CIHT report explores potential of AI in transport decarbonisation

A new report from CIHT explores the role of data and artificial intelligence in achieving transport decarbonisation. Described as a starting point for further research and discussion among transportation professionals, the report is designed to help raise awareness and knowledge “in this emerging area and signposts examples of best practice to help inspire and articulate how AI might best be used to help decarbonise transport in the years to come.”

Sue Percy CBE, Chief Executive, CIHT said, “AI is a topic that has recently been gaining greater attention and is becoming more common in our work and social lives than ever before.”

“The highways and transportation industry is already realising many of the benefits of AI, especially when it comes to improving safety, providing more insightful transport planning, and efficient asset management, as well as improving the way the public experiences transport systems.”

“This new report explores the role AI is playing and could potentially play in achieving transport decarbonisation.”

Produced with the support of the CIHT Partnership Network the report identifies examples where data and AI are already being used with respect to decarbonisation. Some of the key findings from the report include:

Existing applications

• Accelerate 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.

• Decarbonise road transport and how we get our goods by aiding site selection of public electric vehicle (EV) chargepoints; managing grid capacity for EV charging; and reducing congestion, improving traffic flow, and improving road safety to avoid traffic incidents.

• Delivering and maintaining low-carbon infrastructure by predicting asset life cycles; analysing the integrity of existing assets; and recommending low-carbon infrastructure.

Key barriers

• Lack of skills and understanding, especially when it comes to people who specialise in data/AI and possess transportation sector knowledge.

• Funding and investment – although some schemes have been set up to encourage AI innovations, more support needs to be offered to the public sector, for whom investing in new technologies can be expensive and risky.

• Open data standards are needed to ensure that the way the transport industry (and all industries in the UK) collects and stores data is standardised, which will make data sharing easier and more valuable.

• AI strategies and policies must be developed that provide leadership and guidance to the highways and transportation sector, so that AI can be confidently and ethically adopted.

Recommendations

• There needs to be a greater consideration of not just the role data plays in supporting AI technologies but also how it can be used to enhance the experience of transport users. This should be reflected in the AI regulations and standards published by the UK government.

• The government must work towards creating regulations and standards that ensure that the data collected by the transport sector is:

  • Fit for purpose, recorded in standardised formats on modern, secure, future-proof systems

  • Held in a condition that means it is findable, complete, accessible, interoperable, and reusable, and accords with open data standards where possible

• Local authorities and national bodies will be key to rolling out AI in public services such as transport, and so should be given appropriate funding, guidance, and procurement frameworks to do this successfully.

• A platform or community will be needed to share knowledge and best practice.

• A clear evidence-based approach to policy developments 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. Working across the sector to inform and educate people will build a healthy relationship between users and AI.

• The highways and transportation sector needs to build public trust in AI and demonstrate that it is incorporating AI into the sector in the safest and most ethical way possible. A Transport AI Advisory Group should be established, who will focus on public opinion, confidence, and outreach.

• The highways and transportation sector needs leadership from DfT in the form of an AI Transport Strategy that builds on the Transport Data Strategy by:

  • Identifying areas where AI can have immediate impact and initiate pilot projects to demonstrate the feasibility of these solutions

  • Promoting collaboration with other industries for the purpose of data sharing and developing an AI ecosystem

  • Developing training programmes to equip the existing workforce with the necessary AI skills

  • Publishing guidelines for ethical AI development and deployment within the transport sector

  • Looking beyond our borders to see what international learning could help us in the UK, including the strategic roll-out of data regulations and data-sharing platforms.

A full version of the report is available here