We explored the immediate and potential adaptability of the system to future CAV use cases.
waiting times
Our analytics were able to show which junctions in Oxford have the greatest impact on waiting times.


We developed a technical solution focused on the user benefits that such a system could deliver. Building on existing integrations with highway models and geospatial data we were able to interface with other Open Source software and apply new cutting-edge optimisation algorithms to demonstrate significant waiting time and associated carbon reduction improvements at signalised junctions in Oxfordshire.

This work has subsequently been extended, and through the use of cutting-edge Meta-Heuristic algorithms, developed in collaboration with academics at the University of Exeter, waiting time improvements of between 31%-54% have been identified for a range of sub-networks.

To read more about our approach to converting between traditional four-stage transport models and SUMO microsimulation models click here