This project is a proof of concept study that explores the feasibility of integrating population growth projections, agent-based modelling and interactive simulation with existing transport design methodologies. Data sources and software from the University of Leeds' projects MISTRAL and SURF were used to implement activity-based modelling in Aimsun, a traffic simulation tool which does not currently support activity-based modelling to its full extent. The study reveals that the integration between the three components is feasible at a basic level. The methodology adopted provides a strong foundation for further research that could ultimately support the design of future intelligent transport systems.

Explaining the science

It has been estimated that more than two thirds of the world’s population will live in urban areas by 2050. This rapid urbanisation is likely to exacerbate existing problems such as air pollution, noise pollution and traffic congestion. Current research in the field of smart mobility aims to integrate big data as well as innovative ideas and technologies to design a more sustainable mobility system, providing potential solutions to these problems. A step towards achieving this is through the development of novel traffic modelling and simulation techniques that are able to generate accurate predictions of future travel demand.

This project is a proof-of-concept that focuses on modelling and visualising future travel demand by integrating data sources and software from three individual components: MISTRAL, SURF and Aimsun. MISTRAL is a large project involving the generation of high resolution population growth projections using data from the UK census and microsimulation techniques. Simulating Urban Flows (SURF) is a project that uses ‘big’ data and implements agent-based modelling with the aim of improving our understanding of movement patterns in urban areas. It incorporates real footfall data to model the typical 9-5 workday travel patterns of commuters in Otley, West Yorkshire, UK.

The third component is Aimsun, a traffic simulation tool that facilitates the analysis of numerous traffic phenomena and supports the prediction of future traffic scenarios based on existing traffic data. An integration workflow has been designed where MISTRAL produces the population for the SURF model, which in turn generates the trips to be simulated and visualised in Aimsun. Lastly, Aimsun generates the route that each agent follows to reach its destination.

Project aims

This proof of concept study aims to explore the possibility of integrating population growth projections (MISTRAL), agent-based modelling (SURF) and interactive simulation (Aimsun) with existing transport design methodologies. This could ultimately strengthen the potential of Aimsun as a tool for developing and testing future projected scenarios, therefore supporting the design of future intelligent transport systems.


This research could assist government agencies, local authorities and consultancies design future intelligent transport systems within the smart mobility framework. The outcomes could also be beneficial to vehicle manufacturers and service providers who need to understand the future environments within which their products and services will need to operate.

Recent updates

May 2019

The MISTRAL/SURF integration involved generating population projections (2018 to 2041) at the Local Authority District level and then converting them to the Output Area level, to be used as input for SURF. Upon running the SURF model using the synthetic population from MISTRAL, trip data was collected and converted to an XML file of traffic arrivals that is compatible with Aimsun. A digital twin of Otley and its surrounding areas was developed in Aimsun, where the trips were simulated. The case study comparing travel demand between 2019 and 2039 revealed that the integration was feasible at a basic level, however, numerous aspects require further improvement before beneficial insights regarding future travel behaviour can be yielded. 


Researchers and collaborators

Contact info

[email protected]