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The role of AI in decarbonising transport
New research by the Chartered Institution of Highways and Transportation (CIHT) outlines how rapidly developing AI technologies are impacting the transport sector. With a special focus on decarbonisation, Dr Isobel Wilson highlights what this could mean for design.
At the start of 2023, the Chartered Institution of Highways and Transportation (CIHT) identified artificial intelligence (AI) as an area in which there has been a sudden surge of interest. CIHT members were asking us for more information on AI and how it could change the transportation sector as we know it.
We set out to investigate how this rapidly evolving technology could be used to solve some of our sector’s largest problems by looking at how data and artificial intelligence can be used to achieve transport decarbonisation. A working group was formed of 16 CIHT members and partners, representing academia, local authorities and industry to evaluate where the sector is currently producing and adopting AI tools, and where we might be in the future, with the aim of producing a report from our findings.
On one end of the scale, we spoke to people who were only just appreciating how much data they collect and weren’t sure how exactly to utilise it to get the most value out of it. We also held an event early in the project to showcase how AI is being used in the transport sector, and we saw many CIHT members commenting they had no idea AI was already so advanced and not ‘just ChatGPT’. Then on the other end of the scale, we had the opportunity to speak with operators of AI companies who have been using and developing this technology for over 15 years. These experienced practitioners were able to give us a more holistic opinion of AI, having worked with it for so long, and were understandably sceptical of the current hype surrounding it.
We were pleased to use these insights to gather a total of 21 case studies that highlight the multiple ways that AI is already being used to decarbonise the highways and transportation industry. These case studies were grouped into three actions where AI could be used to decarbonise transport, which were:
Accelerating modal shift to public transport and active travel by creating reliable databases on sustainable transport use; optimising traffic flow in favour of active travel and public transport; and monitoring the condition of active travel infrastructure.
Decarbonising road transport and how we get our goods by making it easier to plan for and use electric vehicle charging infrastructure.
Delivering and maintaining low-carbon infrastructure by predicting asset life cycles; analysing the integrity of existing assets; and recommending low-carbon infrastructure.
A common theme that underlined many of the case studies we profiled was the ability of AI technology to aid decision making, especially when it comes to designing and maintaining the built environment.
We included many examples of ‘perceptive AI’ systems, which receive and process data to understand a situation. For example, we profiled one company, XIAS, which equipped a mobility scooter with cameras to travel where cars can’t, to capture asset and condition data on footways and cycleways. This data is then processed using AI to detect, measure, and highlight defects, which can then be acted on to design active travel infrastructure that is safer and more reliable. Similarly, we included an example of a product, the See. Sense Smart Cycling Project, which conducts AI analysis of crowdsourced sensor data gathered from cyclists using their own personal bikes. Again, this is incredibly useful for designers, so they have access to reliable information that reflects the user experience of cyclists.
We also found examples of ‘predictive AI’ systems that receive and process data to anticipate or forecast future scenarios. This included AI systems like the Mind Foundry Platform that collect and process geospatial data, along with data provided by energy networks, to recommend the optimal type and location for a public EV charge point. Likewise, we also profiled Arcadis’ Enterprise Decision Analytics, an AI asset-management tool that uses the historical data of an asset to forecast its deterioration and predict when it will need maintenance, helping to reduce the cost of routine maintenance work.
These AI systems, and the data that feeds them, could have the potential to really change the way we design for the built environment. Being able to use AI to ask data more detailed and nuanced questions and receiving the answers in real time will be another useful tool in a designer’s wheelhouse. In the coming years we will hopefully see these technologies being used to create spaces for communities in a process that considers far more intricacies and details than it would be possible to today, ensuring that when we need to build, it is done in a way that will bring long-lasting value.
AI is a continuously developing field, making it hard to predict the pace and direction at which new technologies will emerge, even within the next three years. Even when predictions are made, it is difficult to know whether they will be fulfilled or if external factors will influence AI’s success. It is also important to remember that AI will not be the answer to everything, and other technologies can also be used to solve some of these challenges. Most of all, we need human ingenuity to mould these technologies to our needs, to apply them so they enhance our decisions, while we remain cognisant of their limitations.
This is why collaboration between all sectors will be important for making AI a success, especially when it comes to data – both quantitative and qualitative. A clear evidence-based approach to policy development is critical, particularly when it comes to public understanding around the adoption of new and emerging technologies within the transport sector. Regulators and organisations such as CIHT should work together to ensure that unbiased evidence on the pros and cons of AI is well communicated and shared widely. Informing and educating people can help to build a healthy relationship between users and AI.
By fostering collaboration across the built environment sector, it is hoped that we will be able to use AI tools that help us to design and build infrastructure that is as beneficial as possible to the environment and the communities our professions are here to serve.
Isobel Wilson is a Policy Advisor for Transport Technology at the Chartered Institution of Highways and Transportation (CIHT).
