The future of mobility

The world is undergoing rapid and unprecedented urbanisation, with the United Nations estimating that by 2050, nearly 68% of the global population will live in urban areas.

The majority of this growth will take place in emerging cities, where existing transportation systems are increasingly unsustainable. Traffic congestion, air pollution, extreme weather events, and rising traffic-related fatalities and injuries pose severe risks to public health, economic productivity, and environmental sustainability. These inefficiencies also result in significant financial losses. For instance, traffic congestion alone costs the global economy billions of dollars annually in lost productivity and fuel consumption.

At the same time, disruptive technological advancements are presenting opportunities to rethink transportation systems entirely. Innovations in artificial intelligence (AI), big data, blockchain, and machine learning are paving the way for a transportation future that is connected, automated, shared, demand-responsive, and safe. The key to this transformation lies in leveraging new methods for data collection, storage, sharing, and analysis. Traditional approaches to transportation planning, engineering, and operations are being replaced by data-driven strategies that offer greater efficiency and adaptability. But how exactly is this shift taking place? The answer lies in the evolution of transportation data itself.

The Rise of Advanced Mobility Data Systems

Transportation data has evolved significantly, moving beyond conventional survey-based methods to real-time, dynamic data streams. Advanced data collection methods are transforming how we understand and manage mobility. For example:

• Radio-frequency identification (RFID): Captures path choices and travel times of individuals, providing granular insights into travel behavior.
• Mobile phone and GPS data: Allows real-time tracking of movement patterns, offering a more accurate representation of urban mobility trends.
• Media access control (MAC) address tracking: Enhances understanding of pedestrian and vehicular traffic flows.
• Connected vehicle-infrastructure-pedestrian (VIP) environments: Enable vehicles, infrastructure, and pedestrians to communicate in real-time, creating an interconnected ecosystem for safer and more efficient mobility.

These technological advancements have made it easier and more cost-effective to collect, store, analyze, and share multi-source data. Traditionally, transportation systems relied on predefined models such as traffic equilibrium and flow models to predict and manage mobility. However, the rise of big data, coupled with increased computational power, has enabled the adoption of micro-simulation and agent-based models. These advanced models allow for more detailed and real-time assessments of mobility systems, ultimately improving the accuracy and responsiveness of transportation planning.

The Shift Towards Shared and Demand-Responsive Mobility

A crucial aspect of the future of mobility is the transition towards shared and demand-responsive transportation systems. Instead of relying on individually owned vehicles, emerging mobility solutions emphasize shared services that leverage technology to optimize efficiency and sustainability.

• Autonomous vehicles (AVs): Expected to play a key role, shared AV fleets can reduce congestion by maximizing vehicle utilization and minimizing the number of cars on the road. This shift will likely lead to significant changes in urban infrastructure, such as reduced street parking and fewer private vehicle lanes, freeing up space for parks, pedestrian walkways, and cycling infrastructure.
• Demand-responsive mobility services: App-based ride-sharing, microtransit, and on-demand public transport optimize route planning and vehicle dispatching. These systems use real-time data analytics to reduce energy consumption, emissions, and costs while ensuring adaptability to evolving urban challenges.

Safety and the Role of Big Data

Ensuring safety remains a fundamental priority in the future of mobility. Big data is playing a transformative role in enhancing road safety, particularly for vulnerable populations such as pedestrians, cyclists, the elderly, and persons with disabilities.

• AI-powered systems: Predict and prevent accidents by analyzing real-time traffic data and enabling connected vehicles to communicate with one another and with infrastructure.
• Traffic management systems: Identify high-risk zones and implement measures such as adaptive signal control, dynamic speed limits, and improved pedestrian crossings.

Despite these advancements, many urban areas, particularly in emerging cities, lack the data infrastructure to fully integrate these safety-enhancing technologies. Ethical considerations around data privacy and governance also need to be addressed to ensure responsible and equitable use of mobility data.

Conclusion

The future of mobility is being shaped by the convergence of AI, machine learning, big data, and blockchain technologies. These advancements are driving a shift towards transportation systems that are connected, automated, shared, demand-responsive, and safe. However, to ensure that these benefits are realized globally, particularly in low-resource settings, concerted efforts are needed to develop the necessary infrastructure, policies, and data governance frameworks.

While the trajectory towards a smarter and more efficient mobility future is promising, it is crucial to address challenges related to accessibility, digital equity, and regulatory adaptation. By doing so, cities can create transportation ecosystems that are not only technologically advanced but also inclusive, sustainable, and resilient.

Suggested links & publications

• Lisbon Study: Connected and Shared Autonomous Vehicles
• Blockchain: Unlock Transport with Blockchain
• Cities of the Future
• The Shared-Use City: Managing the Curb
• Blaise Transit
• IFC: AI for Safety and Efficiency
• Smarter and More Connected: Future Intelligent Transportation Systems
• Big Data and Understanding Change in the Context of Planning Transport Systems
• Mobility Data Decentralization: Leveraging Galileo and Blockchain (EIT Urban Mobility)
• TUMI Publications: Data as a Force to Shape Future Urban Mobility
• The Shared-Use City: Managing the Curb (2018)
• Preparing Infrastructure for Automated Vehicles (2023)
• Lost in Transmission: Communicating for Safe Automated Vehicle Interactions in Cities (2024)