Appendix D

Briefing Paper on Exploratory Topic 3: Leveraging Digitalization, Artificial Intelligence, and Other Integrated System-of-Systems Technologies to Decarbonize Transport

Margriet van Schijndel-de Nooij, Eindhoven University of Technology, The Netherlands

Heng Wei, University of Cincinnati, United States

The U.S. National Blueprint for Transportation Decarbonization establishes a visionary goal of eliminating nearly all greenhouse gas (GHG) emissions from the transportation sector by 2050. With the rapidly evolving development of Internet-of-Things (IoT)-based mobility information and connected and automated vehicle (CAV) technologies, we are in a transformative era for transportation.¹ As pointed out in the U.S. Department of Transportation (U.S. DOT) Research, Development, and Technology Strategic Plan for Fiscal Years 2022–2026 (U.S. DOT RDT Strategic Plan), “we have entered a transformative era for transportation. This transformation is blurring boundaries between traditional transportation domains, enabling a vast array of technological innovations and fostering public awareness of the highly interconnected, multimodal, complex nature of modern transportation.”

The European Commission has set out its vision and ambition in the 2020 document Sustainable and Smart Mobility Strategy—putting European transport on track for the future. The vision is accompanied by an action plan, based on 10 flagships, to establish a fundamental transport transformation. As a result, the ambition is to have a 90% cut in emissions by 2050, delivered by a smart, competitive, safe, accessible, and affordable transport system.

Despite extensive support through a myriad of policy, planning, and technological solutions, the transition process has proven persistently unsustainable, with GHG emissions on a continual rise. The absence of a paradigm shift in communities and culture remains a critical issue.² Essentially, behavioral patterns have not undergone sufficient changes to provide decision makers with the necessary support for implementing more aggressive measures to address the pressing needs.

When responding to the critical challenges of aging roads, bridges needing significant repair, and limited intermodal connectivity, improving infrastructure adaptability, resilience, and sustainability has also been a local focus of many American and European cities. However, historical development patterns and geographical factors have left a legacy of dated roadway design, with low multimodal connectivity networks and facilities in those areas.

Mode shift has been viewed as a big contribution to environmental sustainability and mitigating the impacts of climate change. Mode shift commonly denotes a transition or alteration in transportation modes, a concept frequently employed in urban and transportation planning to promote and assess individuals’ choices of low-carbon and efficient transportation alternatives. The mode shift has been extended to include electric vehicles, micromobility modes (e.g., e-bikes and e-scooters), and Mobility-as-a-Service (MaaS) mode.

The U.S. DOT RDT Strategic Plan points out that leveraging digitalization, artificial intelligence (AI), and other integrated system-of-systems (iSOS) technologies has been recognized as a way to achieve the decarbonize goal. This is twinned by the European policy on the Green Deal from March 2022 titled “Towards a Green, Digital and Resilient Economy: Our European Growth Model,”³ and more detail for several sectors, including mobility, in June 2022, titled “Towards a Green & Digital Future—Key Requirements for Successful Twin Transitions in the European Union.”⁴

Leveraging data-driven insights is essential for creating effective policies, encouraging sustainable practices, and driving innovations to achieve meaningful transportation decarbonization and address climate change. Digitalization is an invaluable tool for understanding, monitoring, and mitigating the environmental impact of transportation. Siemens’s research on Infrastructure Transition Monitor (2023) advocated that reshaping the next generation of infrastructure “will be enabled by the world’s best technologies, data-driven strategies, and hundreds of big ideas.”⁵

Released in October 2023, President Biden’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence emphasizes the critical importance of responsibly overseeing AI development and implementation. This directive has catalyzed a unified, government-wide endeavor to accomplish this objective efficiently. In the transportation sector, it is imperative to adopt a comprehensive approach to harnessing the evolving capabilities of AI for the betterment of our security, economy, and society. At the same time, there is a growing recognition that maximizing the potential benefits of AI while mitigating its inherent risks is crucial. This necessitates preventing irresponsible use that could exacerbate harms within the transportation domain.

When leveraging AI and digitalization, and iSOS technologies to decarbonize transportation, a set of underlying questions are raised to facilitate further directing and promoting transportation decarbonation solutions, as described in the following sections.

