The convergence of artificial intelligence (AI) and big data is fundamentally reshaping urban transport operations and services. As cities grapple with congestion, emissions, and aging infrastructure, transport agencies are looking to AI to deliver more efficient, reliable, and sustainable mobility solutions. However, as Katherine Flesh, a key figure at Microsoft, has pointed out, the greatest opportunities from AI will depend on strong data foundations, workforce readiness, and responsible governance.
The Data Foundation: Fueling AI in Transport
At the heart of any successful AI deployment lies high-quality data. For transport systems, this means collecting and integrating data from millions of sensors, GPS devices, fare card transactions, traffic cameras, and connected vehicles. Without clean, standardized, and real-time data, AI models cannot produce accurate predictions or optimizations. Cities like Sunderland and Dublin have recognized this and are investing heavily in digital infrastructure to capture and manage urban data streams.
For example, Sunderland's smart city strategy emphasizes leveraging digital infrastructure to build a resilient, low-carbon economy. The city is using IoT networks to collect data on traffic flows, air quality, and energy usage, which in turn feeds AI algorithms that optimize traffic light timings, reduce congestion, and improve public transport scheduling. Similarly, Dublin is deploying digital twin projects that create virtual replicas of the city’s transport network, allowing planners to simulate the impact of changes before implementing them in the real world.
Workforce Readiness: Preparing People for AI-Driven Change
Technology alone is not enough. Transport agencies must invest in upskilling their workforce to work alongside AI systems. This includes training data scientists, AI ethicists, and operations staff who can interpret AI outputs and act on them. Many cities are partnering with universities and private sector firms to create training programs. For instance, Singapore Management University President Professor Lily Kong has emphasized the need for cities to move beyond mere resilience toward regenerative and restorative urban systems, which requires a workforce skilled in systems thinking and advanced analytics.
In practice, workforce readiness also involves building trust in AI-driven decisions. When an AI system recommends adjusting bus routes or closing a lane, human operators must understand the rationale and be able to override it if necessary. This calls for transparent AI models and explainable decision-making processes.
Responsible Governance: Ethical AI in Transport
As transport agencies adopt AI, they must navigate ethical and regulatory challenges. Issues such as bias in traffic enforcement algorithms, privacy concerns from surveillance cameras, and the accountability of autonomous vehicle decisions require robust governance frameworks. Cities like Dublin are exploring how to balance innovation with citizen trust. They are developing ethics boards that oversee AI projects, ensuring that data collection is transparent and that models do not inadvertently discriminate against certain neighborhoods or demographic groups.
Moreover, cybersecurity risks are becoming more prominent. The second episode of the Cities Thriving on Lighting series highlighted how smart lighting networks, often integrated with transport infrastructure, must be secured against cyberattacks. Responsible governance includes not only data protection but also physical security of digital infrastructure.
Digital Twins and Predictive Analytics: The Future of Transport Operations
One of the most promising applications of AI in transport is the use of digital twins. A digital twin is a real-time virtual model of a physical asset, such as a traffic intersection, a subway line, or an entire city’s transit network. By feeding live data into the twin, operators can monitor performance, predict failures, and simulate scenarios without disrupting actual services. For example, a digital twin of a metro system can forecast which components are likely to break down, enabling predictive maintenance that reduces downtime and costs.
During the SmartCitiesWorld Summit 2026, a panel discussion titled "Operating smarter: using digital twins and AI to reshape urban infrastructure management" explored how cities are using these tools to shift from reactive to proactive management. The panel emphasized that digital twins are not just for large metros; mid-sized cities like Sunderland are also deploying them for traffic management and emergency response planning.
Case Studies: From Sunderland to Dublin
Sunderland’s repositioning as a smart city provides a compelling example. The city has deployed a city-wide digital infrastructure that connects traffic lights, parking sensors, and electric vehicle charging stations into a unified data platform. AI algorithms analyze this data to reduce congestion by up to 20% and cut emissions. The city’s low-carbon innovation strategy also integrates renewable energy sources, making its transport network more resilient.
Dublin, meanwhile, is innovating in multiple areas: from digital twin projects that model pedestrian flows to traffic reduction schemes that use AI to adjust speed limits dynamically. The city is also focusing on economic growth by attracting tech companies that specialize in mobility solutions. These efforts are coordinated through the SmartCitiesWorld City Profile series, which documents the city’s progress.
Energy Integration: AI and Renewables in Transport
Transport and energy systems are increasingly intertwined as cities electrify their bus and taxi fleets. Local authorities can shape energy systems through renewables, flexibility, storage, and smarter networks. AI plays a crucial role in managing these systems, forecasting demand for electric vehicle charging, and optimizing the use of renewable energy. For instance, a fleet of electric buses can be scheduled to charge during periods when solar or wind power is abundant, reducing costs and carbon footprint.
At Ecomondo, a major environmental forum, leaders discussed how healthier, more sustainable cities depend on integrating transport and energy planning. The SmartCitiesWorld Summit was highlighted as a platform for sharing practical solutions, such as using AI to coordinate building energy use with transit schedules.
How AI and Data Are Transforming Transport Services
Beyond operations, AI is improving the passenger experience. Real-time arrival predictions, personalized route recommendations, and dynamic pricing for public transport are becoming common. Data from ride-hailing apps and bike-sharing services can be integrated with public transit data to offer seamless multimodal journeys. Cities are also using AI to improve safety: computer vision systems detect near-misses at intersections, and predictive models identify high-accident areas for targeted interventions.
OnDemand Trend Report webinars and panels, such as those featured in the SmartCitiesWorld Summit, emphasize that the transformation is ongoing. The greatest gains come when cities adopt a strategic, risk-based approach to infrastructure resilience, using data to prioritize investments and measure outcomes.
In the end, the future of urban transport will be defined by how well cities connect people, data, infrastructure, and investment into coherent, place-based strategies. AI and data are the tools, but the vision must be human-centric, focusing on equity, sustainability, and community well-being. As Professor Lily Kong suggests, cities must strive not only to be resilient but regenerative—actively restoring ecosystems and improving quality of life. This is the promise of AI-driven transport transformation.
Source:Smart Cities World News