refereferenmethjrefere Investigating Ongoing and Future Research Activities for the Advancement of Intelligent Access in Road Freight Transportation A study of Intelligent Access in the Context of Digitization and Automation: Opportunities for Intelligent Transport Systems and National Road Authorities Master’s thesis in Supply Chain Management ALBERT PÉREZ MARCUS SOHLBERG DEPARTMENT OF TECHNOLOGY MANAGEMENT AND ECONOMICS DIVISION OF SUPPLY AND OPERATIONS MANAGEMENT CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024 www.chalmers.se http://www.chalmers.se/ 1 Investigating Ongoing and Future Research Activities for the Advancement of Intelligent Access in Road Freight Transportation A study of Intelligent Access in the Context of Digitization and Automation: Opportunities for Intelligent Transport Systems and National Road Authorities MARCUS SOHLBERG ALBERT PÉREZ Department of Technology Management and Economics Division of Supply and Operations Management CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024 2 Investigating Ongoing and Future Research Activities for the Advancement of Intelligent Access in Road Freight Transportation A study of Intelligent Access in the Context of Digitization and Automation: Opportunities for Intelligent Transport Systems and National Road Authorities MARCUS SOHLBERG ALBERT PÉREZ © MARCUS SOHLBERG, 2024 © ALBERT PÉREZ, 2024 Department of Technology Management and Economics Division of Supply and Operations Management Chalmers University of Technology SE-412 96 Gothenburg Telephone +46 31 772 1000 Gothenburg, Sweden 2024 3 Investigating Ongoing and Future Research Activities for the Advancement of Intelligent Access in Road Freight Transportation A study of Intelligent Access in the Context of Digitization and Automation: Opportunities for Intelligent Transport Systems and National Road Authorities MARCUS SOHLBERG ALBERT PÉREZ Department of Technology Management and Economics Division of Supply and Operations Management Chalmers University of Technology Acknowledgement This thesis represents the culmination of an extensive research journey, and we would like to express our deepest gratitude to those who have supported us throughout this process. First and foremost, we extend our sincere appreciation to our supervisor, Stefan Jacobsson, for his guidance, constructive feedback, and encouragement. His expertise and has been fundamental in shaping our work. We are also grateful to our examiner, Gunnar Stefansson, whose critical evaluation and thoughtful suggestions have helped refine and strengthen this thesis. We would like to express our gratitude to the experts and project coordinators who generously dedicated their time to share their insights and experiences with us. Their contributions have provided essential perspectives on Intelligent Access in road freight transportation, significantly enriching our research. Finally, we acknowledge the Department of Technology Management and Economics at Chalmers University of Technology for providing the necessary resources and a stimulating academic environment that made this research possible. This thesis is the result of a collaborative effort, and we are truly grateful to everyone who has contributed to its success. 4 Abstract The purpose of the master thesis project was to research the potential role of Intelligent Access (IA) in automation and digitization in road freight transportation by covering how IA can strengthen ongoing or future research activities in road freight transportation, and how these could help National Road Authorities (NRAs) in better utilizing existing infrastructure. The study aims to provide answers to the following research questions: What are the characteristics of IA in road freight transportation? What are the ongoing and future research activities in Intelligent Transport Systems (ITS) and how could these contribute to IA? The research provided a detailed state-of-the-art for ongoing and future planned research activities in ITS that relate to IA. A total of 86 activities were found and ranked by a scoring system based on the abstract of each research activity by considering selected search words (ITS, IA, CCAM, Cooperative, Smart Infrastructure, Automation, Connectivity, Data, Road, Trucks, Freight, and High-Capacity Transportation (HCT)), in addition to the opinion of the two authors and based on literature study. To answer the research questions stated above, a total of 12 interviews were conducted. The interviewees were 6 experts in the area of IA and 6 project coordinators from the identified and selected research activities. The findings reveal that little information regarding future research activities was obtained since these are still in an early stage of development. In contrast, ongoing research activities are well-documented in databases, complete with abstracts, and have been organized and validated by institutions, which justifies their funding. In addition to this, further research to support the development of IA is needed, as none of the research activities even included the search word IA in their project abstracts or on the websites. For the characteristics of IA in road freight transportation, four analysis words (Smart Infrastructure, Connectivity, Data, and Automation) were asked to each interviewee to check their relevance and how they could contribute to IA. All recurring insights for each analysis word were collected and further analyzed. Consequently, this thesis contributes to a better understanding of IA definition and its value within road freight transportation. The findings offer valuable guidance for NRAs and members looking to optimize road freight transportation by ensuring “the right vehicle on the right road at the right time”. 