RELEVANT POLICIES AND PROGRAM

This section focusses on the policies and joint programming relevant to support potential leveraging effects of digital tools and AI technologies to decarbonize transport. It includes facilitating conditions for deployment and related research needs, as well as user acceptance. Key questions include:

• How can government programs, initiatives, and policies support the potential leveraging effects? How can they stimulate actual large-scale effects?
• What do governments need to develop and implement evidence-based policies for implementation and deployment of such systems’ technologies?
• Which collaboration across local, regional, national, and international governments would be beneficial for harvesting the decarbonization potential of these technologies?
• How can government programs, initiatives, and policies help to safeguard against risks or misuse of these technologies, when widely implemented in our mobility systems?

• What differences and similarities between the European Union and the United States can be found in their (central) approaches? What can be learned from this?
• What policy barriers are expected in transferring best practices from one region to another?

CURRENT STRATEGIES, TECHNOLOGY, AND RELEVANT CASE STUDIES

Several current strategies are having an impact on the research and development and implementation of new solutions. Examples include the U.S. DOT RDT Strategic Plan, the EU policy regarding the Green Deal,⁶ the AI Act,⁷ and the Data Act.⁸ The development rate of digital tools is much higher than the development rate of new regulations and strategies. U.S. DOT maintains several data resources that can be of use to the AI research community. They include Data.gov—Transportation, ITS DataHub⁹ and Data for Automated Vehicle Integration.¹⁰ Additionally, the Fixing America’s Surface Transportation Act¹¹ required U.S. DOT to designate national alternative fueling corridors,¹² including Freight Electric Vehicle Corridors in alignment with the U.S. National Zero-Emission Freight Corridor Strategy.¹³ Key questions related to these policies and programs are:

• How do policies boost, steer, or slow the development and implementation of new solutions?
• Which strategies are being used? What are the benefits or downsides?
• How are the Chips Act, the AI Act, and U.S. counterparts facilitating actual leveraging effects for decarbonization? What is missing or could be helpful to speed the developments?
• How well known are the leveraging effects of digitalization, AI, and other iSOS technologies for decarbonization? To what extent are the potential benefits quantifiable? What could be done to improve this?
• How can stakeholders be incentivized to adopt and participate in the use of AI-enabled digital products to enhance their effectiveness and uptake in the transport sector? How can governments play an active role in this shift?
• How can stakeholders from different sectors collaborate to address regulatory and institutional barriers to the optimization of digital interfaces between modes of transport?
• How can program resilience and climate adaptations with AI technologies be programed into “system of systems” scenario design (e.g., connected cyber–physical systems) in planning and engineering measures?
• What life-cycle analysis, social technology-based climate change adaptation and environmental impacts are considered in projects?
• What skills do governmental agencies need to reinforce to play a leading role in these discussions and developments?
• What is the role of research and evidence in current policy-making processes? Are changes needed in that role, and if so, which ones?
• What strategies are in place to create coherency among transport modes? What system framework or architecture reference is needed to holistically measure the contributions of mode shifts to climate change mitigation?
• How can we enhance AI-enabled, data-driven prediction of human behavior in heavy weather and in disasters? What data sources are considered for reducing inherent bias and inequity concerns?

The utilization scenarios can be associated with the advancement of the Software Defined Vehicle, or the establishment of a platform for MaaS, alongside AI-enabled data analytics, and digital twins to comprehensively evaluate the impacts of climate change on transportation, whether directly or indirectly, by integrating land use, community and transportation planning, and system management and operations. Life-cycle and social technology-based analysis should be considered in the scenario assessment.

Conversely, tools are crucial for facilitating the utilization of AI technologies to fortify adaptive and resilient policies for transportation and land use in the face of climate change events. They also play a pivotal role in enhancing the identification and monitoring of transportation vulnerability and resilience among socially vulnerable populations, among other functions.

SOCIAL, ECONOMIC, AND ENVIRONMENTAL CONSIDERATIONS

For the EU and U.S. partners to fully leverage the potential benefits of digitalization, AI, and iSOS technologies to decarbonize transport, it will be essential to have a clear understanding of the social, economic, and environmental aspects related to this, and how to address these. An understanding of emerging trends and behavior are to be established. Key questions include:

• Which societal trends can significantly impact the pace and success of these technologies for decarbonization (e.g., aging population, territorial conflicts, pandemics, lack of skills)?
• How can we create user embracement of such technologies to harvest the decarbonization benefits in daily use or at scale?
• How can trust be established in such technologies, and who should act in creating this?
• How can we ensure inclusivity and affordability with the new tools and technologies?
• What other novel aspects, in practice or research, are to be considered?

