7 Oct 2022Blog

ITS World Congress 2022: lessons learned

ITS World Congress 2022: lesson learned

The ITS World Congress provided an opportunity to have a hunch on how mobility may transform and what kind of impacts it could bring in terms of greener communities, more resilient systems, better accessibility and equity – not to mention improved safety.

28th ITS World Congress was held on September 18-22 at the Los Angeles Convention Center. Annually arranged Intelligent Transportation Systems (ITS) event brings together industry stakeholders, including policymakers, authorities, OEM’s, entrepreneurs, researchers, academics, investors, and other leaders from around the world to connect, network and participate in live education sessions in which industry experts present the latest developments in ITS along with a comprehensive expo floor showcasing cutting-edge technology.

This year there were over 16 000 visitors and participants from 39 different countries, ca. 300 exhibitors, and +180 parallel sessions where the latest findings and studies were presented and discussed. In addition, 20 different technology tours, visits and demos were available for the registered industry participants. Tours, demos and visits covered e.g., traffic management, ports, automated driving, surveillance, vehicle remote controlling, connectivity and situational awareness.

Intelligent technologies and applications provides an opportunity to redefine transport systems and reimagine the future: sustainable mobility, fluent and resilient systems, improve accessibility and equity, and safer network for all of us.

To foster the achievement of the goals, the theme for this year’s ITS World Congress was “Transformation by Transportation,” which included the following topics:

  • Digital Infrastructure
  • Equitable and Seamless Mobility
  • Intelligent, Connected, and Automated Vehicles
  • Organizing for Successful Policy and Governance, Business Models, and International Cooperation
  • Path to Vision Zero
  • Sustainability and Resiliency
  • Technology from Entry to Last Mile

Main findings from technology and digital transformation company point of view

In general, congress did not offer anything mind-blowing or something that we were not aware of. But it made clear that our expectations have been right. However, the expected technologies and applications are probably coming faster than we thought. The main findings and key takeaways can be summarized in five themes:

  • Ecosystem approach and data business: Many times, it is not worth building new lanes and roads but trying to make better use of the existing infrastructure by the means of digitalization. Data and data sharing will be one of its main drivers and ecosystems is a way to enable it. However, only a few are ready to give up the data for free and that is why rules and business models of data-driven business still need research and development. Opening the data for ecosystems is mandatory for example for achieving real-time digital twins of the transport system.
  • Computer vision and edge computing: creating situational awareness and digital will more and more be based on computer and machine vision technologies such as cameras, Lidars and radars. The amount of data they generate is so large that there is no point in sending it to the cloud. In addition, the enable situational awareness, the data must be analyzed as close to the source as possible. That is why edge computing and edge hardware were presented very comprehensively in the exhibition and the parallel sessions.
  • Situational awareness and digital twins: real-time digital twin of the transport system will be the core of future traffic management as it is going to rely on real-time data and situational awareness. As the digital twins can be relatively big and heavy models, they are not going to be run on the edge but in the cloud and on-prem servers. Whatever the solutions are, it highlights the connection between edge and cloud/on-prem as all the information may not be essential to analyze on edge. That is why it may be essential to build distributed intelligence -concepts that enable dynamic distribution of analytics and information sharing case by case. We have to admit that there is a long way to go in creating digital twins of transport systems. The technology is there but transport ecosystem data must be opened to have complex real systems modeled. The challenge is that organizations still also lack vision, data capabilities, competencies and awareness of their data assets. Even standards are partly missing.
  • Low-latency communication: Obviously when dealing and playing with situational awareness, computer vision and digital twins, information must be communicated adequately and reliably. Hence the advanced communication technologies, 5G specifically were well presented and marketed at the expo. It is worth noting that 4G is sufficient in most cases, especially if data is chewed on the edge or close to its source. However, autonomous driving and urban environments are use cases where solution providers will rely on 5G.
  • Predictive traffic management and modeling: Predictive traffic management aims at identifying the emergence of traffic phenomena and events in advance and mitigating their impacts. Such competence calls for sophisticated and advanced AI/ML algorithms. By understanding the situation and potential causes, the utilization and capacity of the transport network can be managed more efficiently and proactively by applying simulations and modeling techniques.

By summarizing the main findings above, technologically the way forward appears to be “distributed edge and cloud computing for real-time big data analytics enabling situational awareness and predictiveness”.

This requires investments in edge technologies (not hardware as such) and capabilities to take advantage of the synergistic benefits of edge and cloud across the industry. At the same time, there is a call for AI and ML expertise in order to identify complex phenomena and their root causes in transportation. Also simulation and modeling capabilities will be needed in the future to ensure predictiveness and optimization.

From a business and ecosystem consultancy point of view, there are huge gaps regarding how ecosystems work in business and policy manner, what kind of practices should exist in terms of data models, governance and management, and what kind of business models for data platforms may be.