AI is changing transportation not just inside vehicles, but in the systems around them: traffic operations, transit dispatch, connected and automated transport, and services that help people combine modes. These technologies can help agencies process data and make decisions, but the results depend on infrastructure, data governance, service design, and safeguards. Official sources from the United States, the United Kingdom, and the European Union describe potential benefits and ongoing work—not proof that AI has made transport universally safer, faster, cheaper, or lower-emission.
Where AI fits in the wider transport system
AI in mobility is not a single technology or a synonym for self-driving cars. It can be part of a broader digital system that gathers transport data, analyzes conditions, and supports decisions by operators or travelers. European Commission materials describe applications ranging from traffic-flow management and connected and automated mobility to Mobility as a Service (MaaS), which brings travel options together across modes.
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Those applications rely on more than algorithms. The European Commission’s overview of transport digitalisation places AI alongside the Internet of Things, cloud and edge computing, and 5G. Sensors, communications, computing, and compatible data systems help make information available where it may be useful; AI can then help interpret it or support an operational decision.
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Traffic operators have to make decisions across a network, not just respond to what one driver sees. The European Commission Publications Office’s 2021 report AI watch, AI uptake in smart mobility identifies traffic-flow management as an area where AI may be applied. In principle, data analysis can help operators understand changing conditions and coordinate responses across roads and infrastructure.
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That possibility is not the same as a proven improvement in travel time or safety. The report also identifies data sharing, protection, and standardisation as continuing challenges. A system that receives incomplete or incompatible information may not give operators a reliable view of conditions, and decisions still need appropriate oversight.
Connected and automated vehicles need a connected system
Connected and automated mobility includes vehicles that exchange information with other vehicles or infrastructure, as well as transport that performs some driving tasks automatically. It is therefore a network and governance question as much as an onboard-technology question: infrastructure, data, safety practices, and coordination all affect what can be deployed.
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The European Commission says connected and automated mobility has the potential to improve efficiency, safety, accessibility, and emissions outcomes. Those are potential benefits, not measured results established across deployments. The Commission’s 2025 report Research and innovation in smart mobility in Europe assesses 247 projects, primarily funded through Horizon 2020 and Horizon Europe, and covers areas including connected, cooperative and automated mobility (CCAM), vehicle-to-everything (V2X), digital twins, and multimodal traffic management. That figure counts research projects; it is not a count of commercial deployments or proof of their impact.
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What automation and AI may change in public transport
Transit automation is a research and demonstration agenda
The U.S. Federal Transit Administration’s Strategic Transit Automation Research Plan 2.0: 2023–2028 sets out enabling research, integrated demonstrations, and stakeholder engagement. Its stated goal is transit automation that is safe, equitable, and effective. The plan, updated on the FTA website on February 20, 2025, describes a programme of work; it should not be read as evidence that transit services across the country have already adopted automation.
Dispatch can be data-driven without being fully automated
Transit agencies also face decisions about how to assign vehicles and serve trips that do not fit a fixed route or timetable. The FTA’s publications index lists a September 2024 report on dynamic, data-driven microtransit with AI smart dispatch. It is an example of research and operational design, not evidence that every agency uses AI or that a particular service outcome has been achieved.
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Automation can change what staff monitor and how decisions are made, but it does not remove the need to evaluate safety, service reliability, workforce effects, maintenance, and access. The National Academies’ transportation technology collection includes materials on mobility inclusion, effects of connected and autonomous vehicles on state Department of Transportation maintenance, and procurement and management of advanced technologies. These are relevant implementation concerns, not a guarantee that one approach will work in every agency.
How Mobility as a Service connects trips across modes
MaaS brings multiple transport options together through a service or application, with the aim of making multimodal travel easier to plan or access. The European Commission says that “Mobility as a Service (MaaS) by allowing seamless multimodal travel and facilitating access to greener alternatives, can help to decarbonise transportation.” The wording is appropriately conditional: a service can make options easier to use, but that alone does not establish a quantified emissions reduction.
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- Directory of U.S. national parks simplifies navigation to entrances, visitor centers and landmarks within the parks
For MaaS to coordinate a useful journey, the underlying services and data need to work together. Availability, schedules, fares, and other trip information have to be accessible and usable across systems. Whether a digital service improves access also depends on who can use it and whether the options it presents meet travelers’ needs; access is a potential benefit, not proof of equitable outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the figures do—and do not—show
The European Commission’s June 2024 transport overview reports that EU passenger transport grew 33% and freight transport grew 41% between 1995 and 2019. These are historical changes in transport volumes, not effects attributed to AI. The same overview describes an EU policy goal to reduce transport greenhouse-gas emissions by 90% by 2050. That is a long-range policy target, not an observed result or an AI-specific target.
These distinctions matter when judging claims about smart mobility. A policy target, research plan, project portfolio, or demonstration can show what governments and researchers are pursuing; it does not by itself establish that a technology has delivered a particular safety, cost, travel-time, access, or emissions outcome.
Why deployment depends on governance as well as technology
- Data quality and compatibility: useful coordination depends on data that can be shared, protected, and standardised across relevant systems.
- Fairness and transparency: the European Commission’s 2021 AI Watch report identifies algorithmic fairness and transparency as areas requiring further attention. AI decisions should not be treated as neutral simply because they are automated.
- Safety and accountability: automation requires clear evaluation, oversight, and responsibility for decisions. A recommendation or research agenda is not a substitute for evidence about operational readiness.
- Inclusion and public value: a service may offer new ways to travel, but its value depends on who can access it and whether it serves their needs.
- Infrastructure and workforce readiness: connected systems need supporting infrastructure, while agencies must consider procurement, maintenance, and how new systems affect staff and first responders.
Policy approaches also differ by jurisdiction. The UK Department for Transport’s Transport AI Action Plan, published June 10, 2025, sets out the department’s approach to working with AI within government and across the transport system, including user experience, growth, and risk management. It is a government plan, not confirmation that each proposed application has been deployed. EU policy and research materials, U.S. federal recommendations, and a UK departmental plan describe distinct contexts and should not be treated as a single global rulebook.
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