AI Connecting the Dots for Future Urban Mobility

Last week, I had a most exciting round table video podcast with Sampo Hietanen, founder and CEO of Aspectu, and Ben Foulser, a partner at KPMG UK. 

Our topic, “Smart Cities and AI: The Future of Urban Mobility,” offered valuable insights into AI’s growing role in urban mobility trends, specifically in Mobility as a Service (MaaS), autonomous mobility, and public transport. We discussed how these innovations could redefine city life through more sustainable, efficient, and personalized transport options. Both Sampo and Ben shared their expertise and revealed a dynamic landscape of possibilities and challenges.

The discussion centered around how AI influences trends like Mobility as a Service (MaaS), autonomous mobility, and public transport, painting a picture of cities where sustainable, efficient, and highly personalised transport options could become the norm. The guests shared insights on how AI’s potential for integration, prediction, and automation can reshape mobility networks, while also revealing the technological and social challenges that cities must navigate.

Introduction: AI as the Enabler in Urban Mobility

At the heart of the conversation was the question, What is AI’s purpose in mobility? AI was described as a gradual technological evolution, not an overnight revolution. The growing computing power and machine learning capacities of AI systems have allowed for unprecedented predictive and responsive capabilities within the transport sector. Rather than a standalone solution, AI is a critical enabler, creating visibility into supply and demand and improving transportation network efficiency. With AI, the vision of highly responsive, personalized transport is closer than ever.

Mobility as a Service: Toward a Seamless User-Centric Experience

The podcast then examined MaaS, a concept that seeks to combine various modes of transport into a cohesive, user-friendly experience. AI offers transformative potential for MaaS, especially in creating individualised transport options. By learning from user behaviour, AI could deliver tailored services, whether that means suggesting a bus, an e-scooter, or a ride-sharing service based on an individual’s preferences and current needs. However, the guests cautioned that technology alone will not suffice; rather, the focus must remain on defining the services that truly benefit users. Integrating diverse transport providers into a seamless ecosystem is both technically and commercially challenging, as operators often hesitate to cede control of customer relationships and revenue.

In this respect, AI could facilitate easier integration by automatically aligning different data standards across providers. For example, AI’s ability to translate data between systems could streamline the costly and complex process of combining services but achieving a widely adopted MaaS system will still require substantial industry cooperation and strategic alignment.

Autonomous Shuttles: Realizing the Vision of Shared Mobility

Autonomous vehicles (AVs) are often seen as a natural extension of AI in urban mobility, potentially eliminating the need for private car ownership. Autonomous shuttles in particular were highlighted as an ideal solution for urban centers, providing flexible and accessible transportation that could match or surpass the convenience of a car. However, if not carefully managed, these shuttles risk significantly increasing traffic by making individual transit too easy. The challenge, therefore, lies in ensuring AVs serve a greater transit network, providing convenient links to main transit hubs rather than encouraging single-route, A-to-B trips for everyone.

The value of autonomous shuttles also lies in their potential to connect neighbourhoods to larger transit corridors, such as metro stations or bus depots, acting as “feeder” services to core networks. This integration would allow AVs to optimise urban mobility while minimising congestion, and AI can help streamline these networks to maximise efficiency.

Public Transport and Micro-Mobility: Blending Modes for Optimal Outcomes

The conversation highlighted that traditional public transport will remain essential, yet the line between public and private transport services is becoming increasingly blurred. Micro-mobility solutions, such as e-scooters, shared bikes, and even ride-sharing, could play a larger role in future transport networks. Expanding the definition of public transport to include these modes would promote the growing trend of cities to offer a broader range of mobility options, ideally reducing private car use.

Public transport is crucial not only for managing congestion but also for ensuring affordability, energy efficiency, and accessibility. While the benefits of a balanced transport mix are evident, this balance must be achieved thoughtfully, considering each city’s unique demographic, economic, and geographic factors. This balance would enable urban centers to optimise for different objectives, including air quality, energy efficiency, affordability, and connectivity.

Data Protection: Navigating Privacy in AI-driven Mobility

AI-driven solutions rely heavily on data, and managing this data responsibly is key to creating trust in these systems. The podcast touched upon the need for robust data protection measures that balance data collection with privacy concerns. While AI can learn from extensive user data to optimise services, concerns over individual privacy are valid. There’s a need for clear regulations that address data ownership and usage in real-time transit networks, giving people control over their data while maintaining the privacy and security of city-wide mobility systems. Lessons can be drawn from how telecommunications companies handle sensitive user data to establish data protection frameworks in AI-driven mobility.

Conclusion: Crafting the Vision for AI-driven Urban Mobility

The conversation wrapped up with a consensus that AI has vast potential to transform urban mobility into a smarter, more efficient, and more accessible system. However, the guests emphasised three key requirements for this vision to materialise:

1. Market Creation and Regulatory Support: Establishing a conducive market environment is essential. Proactive regulation and policies that promote investment and collaboration among diverse mobility players can help advance the industry.

2. Incremental Implementation of Solutions: Starting with low-hanging fruit, such as customer service automation, can set the stage for more complex AI applications, allowing the industry to learn and adapt incrementally.

3. A Clear, Unified Vision for Urban Mobility: Cities need to define a clear, unified vision for urban mobility, one that aligns policies, infrastructure, and services with societal goals. By doing so, cities can create a balanced ecosystem where individual freedom and sustainable outcomes coexist.

The podcast concluded with optimism: AI holds significant promise in transforming urban mobility. By focusing on user-centered solutions, strategic collaborations, and regulatory guidance, cities can harness AI’s potential to create sustainable, efficient, and accessible mobility solutions that meet the needs of urban dwellers in the future.