NEW AND EMERGING TECHNOLOGIES OR OPPORTUNITIES

Primarily, the Fourth Industrial Revolution is distinguished by the fusion of technologies that dissolve the barriers between physical, digital, and biological domains, collectively referred to as cyber–physical systems (CPS). The emergence of CPS in transportation (CPS-T) promises to revolutionize how individuals interact with engineered systems, leveraging AI, digitalization, and iSOS technologies.

The potential of CPS-T lies in its ability to reshape transportation dynamics through advancements such as CAV-enabled cooperative driving automation, cooperative intelligent transportation systems (C-ITS), and decentralized intelligent sensing and information networking technologies. These innovations seamlessly integrate into physical transportation infrastructures via vehicle-to-everything communications, IoT, human–machine interface, and AI-enabled, data-driven analytics technologies.

Advancements in AI approaches, including Explainable AI, Large Language Models, Generative AI, and Edge AI are rapidly emerging and are finding their way also to the transport domain.

Consequently, there arises an urgent need for a comprehensive, evidence-based, and system-engineering-oriented approach to ensure the sustainable transition of such new and emerging technologies on a broader scale. Key questions include:

• What promising digital technologies can we see emerging, and what is needed to speed their development? How can EU-U.S. collaboration help?
• How can we make more structural use of the innovation potential and out-of-the-box thinking by young scientists and new start-ups and scale-ups?
• How can the various modes of transport collaborate in developing and deploying these new technological solutions, to enlarge the decarbonization effects?

• How can we leverage digitalization, AI, and other integrated system-of-systems technologies in design, renovation, or revamping of transportation infrastructures to make them adaptable to implementing digital shift strategies?
• How can mode shift be incentivized and optimized through the implementation of digitalization, AI, and other integrated system-of-systems technologies in the transport sector?
• How can we use social-technological digitalization tools to get community engagement involved in the participatory design processes as part of systemic solutions for accessible and inclusive transportation infrastructure and services, particularly in marginalized communities?
• How can AI-based predictive modeling techniques (e.g., neural networks, decision trees, and support vector machines) be adapted to effectively and efficiently predict transportation-related outputs with negative climate impacts, such as traffic congestion, vehicle emissions, and energy consumption?
• How do we fortify the system against potential data security threats, preserving CPS-T integrity and ensuring the trustworthiness of the information it disseminates and safe operations?
• How can we holistically streamline policy, planning, and engineering approaches in an integral process to transform traditional auto-dependent transportation into the climate-friendly and resilient transportation systems for all the users, within underserved communities and beyond?

CHALLENGES AND BARRIERS

• How can barriers such as lack of user engagement, disharmonious business models, insufficient funding for sustainable projects, and resistance to adopting new technologies be overcome?
• How can a coherent approach toward cybersecurity be developed? How can we effectively gauge cyber–physical security concerns and accurately forecast vulnerabilities for all users amid climate change events by using AI-driven data analysis tools or methodologies?
• To what extent do we need international standards and harmonization, and for what benefits?
• What are the key challenges and opportunities associated with optimizing digital interfaces between different modes of transport (e.g., road, rail, waterways) to reduce traffic congestion?
• How can we ensure that the applications and benefits are valid across various demographic regions (including urban and rural)?

CONCLUSION

• What are the critical factors and design principles for developing user-friendly digital tools that cater to the diverse needs of different user groups, including elderly and marginalized populations, while ensuring privacy and data security?
• What are the key technological advancements and innovations driving the development of automated transportation solutions, and how can these be leveraged to promote mode shift away from personal vehicles?
• What are the factors influencing individuals’ decisions to adopt automated transportation solutions, and how do these factors differ across different demographic groups and geographic regions?
• What are the challenges and opportunities associated with integrating automated transportation solutions with active transport modes such as walking and cycling, and how can these be addressed to encourage mode shift?
• Given the complexity of the system of systems technologies, what architecture reference framework is needed to guide the best practice of digitalization, AI, and C-ITS for transportation decarbonization at regional or local levels, following the principles for a comprehensive societal shift to decarbonized systems, that is, transformation, integration, and universality, as outlined in the Paris Agreement and the 17 Global Sustainable Goals established at the United Nations 21st Conference of the Parties?