5 Abbreviations Abbreviation Meaning AI Artificial Intelligence AHP Analytical Hierarchy Process BEV Battery Electric Vehicle CAD Connected and Automated Driving CCAM Cooperative, Connected and Automated Mobility C-ITS Cooperative Intelligent Transport Systems GDPR General Data Protection Regulation GHGE Greenhouse Gas Emissions HCT High Capacity Transportation IA Intelligent Access IAP Intelligent Access Program IoT Internet of Things ISAC Intelligent Surface Access Community ITS Intelligent Transport Systems LTL Less-Than Truckload NRAs National Road Authorities PEV Plug-in Electric Vehicle PDI Physical and Digital Infrastructure RSU Roadside Unit UVAR Urban Vehicle Access Regulation V2I Vehicle to Infrastructure V2N Vehicle to Network V2V Vehicle to Vehicle V2X Vehicle to Everything 6 Content 1. Introduction.............................................................................................................................................. 8 1.1. Background......................................................................................................................................8 1.2. Background of the Intelligent Surface Access Community (ISAC)...............................................9 1.3. Problem discussion.......................................................................................................................... 9 1.4. Purpose and research questions..................................................................................................... 10 1.5. Limitations..................................................................................................................................... 11 2. Frame of reference..................................................................................................................................12 2.1. CCAM and CAD........................................................................................................................... 12 2.1.1. Connectivity..........................................................................................................................12 2.1.2. Automation........................................................................................................................... 13 2.1.3. Cooperative...........................................................................................................................14 2.1.4. Data.......................................................................................................................................14 2.1.5. Mobility................................................................................................................................ 15 2.2. Intelligent Transport Systems........................................................................................................ 15 2.3. Smart Infrastructure.......................................................................................................................16 2.4. Geofencing.....................................................................................................................................17 2.5. Road freight transportation............................................................................................................17 2.5.1. High Capacity Transportation...............................................................................................18 2.5.2. Benefits of road freight transportation..................................................................................19 2.5.3. Disadvantages of road freight transportation........................................................................20 2.6. Intelligent Access.......................................................................................................................... 20 2.7. National Road Authorities............................................................................................................. 21 2.8. EU Regulations..............................................................................................................................22 2.9. General Data Protection Regulation............................................................................................23 2.10. Artificial Intelligence...................................................................................................................23 3. Methodology...........................................................................................................................................24 3.1. Data Collection.............................................................................................................................. 24 3.1.1. RQ1: What are the characteristics of IA in Road Freight Transportation?.......................... 25 3.1.2. RQ2: What are ongoing and future research activities in ITS and how can these contribute to IA?.............................................................................................................................................. 25 3.1.3. State of the art.......................................................................................................................25 3.2. Data Analysis.................................................................................................................................26 3.2.1. Selection of search words and analysis words......................................................................26 3.2.2. Selection of activities............................................................................................................27 3.2.3. Selection of Project Coordinators.......................................................................................29 3.2.4. Selection of Experts........................................................................................................... 29 4. Empirical findings.............................................................................................................................. 30 4.1. Experts........................................................................................................................................... 30 4.1.1. Expert 1.................................................................................................................................30 7 4.1.2. Expert 2.................................................................................................................................31 4.1.3. Expert 3.................................................................................................................................34 4.1.4. Expert 4.................................................................................................................................35 4.1.5. Expert 5.................................................................................................................................37 4.1.6. Expert 6.................................................................................................................................39 4.2. Project Coordinators...................................................................................................................... 