Artificial Intelligence And Urban Mobility

AI can also be applied to enhance the situational awareness of authorities and transport operators and provide them with decision support tools. In Singapore, for example, the Land Transport Authority has developed a fusion analytics for public transport (FASTER) system that can ingest and analyse data from various sources such as CCTV, wifi/ telco, ticketing and traffic data to help us visualise commuting patterns to improve transport planning, trigger early alerts of any incidents and model the impact of transport delays.

Perhaps the most widely known use case for AI in mobility is that of autonomous vehicles (AVs), not least due to the help of pop culture. Remember the autonomous cars in I, Robot or Minority Report? Today, while AVs are still some years away from widespread deployment, many cities are testing them on public roads, from passenger cars to trucks to even electric scooters. Here in Singapore, AVs have been trialled on our public roads since 2015 by companies such as Aptiv and ST Engineering and local universities like the National University of Singapore and Nanyang Technological University. We are now working towards town-scale pilot deployments of autonomous buses and shuttles in three of our towns by the early 2020s.

When mature, AV technology can positively transform the mobility landscape, opening up new and more convenient mobility options for commuters, allowing mobility services to be delivered more efficiently, and reducing the number of traffic accidents due to human error. We may soon even see autonomous mobility taking to the skies, with companies like Volocopter and Airbus conducting trials of autonomous aerial vehicles.

However, even as AI brings about transformational change to mobility, we have to be mindful that it can potentially pose some challenges. Much has been made, for example, of the disruption that AI would bring for some jobs. But AI will also lead to the creation of new and potentially better jobs. What is important is that workers that are displaced have the opportunities to be reskilled to take on new jobs that become available. Yet another example is how existing regulatory and liability frameworks may struggle to deal with the implications of AI. Here, governments will have to ensure that these frameworks are regularly updated to safeguard the public interest, while still encouraging innovation.