AI for smart cities and future mobility: A quick guide

As urban populations grow, cities struggle with congestion, pollution, and inefficient infrastructure. To address these issues, Smart cities use advanced technologies, especially Artificial Intelligence (AI). By integrating AI into urban systems, cities can better manage resources, optimize transportation, and improve mobility for everyone.

AI-driven transport allows real-time traffic management, adaptive public transport, and predictive infrastructure maintenance. With the right AI tools, urban planners and policymakers can create resilient cities that prioritize accessibility, sustainability, and connectivity. This article explores the role of AI in smart cities, focusing on transportation and mobility.

What Are Smart Cities?

A smart city uses technology and data to improve urban life. Its key features include:

• Connectivity: Internet of Things (IoT) networks collect and share real-time data.
• Data-Driven Decision Making: AI analyzes data for better urban management.
• Sustainable Infrastructure: Green buildings, renewable energy, and smart transportation.
• Citizen Engagement: Digital platforms allow residents to participate in city planning.
• Smart Mobility Solutions: Public transport, bike-sharing, and autonomous vehicles reduce congestion.
• Enhanced Public Services: AI improves emergency response, waste management, and utilities.

By adopting these technologies, cities become more efficient, eco-friendly, and livable.

AI for smart cities: Transportation

In smart cities, AI helps to analyze data, optimize resources, and support real-time decisions. AI in transportation improves urban mobility by using data from sensors, cameras, GPS, and mobile networks to provide useful insights. These insights enhance traffic management, improve public transport, and create better mobility plans.

AI in transportation also includes real-time data collection, smart transport systems, and energy-efficient infrastructure.

AI-Powered Traffic Management in smart cities

The mobility sector produces large and complex datasets. In smart cities, AI processes this data to make real-time traffic decisions, predict trends, and improve operations.

For example, intelligent traffic signals adjust their timing based on congestion, reducing delays and improving traffic flow. Intelligent traffic signal control systems, tested in cities like Pittsburgh, use AI to analyze traffic and adjust signals dynamically. This reduces congestion and improves efficiency.

Pittsburgh’s AI-driven traffic lights adapt to real-time conditions, cutting travel times by 25% and emissions by 21%. AI also detects traffic violations, tracks pedestrians, and alerts authorities about accidents for faster response times.

AI in smart cities public transportation

AI for smart cities improves public transportation by making it more efficient and user-friendly. AI-based demand forecasting optimizes bus and train schedules by analyzing real-time passenger demand, past ridership data, and factors like weather and city events. This use of AI in public transport reduces wait times and improves reliability.

Dynamic routing algorithms help buses and shared mobility services adjust routes based on live traffic data, enhancing service efficiency. AI-driven predictive analytics in mobility also support long-term route planning and infrastructure investment, ensuring better resource use and improved transit services.

For example, Singapore’s AI-based transport system analyzes passenger demand and adjusts bus schedules dynamically, reducing wait times and overcrowding.

AI for sustainable mobility in smart cities

AI and sustainability in transport help smart cities reduce carbon emissions and support eco-friendly AI transport. AI-powered tools optimize energy use in electric vehicles (EVs), improve traffic flow to cut fuel waste, and make shared mobility services more efficient.

For example, AI-driven ride-sharing algorithms match passengers traveling in the same direction, reducing vehicles on the road and lowering emissions. AI also improves multimodal transport by integrating bicycles, electric scooters, and public transit, creating greener mobility options in smart cities.

AI-based urban mobility dashboards, help cities track accessibility and sustainability, guiding data-driven policies for green transportation. In cities like Amsterdam, AI-powered ride-sharing services reduce traffic and emissions by matching passengers traveling similar routes.

AI in Transportation Modeling and Planning

In smart cities, AI in transport modeling helps future-proof mobility and infrastructure by validating plans and changes. AI-based transportation planning speeds up model creation and calibration, leading to more accurate predictions and better planning.

For example, some software tools use machine learning to generate transport models for cities in just a week, making urban planning more efficient. By applying AI-driven data analysis, these models improve decision-making, optimize transport networks, and enhance mobility in smart cities.

AI-powered software also plays a key role in smart cities’ mobility strategies. Advanced simulation and modeling tools help city planners design efficient transport networks. For example, Dubai integrates AI into its smart mobility plan, including autonomous taxis and AI-driven traffic management systems to reduce congestion.

Examples of software using AI for smart cities:

• Dynamic Multimodal Network Management: Developed by PTV Group, this system combines planning, management, and decision support. By using AI and diverse data sources, cities gain real-time awareness, improve congestion management, and prepare for autonomous vehicles.
  
  
• PTV Optima: This traffic management software applies machine learning and dynamic modeling to deliver real-time traffic forecasts. By analyzing sensor and floating car data, it predicts traffic conditions up to 60 minutes ahead, allowing proactive congestion management.
  
  
• PTV Flows: A cloud-based AI traffic management tool that provides real-time monitoring and short-term traffic predictions. Its self-learning algorithms help urban mobility operators anticipate and reduce congestion without major infrastructure changes.

Challenges and risks

As smart cities increasingly integrate AI in transportation networks, several challenges and risks must be addressed to ensure safety and efficiency. One critical concern is the reliability of AI-driven vehicles, particularly in unpredictable conditions such as heavy traffic, adverse weather, or interactions with human drivers and pedestrians.

Cybersecurity threats also pose significant risks, as AI-powered systems could become targets for hacking, potentially leading to system failures or malicious disruptions. Additionally, ethical and legal uncertainties, such as liability in accidents involving autonomous vehicles, remain unresolved.

Effective regulation, robust infrastructure, and human oversight are essential to mitigate these risks and ensure AI-driven transportation benefits smart cities.

The future of AI in smart cities

The future of smart city mobility depends on integrating AI with IoT, 5G connectivity, and edge computing. These technologies will improve real-time data processing, making mobility solutions more intelligent and responsive.

Cities are also adopting Digital Twins – AI-driven virtual models that simulate traffic scenarios and predict the effects of new mobility policies. These models allow urban planners to test strategies before applying them, ensuring better transport planning.

For example, London already uses AI-powered Digital Twins to simulate traffic and assess new policies before implementation.

Conclusion

AI for smart cities is transforming transportation, making urban mobility more efficient, sustainable, and responsive. From AI in traffic management and public transport optimization to sustainable mobility and predictive modeling, AI transportation solutions are changing how cities manage transportation.

As AI technologies advance, collaboration between urban planners, policymakers, and technology providers will be essential for creating truly smart cities that focus on mobility, sustainability, and quality of life.