40 4.2.1. Project Coordinator 1............................................................................................................40 4.2.2. Project Coordinator 2............................................................................................................42 4.2.3. Project Coordinator 3............................................................................................................43 4.2.4. Project Coordinator 4............................................................................................................44 4.2.5. Project Coordinator 5............................................................................................................46 4.2.6. Project Coordinator 6............................................................................................................48 5. Analysis.................................................................................................................................................. 50 5.1. Analysis related to RQ1.................................................................................................................50 5.1.1. Automation Insights............................................................................................................. 50 5.1.1.1. Analysis of Automation...............................................................................................50 5.1.2. Connectivity Insights............................................................................................................52 5.1.2.1. Analysis of Connectivity............................................................................................. 52 5.1.3. Smart Infrastructure Insights................................................................................................ 53 5.1.3.1. Analysis of Smart Infrastructure..................................................................................53 5.1.4. Data Insights......................................................................................................................... 54 5.1.4.1. Analysis of Data.......................................................................................................... 54 5.1.5. Other Aspects and Insights................................................................................................... 55 5.1.5.1. Analysis of Other Aspects........................................................................................... 55 5.2. Analysis related to RQ2.................................................................................................................56 6. Discussions............................................................................................................................................. 58 6.1. RQ1: What are the characteristics of IA in Road Freight Transportation?.................................................. 58 6.2. RQ2: What are the ongoing and future research activities in ITS and how can these contribute to IA?..... 59 7. Conclusion.............................................................................................................................................. 61 Appendices................................................................................................................................................. 73 A Databases.......................................................................................................................................... 73 B Interview guide................................................................................................................................. 74 C Experts & Project Coordinators Insights.......................................................................................... 75 D Research Activities........................................................................................................................... 77 8 1. Introduction The purpose of the master thesis project was to research the potential role of Intelligent Access (IA) in automation and digitization in road freight transportation by covering how IA can strengthen ongoing or future research activities in road freight transportation, and how these could help NRAs in better utilizing existing infrastructure. This chapter will develop and discuss the relevance and purpose of the project. Additionally, the problem will be outlined and defined. Finally, research questions will be raised along with the limitations of the thesis. 1.1. Background According to Lumsden (2007), freight transportation encompasses all the stages and deployed means of transporting goods through different channels, such as road, rail, sea, and air. The global transport system moves billions of tons of goods around the globe yearly (Greene, 2023), creating a direct economic impact both nationally and internationally, while influencing logistics processes, transportation methods, and the resources used to reach the final destination (Freight Transport | CEVA Logistics, s.f.). While focusing on road freight transportation, it is worth highlighting that it accounts for 25% of worldwide total transportation emissions and emits more than 1,75 billion metric tons of carbon dioxide (GtCO₂) yearly (Statista, 2024). Additionally, traditional transportation and fleet management have challenges due to reliance on manual processes, resulting in inefficiencies, resource wastage, increased costs, street congestion, and environmental degradation. Safety considerations, including accidents, vehicle maintenance difficulties, and driver conduct, are challenges in fleet management that need more attention (Apata Stella Bolanle et al., 2024). In addition to the problems mentioned above, there are also outspoken challenges in the areas of connectivity, vehicle misplacement, and digitization. The lack of mobile connectivity and sufficient broadband in rural areas results in a digital divide and reliable infrastructure, which is needed for technologically advanced solutions (Cottrill, 2018). An emergent need for innovation in skills, data management, and infrastructure has therefore been detected. To optimize existing infrastructure usage, and promote environmentally sustainable road freight transports from a supply chain perspective, it is essential to align road usage with conditions set by NRAs and facilitate IA (ISAC Project – Intelligent Surface Access Community, 2024). This will help in achieving, according to Asp & Wandel (2022): "To have the right vehicle with the right load on the right road at the right time". IA is defined as a system that regulates vehicle access to road networks based on aspects such as weight, dimensions, emissions, and cargo, as well as how these align with infrastructure conditions to enhance road safety and environmental goals (Kural et al., 2021). The main key enablers are ITS which can be explained as a technological architecture that combines cooperation, automation, data, and communication technologies (CCAM) (Elassy et al., 2024; Noori, 2013). In this context, CCAM integrates connectivity, automation, and cooperation among vehicles, infrastructure, and road users through real-time data sharing and seamless communication (European Commission, n.d.), while the concept of Connected and Automated Driving (CAD) 9 focuses on real-time interaction between vehicles and their environment (other vehicles, infrastructure, pedestrians, etc) and advancing on autonomous vehicle ecosystems (Connected Automated Driving, 2024). Connectivity is one of the main ITS technologies that enable seamless communication between vehicles, infrastructure, and users via vehicle-to-everything (V2X) technologies for better navigation, and decision-making to enhance proactive traffic management, hazard detection, and route optimization (Singh, 2023) through data sharing between vehicles and infrastructure, enabling real-time monitoring, predictive analytics, and decision-making. Types of data include static, historical, real-time, and dynamic, all crucial for improving safety and optimizing road usage (European Commission, n.d.). Additionally, smart infrastructure can be known as the maintenance of the physical infrastructure by using sensors to collect data for further analysis and make a decision based on it, as well as adaptive signage, dynamic traffic management as well as geofencing (Economic Role of Transport Infrastructure, 2018). One of the main goals is to reduce human intervention by employing technologies for navigation and decision-making, from basic driver assistance to fully autonomous systems. For this reason, automation improves safety and reduces inefficiencies, while supporting environmental sustainability through optimized vehicle operations (Overview of Driving Automation Levels, 2016). 1.2. Background of the Intelligent Surface Access Community (ISAC) In 2023, the ISAC project was launched as part of the Conference of European Directors of Road’s (CEDR) Transnational Research Programme (TRP). CEDR TRP aims to produce research results that can be implemented by CEDR members, contributing to a safe, sustainable, and efficient road network across Europe. Participation in these programs is open to any legal European entity. The Call 2023 program specifically focused on IA, providing funds for research on optimizing infrastructure and advancing sustainable freight transport (CEDR Research Call 2023, 2023). This thesis project is part of one of several work packages in the ISAC project, a collaborative initiative aimed at enhancing road infrastructure management and promoting sustainable freight transport. Engaging NRAs from countries including Finland, Ireland, the Netherlands, Norway, and Sweden, ISAC addresses critical issues in transport such as budget limitations and climate impact. The project focuses on leveraging digital transformation within both road management and logistics sectors to improve monitoring and control of road usage by road freight vehicles. The main objective of IA is to coordinate and manage vehicle usage with road conditions effectively by ensuring optimal infrastructure use and minimizing environmental impact. ISAC’s research includes the creation of scenarios that demonstrate IA’s potential to improve efficiency, safety, and environmental standards. Through these scenarios, guidelines and strategies for NRAs need to be offered to enhance sustainable road freight transportation both nationally and internationally (ISAC Project – Intelligent Surface Access Community, 2024). 1.3. Problem discussion 10 The ISAC project aims to find solutions that optimize road infrastructure use and enhance sustainability in freight transport across various European countries by helping NRAs, whose aim is to enhance traffic management thanks to a major shift towards digitalization (Digitalisation: Driving The Transition Towards Smart And Sustainable Mobility, 2024). NRAs face financial and environmental constraints in managing road infrastructure, prompting the need for innovative approaches. Through IA, ISAC seeks to use digital tools and data from connected vehicles to manage road usage effectively, promoting the goal of “the right vehicle with the right load on the right road at the right time” (ISAC Project – Intelligent Surface Access Community, 2024). Two core concepts for IA are Connected and Automated Driving (CAD) and Cooperative, Connected, and Automated Mobility (CCAM). In brief, CAD supports vehicles to interact with the surrounding environment, make decisions in real time, and manage driving with different levels of involvement from humans. This is possible thanks to the integration of connectivity technologies such as V2X and different automation systems. Part of V2X is Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) communications, for example (U.S. Department of Transportation, n.d.). CCAM, on the other hand, takes more of a macro view and focuses on the transport system as a whole and the cooperation between vehicles among others, as explained through V2X (European Commission, n.d.). While CAD and CCAM are critical for IA, there are several outspoken challenges for the purpose of integration into current transport systems. Firstly, much research has been done in regard to ITS, CAD and CCAM separately, but there is little research on how these three topics can be combined to achieve IA. Gaps in existing data-sharing systems and standardizing practices across regions are required, making it difficult to align vehicle use with infrastructure capacity and environmental goals (ISAC Project – Intelligent Surface Access Community, 2024). Secondly, implementing IA in a complex logistics environment with multiple stakeholders is challenging, requiring the integration of digital infrastructure and cooperation across borders (ITS Directive And Action Plan, s. f.). 1.4. Purpose and research questions The purpose of the master thesis project was to research the potential role of IA in automation and digitization in road freight transportation by covering how IA can strengthen ongoing or future research activities in road freight transportation, and how these could help NRAs in better utilizing existing infrastructure. The report will first present a state-of-the-art for ongoing and planned research activities in ITS that relate to IA. This review will focus on the selected search words: Cooperative, Smart Infrastructure, Automation, Connectivity, Data, Road, Trucks, and HCT. By examining these search words, the research aims to identify potential developments and future directions in ITS that intersect with IA. Following the state of the art, a selection of the most relevant research activities will be identified. To gain further insights, interviews were conducted with project coordinators as well as experts in ITS and IA. This combination of a literature review and gaining experts’ insights support deepening the understanding of IA’s current state and in conducting the final analysis based on the data collected. Based on the purpose discussed above, the following research questions were developed: 11 -​ RQ1: What are the characteristics of IA in road freight transportation? -​ RQ2: What are the ongoing and future research activities in ITS and how could these contribute to IA? The study seeks to contribute to the ISAC project providing valuable guidance for IA among all research activities in ITS. 1.5. Limitations The study focuses on ITS as well as CAD and CCAM in road freight transports. Thus, rail, aviation, and sea transport are excluded, as well as public transportation and the transport of passenger cars. Even if ITS has many applications, the focus of this thesis will be on the selected search words: Intelligent Transport Systems (ITS), IA, Cooperative, Smart Infrastructure, Automation, Connected, Data, Road, Trucks, and High-Capacity Transportation (HCT) by following the Multi-Criteria Decision Analysis (MCDA). This helped in evaluating and selecting analysis words, a process explained in Chapter 3.2. A primary focus was on research activities in European countries, whilst a few additional single markets such as Australia were reviewed as well. 12 2. Frame of reference In this chapter, the frame of reference will help in providing more context to the thesis project and the background of the research. Here, an emphasis to better understand the concepts will be put on ITS, Smart Infrastructure, Geofencing, CCAM and CAD as well as road freight transportation.Various resources have been used and they are referred to in the frame of reference. 2.1. CCAM and CAD Connected, Cooperative and Automated Mobility, often abbreviated as CCAM, is a concept that integrates connectivity, cooperation, and automation for the purpose of increasing sustainability, efficiency, and safety in transport (European Commission, n.d.). Even if vehicles can be seen as connected devices today, communication between vehicles, infrastructure and other road users will increase due to CCAM. The general objectives of the CCAM Partnership are increasing safety in road transport, ensuring inclusive mobility and goods access for all, strengthening the competitiveness of European industries, reducing negative impacts from road transport on the environment, and capitalizing on knowledge to accelerate the development and deployment of CCAM solutions (CCAM Partnership - CCAM, 2022). CAD stands for Connected and Automated Driving which enables communication between vehicles and other elements, both statically or dynamically, to share relevant information for fleet management (Connected Automated Driving, 2024). 2.1.1. Connectivity According to the World Bank Group - 2024, connectivity involves all physical facilities and services to facilitate the transportation of goods and people within and across borders despite their position within a network. Connectivity permits navigation for real-time information, as well as safety, to find or avoid accidents when identifying the exact position or information about the road and weather conditions (Vehicle Connectivity: Telematics and V2X Communication, 2023). V2X communication implies connectivity with several elements such as with other vehicles, infrastructure, or pedestrians, for example, and sharing data or information in different directions, to optimize traffic flow, road safety, and the transportation environment (Vehicle-to-everything (V2X) in the Autonomous Vehicles Domain – a Technical Review of Communication, Sensor, and AI Technologies for Road User Safety, n.d.). 13 Figure 1: ​ V2X Connectivity. Source: Vehicle-to-everything (V2X) in the Autonomous Vehicles Domain – a Technical Review of Communication, Sensor, and AI Technologies for Road User Safety, n.d. The main goal of V2V communication is to make driving safer. For instance, when a vehicle in front of another vehicle stops, it shares the information with the vehicle behind to avoid a possible accident. For autonomous vehicles, V2V becomes a common enabler for coordinated maneuvers (Vehicle to Vehicle “V2V” Communication: Scope, Importance, Challenges, Research Directions and Future, n.d.). V2I, on the other hand, is a concept where vehicles communicate with surrounding physical or digital infrastructure such as traffic lights, road signs, or road tolls. For instance, traffic lights can turn green or red depending on the situation and adapt to the traffic when necessary. Additionally, real-time V2I communication can enable drivers or automated vehicles to get updated about lane closures, road works, or detours (An Empirical Study of Vehicle to Infrastructure Communications - an Intense Learning of Smart Infrastructure for Safety and Mobility, n.d.). Vehicle-to-Pedestrian (V2P) enables vehicles to detect pedestrians in crossing sections if the driver does not see the pedestrian, as well as being an important feature for autonomous vehicles to reduce accidents with pedestrians, especially in urban areas (An Overview on V2P Communication System: Architecture and Application, n.d.) Vehicle-to-Network (V2N) can be used to obtain information about real-time traffic updates in order to manage the optimal route to get from point A to B, as well as diagnosing vehicles remotely (Singh, 2023). 2.1.2. Automation Automation in transportation refers to the use of new technologies to minimize human interaction (New Technology and Automation in Freight Transport and Handling Systems, n.d.). There are 5 different levels of automation regarding human interaction (Overview of Driving Automation Levels, 2016) which are presented below: Level 0 (No Automation): Vehicles where the driver is totally responsible for all the driving tasks (steering, braking, acceleration, and controlling the surroundings and interaction with other vehicles). No automation is involved, except for simple warning systems such as collision alerts or warnings when switching lanes. 14 Level 1 (Driver Assistance): Implies simple automation characteristics in which vehicles are able to assist drivers when controlling steering or acceleration/braking. Despite this, the driver still remains responsible for all other aspects and has to be aware of the environment. Level 2 (Partial Automation): The vehicle can either monitor both steering and acceleration or braking at the same time in determined situations or conditions. Despite this, the driver has to be aware of the surroundings in order to intervene if necessary. Level 3 (Conditional Automation): Enables the vehicle to manage all driving tasks, such as surroundings within a defined operational domain (ODD). However, the driver must be prepared and take control if the situation requests it. Level 4 (High Automation): Enables vehicles to work in a completely autonomous way ODD without requiring human intervention. Even if the system encounters a situation outside its operational parameters, it can safely manage the situation, such as stopping or parking. Nevertheless, a driver is still needed for some specific use-cases. Level 5 (Full Automation): Is the highest level of automation and where the vehicle is completely autonomous without any human input, steering wheel, or pedals, being able to handle any driving scenario. 2.1.3. Cooperative Within supply chain management, Bengtsson and Kock (1999) identify cooperation as a strong or tight bond between companies to accomplish common goals, while Lambert et al. (1999) define cooperation as a "tailored relationship based on mutual trust, openness, shared risk and shared rewards that yield a competitive advantage, resulting in business performance greater than would be achieved by firms individually". There has been a call for better decision-making, stakeholder engagement, and technology integration by providing input on strategic goals and leading to better decision-making by incorporating diverse perspectives (Wilson et al., 2003). Hence, technology development such as collaborative decision support systems as well as community-based planning efforts have been encouraged (Jankowski et al., 1997). 2.1.4. Data Data is crucial in ITS and allows stakeholders to develop, test, and optimize safety, infrastructure, and efficiency in transport (Kessler et al., 2016). Thanks to technological advancements and digitalization with new concepts such as Smart Cities, the Internet of Things (IoT), new wireless technologies, as well as reduced costs in storing data, the importance of data will just continue to increase. Along with this, data sharing between vehicles and infrastructure will also increase significantly (European Commission, n.d.). Data used in ITS can be divided into 4 different types, namely static data, historical data, real-time data, and dynamic data. Static data doesn’t change over time and could be, for example, the length of a bridge or a tunnel. Historical data refers to data collected from previous events such as the number of trucks sold in a specific year. Real-time data is provided directly to different stakeholders upon collection such as 15 real-time monitoring of traffic updates. Dynamic data, on the other hand, changes frequently and could be data for weather updates, shared daily. 2.1.5. Mobility Since CCAM stands for Connected, Cooperative Automated Mobility, it plays a crucial role in enhancing the efficiency and effectiveness of freight transportation. In road freight transportation, mobility is understood as the efficient and effective movement of goods through road networks (Mobility of Freight (Selected Cargo) | the Geography of Transport Systems, 2022). Factors such as infrastructure quality, regulatory frameworks, technological advancements, and environmental considerations influence the speed, reliability, and sustainability of freight movement (Stepper, 2023). 2.2. Intelligent Transport Systems The definition of ITS can be explained as a technological architecture dedicated to improve safety, efficiency and sustainability of transportation networks. Through data, communication systems, and automation, ITS enables smarter decision-making in traffic management and infrastructure usage. Applications such as adaptive traffic signal systems, real-time congestion management, and mobility predictions ensure smoother traffic flows, reduced environmental impacts, and improved safety. ITS also facilitates the development of smart cities through innovations like V2X communication and IoT-driven analytics (Elassy et al., 2024; Noori, 2013). The concept of ITS originated as a response to increasing urbanization and its associated challenges, such as traffic congestion and emissions. Early systems focused on coordinating traffic signals and basic congestion monitoring. Over time, advancements in IoT, 5G networks, and Artificial Intelligence (AI) transformed ITS into a sophisticated ecosystem encompassing automated toll systems, real-time traffic analytics, and autonomous vehicle communication. Modern ITS solutions aim to reduce environmental footprints, energy usage efficiency, and support sustainable urban development (Jeung et al., 2010; Singh & Nandi, 2019). ITS applications are diverse and transformative, addressing various aspects of transportation to enhance efficiency and sustainability: ●​Traffic Management: ITS improves traffic flow and reduces delays using adaptive traffic signals and predictive congestion algorithms, enabling real-time route adjustments and reduced idle times (Ferreira & d’Orey, 2012; Ang et al., 2019). ●​Autonomous and Connected Vehicles: V2V and V2I communication technologies ensure safer and more efficient road usage by supporting vehicle navigation and hazard detection in order to enhance overall road safety (Khalid et al., 2018; Javed et al., 2016). ●​Public Transit Optimization: Real-time monitoring and predictive analytics help public transportation schedule optimization, improve reliability, and enhance user satisfaction (Khattak et al., 2019). ●​Environmental Sustainability: Traffic congestion reduction and fuel usage optimization, ITS directly contributes to lowering greenhouse gas emissions (GHGE). 16 Applications like eco-driving advisories and dynamic speed control further support environmental goals (Al-Turjman & Lemayian, 2020; Noori, 2013). ●​Smart Parking Solutions: ITS uses sensor networks and real-time data to identify available parking spaces, significantly reducing time spent searching for parking and lowering emissions (Jeung et al., 2010). ITS plays an important role by reducing travel times, improving road safety, and therefore optimizing fuel consumption and lowering emissions. Furthermore, ITS supports economic growth by reducing logistics costs and improving freight efficiency, which benefits industries and urban economies alike (Ang et al., 2019; Singh & Nandi, 2019). Despite its many benefits, ITS faces challenges in regard to data security and user privacy, particularly with the vast amount of sensitive data collected. Besides, both IT and physical infrastructure costs for upgrades and the need for collaboration and interoperability between systems are the main barriers to implementation. Additionally, public acceptance and trust are also critical, as the deployment of ITS often requires changes in regulatory frameworks and behavioral adjustments by users (Javed et al., 2016; Khalid et al., 2018). Since not long ago, ITS has become crucial for smart city development by integrating it with other urban systems and creating sustainable and livable environments. IoT, 5G, and big data analytics become the main pillars for ITS, while their integration ensures that the population can adapt to growing populations and changing mobility demands while maintaining environmental sustainability (Khattak et al., 2019; Al-Turjman & Lemayian, 2020). 2.3. Smart Infrastructure Smart infrastructure is the use of digital technologies to improve the efficiency, sustainability and maintenance of the physical infrastructure by using sensors to collect data and for decision-making. Based on that, it can be described as consisting of three basic elements: data management, sense-making, and decision-making (Transportation, Land Use, and Environmental Planning, 2019). In the new era of smart cities and digital advancements and improvements, smart infrastructure can be applied in several situations (Smart Cities Cybersecurity and Privacy, 2018): ●​ Smart Infrastructure: Use of sensors as well as technologies such as water and energy networks, streets and buildings to support the infrastructure. ●​ Smart Mobility: Transportation networks with enhanced and real-time monitoring control systems. ●​ Smart Environment: Provides smart innovation and ICT (Information and Communication Technologies, 2024) in order to incorporate natural resource protection and supervision such as waste management or pollution sensor control. ●​ Smart Governance: Based on the urban space and linked with technology for service delivery and resource utilization (Resource Utilisation, 2024) in accordance with government policy. According to the Economic Role of Transport Infrastructure, the term “smart” can be considered as a continuous interconnection between 1I which, based on the new technologies, will reduce human intervention in vehicle driving and manage traffic flows accordingly. Thanks to the adoption of ITS, smart infrastructure can be referred to as an enabler connector between vehicles and infrastructure and vice-versa. Dynamic 17 https://www.sciencedirect.com/topics/computer-science/information-and-communication-technologies https://www.sciencedirect.com/topics/computer-science/resource-utilisation signs and adapting speed limits on highways or roads enable drivers to modify their behavior based on the decision taken after the data has been analyzed and managed (Economic Role of Transport Infrastructure, 2018). 