How AI will define cities and mobility in 2026

As cities continue their digital transformation, the foundations laid by data integration, IoT, and advanced connectivity are now being accelerated by artificial intelligence – a force that is redefining not only how cities function, but how they think, learn, and adapt. 

In the past year, the major trends in urban innovation – focused on AI, digital twins and smart city platforms – have remained consistent, but have also clearly matured. AI is now becoming a central element of city management, touching virtually every domain from operational efficiency to citizen experience. Simply put, AI is no longer just analysing data – it is powering dynamic, real-time action in critical urban systems that we all depend on, such as urban mobility, public safety, energy and utilities on a city scale.  

Making city operations more adaptive and human-centred 

As cities integrate data across systems, leaders are gaining a more holistic view of their operations. This shift enables predictive, data-driven decision-making rather than reactive problem-solving. 

Building on this, AI and analytics are amplifying that visibility by helping cities become more adaptive. With access to real-time information, operations can be dynamically adjusted to respond to changing circumstances. This adaptiveness now extends beyond traffic and transport to areas such as urban safety, public services and environmental management. 

At the same time, AI is driving a growing emphasis on personalisation. Governments are increasingly designing services and mobility systems to be more user-centric, convenient, and accessible. In mobility, for example, the evolution of AI-driven, multimodal transport orchestration enables systems to respond instantly to real-time conditions – improving efficiency, safety, and the commuter experience. 

One of the biggest challenges for cities adopting emerging technologies has always been translating abstract potential into visible, measurable impact. Demonstrating working solutions under real-world conditions has proven vital to building confidence and accelerating adoption. 

The AGIL® Urban Traffic Management System (UTMS) provides a good illustration of how AI-powered platforms are transforming urban mobility. Deployed in cities such as Dubai, Abu Dhabi, and Singapore, the AGIL UTMS collects live data from road infrastructure and feeds it into a central platform, enabling predictive capabilities like traffic flow forecasting and automated incident response. With these tools at their fingertips, city and transport agencies can immediately see how the technology enhances efficiency and generates insights, supporting faster, more informed decision-making. These systems also reduce travel time, directly contributing to lower emissions and improved air quality.

Singapore’s public transport system offers another example. Its AI-enabled fleet management platform oversees more than 6,000 buses across multiple operators, using digital twin technology to ensure bus service reliability and dynamically respond to delays. The system communicates directly with drivers to make real-time adjustments, resulting in smoother services, improved safety, and better passenger experiences. This not only boosts passenger confidence but also encourage a modal shift away from private car ownership – a vital step toward greener mobility ecosystems. 

Bringing smart urban rail on the journey

In 2026, one of the most significant shifts in smart mobility will be the continued evolution of rail networks into proactive, "thinking" systems. AI will increasingly serve as the bridge between deep engineering expertise and real-time data, turning granular insights into metro-wide operational improvements.  

A major area of focus will be AI-powered predictive maintenance. Traditionally, rail maintenance has been a choice between costly over-servicing or waiting for breakdowns to occur. However, the industry is entering a new era where enterprise asset management systems (EAMS), powered by machine learning, continuously monitor asset health across entire metro networks. By analysing data from sensors and historical performance, these systems will be able to predict faults in critical components – such as rolling stock and signalling – before they occur. This shift to condition-based and usage-based maintenance not only reduces downtime and costs but fundamentally enhances commuter safety. 

Rail systems are crucial parts of the broader urban mobility ecosystem, which is why our Integrated Transport Operations Centre (ITOC) is set to play a key role in transforming rail and urban traffic management by integrating all electrical and mechanical subsystems into a single, resilient software environment. With AI enabling the real-time coordination of rail, road, and other urban mobility systems, this integration will allow cities to: 

• Predict and mitigate congestion by dynamically adjusting service frequencies to meet real-time demand. 
• Coordinate emergency responses and facilitate swift service recovery across different transport modes through sense-making and situational awareness 
• Enhance city-wide resilience by serving as a digital bridge between rail operators and government agencies to ensure coordinated incident mitigation.

The integration of AI and real-time analytics will also allow operators to conduct stress tests and simulate environmental impacts, giving them the foresight to make faster, more informed decisions. This will enhance the reliability of rail systems, advancing the broader vision of smarter, more connected cities. 

The next phase: agentic and generative AI 

Looking ahead, projects such as Singapore’s latest iTransport system and our AGIL® Smart City Operating System deployment in Qatar’s Lusail City offer a glimpse of the next generation of AI-led innovation.

In Singapore, the new-generation iTransport intelligent transport system builds on the success of its predecessor by operating as a comprehensive, all-in-one back-end platform that aggregates real-time data from a wide range of intelligent transport systems. By processing and interpreting large volumes of information – including live video feeds and other traffic data sources – the system can automatically analyse current conditions, enabling faster incident detection and the rapid generation of response workflows for traffic management and rerouting. Our recent enhancements have further strengthened these capabilities, enabling faster incident detection, more proactive traffic monitoring, and significantly reduced response times, while minimising the need for manual intervention during periods of network pressure.

Similar advances are shaping tolling operations. By leveraging advanced machine learning and convolutional neural networks, modern tolling systems like the one used for the Central Business District Tolling Program in New York can instantly process video analytics to identify, classify, and automatically charge vehicles entering congested zones. This high-speed, accurate AI-powered tolling not only supports the practical function of regulating traffic flow and reducing vehicle volumes but also strategically encourages the use of public transport and achieves reductions in vehicle emissions. 

Beyond transport, AI-driven security systems are helping cities move from simple surveillance to proactive detection and response, bolstering public safety. The same approach is being applied in energy and building management, where sensors and analytics are used to reduce energy consumption and lower carbon footprints.

Lusail City represents a new model of city management where AI is embedded at its core. This greenfield development integrates city infrastructure into a single platform built on a common data lake and powered by our AGIL® Smart City Operating System. Traditionally, city systems such as traffic management, security, utilities, and building management have tended to operate in silos. Lusail breaks that model by using agentic AI to drive workflows dynamically, rather than relying on static applications. The result is a city that can continuously learn, optimise operations, and generate cross-domain insights in real time.

The cumulative impact of these technologies can be felt in daily life. Public transport runs with greater precision, supported by apps that provide commuters with real-time updates, route suggestions, and accurate arrival times – all powered by the same digital platform that help the city operate smarter every day. 

AI as the engine of urban evolution

Looking ahead, AI’s influence on urban innovation is set to accelerate. Even at its current level of maturity, AI is enabling cities to do what was once impossible. 

Earlier generations of smart city systems could collect and visualise data but lacked the ability to interpret it or act on it intelligently. Today’s AI tools can analyse, predict, and optimise performance across multiple domains in real time. Where IoT and connectivity once defined the foundations of smart cities, AI now serves as their driving force, transforming raw data into dynamic, coordinated action.

While edge computing, 5G, and sensor networks will continue to advance, AI will define the future. It is turning cities from passive systems that observe to active systems that understand – and ultimately, to systems that act. In doing so, it is setting the course for the next phase of smart city evolution: one that is more connected, responsive, and human-centred than ever before. 

ST Engineering is a global technology, defence and engineering group with a diverse portfolio of businesses across the aerospace, smart city, defence and public security domains. Its Urban Solutions business is a leading provider of smart mobility and digital infrastructure solutions, empowering more connected, secure and efficient cities. Through advanced technologies and deep engineering expertise, it delivers integrated, future-ready innovations that address urban challenges and enhance quality of life.