Traffic digital twins: a fast track to environmental sustainability

January 10th, 2024

Article first published in Traffic Technology International Magazine
Digital twins are powerful simulation tools that can accurately replicate complex real-world systems. They provide a safe digital environment within which to test and evaluate the outcomes of investments, and proactively manage new and existing infrastructure to increase capacity and mitigate against air pollution and traffic-related emissions. These tools, when deployed carefully, can help us tackle some of the biggest issues in the transport sector, such as optimising traffic flow to prolong the life of existing assets or improving bus reliability to move people more sustainably. 

What are traffic digital twins? 

Earlier this year, a digital twin was officially defined by the UK Government Office of Science as “a cyber-physical system that links a computational representation of a physical asset, entity or process with a two-way flow of right-time data from installed sensors on a physical twin.” This definition is a perfect match with traffic digital twins such as they are understood and created by transport technology firm, Aimsun. Aimsun’s digital twins can include traffic flow, vehicle routing, and even environmental information such as air quality. It is this ability to consume substantial quantities of data from various multiple sources in real time, and accurately simulate the likely outcomes that makes them so important to maximising positive environmental impacts for the transport sector, both for the future and the present day. 

How can they help? 

Digital twins are specifically designed to replicate large-scale complex systems, such as national transport networks. They can account for the multitude of complex interactions within and between systems. Aimsun’s traffic digital twins have demonstrated that they can provide sophisticated planning and operational management tools in a single platform, helping to achieve desired outcomes; they can also proactively identify network incidents and late buses to mitigate against congestion and decrease delay, thereby improving customer experience and journey times. That’s not all!  When paired with air quality modelling systems, Aimsun’s digital twins can also predict air quality exceedances so that authorities can proactively manage of traffic to minimise stop-start conditions, reduce exhaust emissions, and stay within legal pollution thresholds. 

Enabling digital twins 

Data is the fundamental element required to install a digital twin in any transport management system. Although digital twins employ artificial intelligence methods, often in the form of machine learning algorithms, they are subject to the same basic principles of any model. That is, garbage in- garbage out (GIGO). If models are fed with low quality or inaccurate data, the results of any analytics will be of poor or diminished quality. Therefore, before deploying a digital twin to solve your traffic woes, it is important to carry out a health check of the traffic sensing network to identify and address your gaps. This is often recognised by good suppliers of digital twin solutions, who will check the available data inputs, identifying and addressing data gaps with data driven approaches alongside recommendations for new and existing data sources. 


Where to start? 

Digital twins are big, they are exciting, and they can also be daunting, but this shouldn’t be a barrier to their implementation. The first step for transport authorities is an exploration of the scope and quality of their data. This is key to understanding what is possible and is the first step in the process of deployment of demonstrations and proof-of-concepts. This pathway is baked into the Aimsun digital mobility solutions ecosystem and is key to maximising the utility and success of traffic digital twins.  


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Cite Aimsun Next

Aimsun Next 24

Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].


Aimsun Next 24

@manual {AimsunManual,
title = {Aimsun Next 24 User’s Manual},
author = {Aimsun},
edition = {Aimsun Next 24.0.0},
address = {Barcelona, Spain},
year = {2024. [Online]},
month = {Accessed on: Month, Day, Year},
url = {},

Aimsun Next 24

T1 – Aimsun Next 24 User’s Manual
A1 – Aimsun
ET – Aimsun Next Version 24.0.0
Y1 – 2024
Y2 – Accessed on: Month, Day, Year
CY – Barcelona, Spain
PB – Aimsun
UR – [In software]. Available: