techUK perspective: harnessing AI in transport

30 Oct 2023 02:29 PM

Nine principles and approaches for harnessing AI in transport

As the industry responsible for the movement of people and goods, the transport sector affects everyone in society and is a foundation of our economy. Artificial intelligence (AI) has the potential to drive huge improvements in this space, from improving efficiency and environmental performance, to creating better passenger experiences.

In order to support this transformation, techUK has outlined some key principles for harnessing the benefits of AI in transport following a workshop held with our members in September 2023.

Clarify to avoid confusion

AI can be a complex area. In order to avoid confusion, transport authorities and providers adopting AI systems should be working to clarify a number of core aspects set out below.

• Defining AI: In many cases, there is a need to align understanding of what AI encompasses and the different levels of AI, including artificial general intelligence (AGI), machine learning (ML) and Generative AI. Clarifying these concepts is crucial for informed decision-making.
• View AI as a tool: AI in transport should be seen as a tool that augments human intelligence rather than replacing it. Human expertise remains essential for making important decisions in many transportation scenarios, however AI can be extremely beneficial in improving productivity, accuracy and decision making across a large number of use-cases. This is important to highlight within the labour-relations debate surrounding the transport sector.
• Identifying the use-case: Identifying clear use cases, and relating these back to wider organisational goals, is crucial for effective implementation of AI in transport. In addition, prioritising user needs is a fundamental principle, with a focus placed on designing and implementing solutions that address passengers' preferences, such as sustainability, affordability, and convenience. Establishing a clear timeline or roadmap for AI adoption in transport can also help plan and coordinate efforts across the sector effectively.

The enabling factors

AI requires the right conditions to deliver the best possible outcomes for the sector. For transport, its vital we get these right.

• Data quality: The quality of data used in AI models is a primary determinant of their accuracy and reliability. The segmentation of transport’s data landscape means the industry faces a particularly complex challenge as information is often held in non-standardised formats and varying levels of completeness that would limit the capabilities of AI to drive system-level enhancements. The industry must unite in its effort to deliver on the ambitions of the Government’s Transport Data Strategy to maximise the benefits of AI in the sector.
• Regulation, responsibility and ethics: The power of AI in transport brings responsibility for its safe and ethical use. Concerns include issues like the liability of autonomous vehicles and the potential impact on employment in the sector. As AI technology evolves, regulations are essential to ensure safety, fairness, and ethical use. The Government published its pro-innovation approach for regulating AI within its AI White Paper. The approach is non-statutory and highlights five principles that regulators will have to implement to make sure AI is deployed safely and efficiently. The regulation of generative AI is also included in this approach.
• Public perception and communication: Public perception of AI in transport can influence acceptance and adoption. Research by the Alan Turing Institute found that the UK public has broadly positive views of most AI use cases, but many are concerned about relying too heavily on technology over professional judgements. Both transport businesses and government bodies have a duty to ensure that AI systems do not perpetuate bias, are inclusive and trustworthy, and explainable to the public in order to build trust.

Supporting adoption

To support adoption of AI in transport we must ensure that the wider environment a number of factors should be considered.

• Innovation in procurement: Innovation in transport, especially in AI, can lead to improved efficiency, safety, and sustainability. However, traditional procurement processes may not be conducive to fostering these innovations. Large frameworks and bureaucratic procedures can slow down or inhibit the adoption of cutting-edge technologies. Therefore, there is a need for more agile procurement approaches and innovation funds to support creative solutions.
• Sustainable business models: There is a need to investigate and develop a sustainable business model for AI adoption. Models should be monitored continuously and adapted as necessary to ensure the best outcomes. It is therefore important to consider the operational expenditure and maintenance costs associated with AI, in addition to the upfront investment. This includes ensuring that existing technology stacks are prepared for AI integration, evaluating the commercial model to avoid lock-in and clarifying who will own the intellectual property.
• Implementation, collaboration and skills: Successful implementation of AI in transport often requires collaboration among various stakeholders, including government agencies, industry partners, and technology providers. Ensuring that organisations across the transport ecosystem have the right level of data and digital literacy is also fundamental within this journey, as well as culture of continual learning through knowledge exchange and reporting best practice.

techUK looks forward to continuing the conversation around harnessing AI in transport in the months ahead. To develop the ideas further, we will be holding a series of deeper-dive events and workshop in early 2024. We also look forward to the publication of the Department for Transport’s AI Strategy that will set out how government intends to support AI in this sector.

If you would like to learn more about our work in this area, please contact Ashley.Feldman@techUK.org.

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