2.4. Geofencing The authors have decided to include geofencing since the interviewees often mentioned it as an enabler for IA and as part of ITS for the first research question. The concept of geofencing has been around since the mid-90s and with a purpose to create a virtual perimeter covering a geographical area. Mobile devices are connected to the selected area and an alert is issued once any of the mobile devices cross the line of the virtual perimeter for controlling and monitoring citizens through their mobile devices (Nait-Sidi-Moh et al., 2013). So-called Global navigation satellite systems (GNSS) are often further supporting the universal system for tracking and geofencing, other communication and information technology solutions are also used. Not only does geofencing help in monitoring traffic, but its usage area goes far beyond that. Thanks to the technology, drivers can be informed digitally and through in-vehicle technology, rather than by building expensive infrastructure that communicates with drivers. Road conditions, information about accidents, the collection of payments for parking, and speed limits can all be shared through the concept of geofencing, for example (Foss, Seter, & Arnesen, 2019). Geofencing can also help in reducing emissions and improving air quality by limiting the use of larger vehicles or fossil-fueled vehicles in certain urban areas, as well as close to schools. In Sweden, local partners and governments initiated a project to test geofencing following the terrorist attack in Stockholm in 2017, where five persons were killed as a truck ran through Drottninggatan (European Commission, 2020). Besides, certain bus lines such as 16 and 55 are hybrid or electric and where geofencing is adopted on both lines (Geofencing: A New Tool to Make Urban Transport Safer and More Sustainable?, 2024). By forcing vehicles to switch to electric engines in zones with many pedestrians or cyclists, the city has introduced digital transport regulations that reduce speeds, reduce noise, and enhance air quality. 2.5. Road freight transportation Road freight transportation can be divided into national and international road freight transport. Here, national road freight transport refers to road transport between two locations (having loading and unloading points) within the same country, and where the vehicle must be registered in that country. International freight transport, on the other hand, involves road transport between two locations in different countries, regardless of where the vehicle is registered (Glossary: Road Freight Transport, 2023). Looking at its increase in popularity, the usage of road freight increased much from 1990 until 2005, specifically from 58.4% to 72.4% when measured in ton kilometers (tkm) (Glossary: Tonne-kilometre (Tkm), 2023), and where the remainder is allocated for railroad freight and waterborne freight. Contrary to what many believe, the usage of road freight will also continue increasing until 2030, accounting for as much as 75.4% of global freight. By comparison, rail freight will decrease from 27.9% to merely 15% by 2030, showing the increasing usage and importance of road freight in the future (Kural et al., 2021). 18 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Road_freight_transport https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Tonne-kilometre_(tkm) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Tonne-kilometre_(tkm) https://www.researchgate.net/publication/356493956_Intelligent_Access_Policy_ensuring_the_right_vehicle_on_the_right_road_at_the_right_time Europe is not an exception where inland passenger traffic is greatly dependent on road traffic and infrastructure, accounting for 90% of the total passenger traffic. For inland freight transportation, road traffic accounts for 75%, being the dominant means of transportation. From 1990 to 2020, the length of Europe’s highways increased from 30,000 km to 73,000 km, similar to efforts done in China and US projects (Ignatov, 2023). Here, Spain stands out, having the longest highway network in the EU, only third after China and the US. Portugal’s highway network system was built solely after 1990 and is the EU’s fifth longest. Many of the roads in the EU were finalized between 1990 - 2020 and we have seen a strong growth of the length of highways since the 1990s. Consequently, travel times have decreased by 8.6% in European regions. Comprising 27 member states, it is worth highlighting the differences in infrastructure quality between member countries that pertain to historical development, economic differences, and geographical constraints, for instance. 2.5.1. High Capacity Transportation Studies state that HCT enables traffic reduction by requiring fewer trips, although stricter vehicle standards are needed for road safety, and compliance with regulations to minimize risks. Additionally, it has been proved that axle increase reduces axle loads and hence, contributes to lower wear on roads and bridges. From a productivity perspective, changes on HCT can minimize the need for both drivers and vehicles while lowering energy consumption by enhancing operations and making them more cost-efficient. Although these systems could provide considerable societal benefits, HCT reforms have not been widely implemented in many countries. In Sweden, for instance, it is estimated that increasing the maximum vehicle length from 25.25 meters to 34.5 meters while maintaining a gross weight of 64 tons could generate benefits up to 13 times the cost of infrastructure upgrades over a 40-year period (Lindqvist et al., 2020). Furthermore, adopting such measures would significantly enhance efficiency by reducing CO2 emissions, costs, and the space required for infrastructure by 15-50% at the vehicle level and 8-15% at the freight system level, without increasing road wear, infrastructure degradation, or the frequency of accidents (High Capacity Transport, 2019) (ITF Transport Outlook 2019, s. f.-b). 19 Figure 2: Transportation distribution. Source: High Capacity Transport, 2019. Despite these advantages, many nations are slow to adopt HCT systems. Reports from ITF (2017, 2018, 2019) suggest that political and public hesitancy could be mitigated through the integration of ITS technologies. Tools such as vehicle tracking, route optimization and geofencing have been recommended to enhance public confidence and minimize the need for infrastructure investments (Aronietis & Voege, 2018). Similarly, Moore, Regehr, and Rempel (2014) emphasize the importance of additional measures to facilitate HCT reforms, as observed in jurisdictions where these policies have been successfully implemented. 2.5.2. Benefits of road freight transportation There are plenty of outspoken benefits of road freight which have resulted in the surge of its popularity and usage, which can mainly be attributed to its reliability, flexibility, and quick deliveries (Nkesah, 2023) adopting a concept associated with Lean production and Just in Time, companies tend to keep less stock for consistent and continuous flows of deliveries and goods within shorter periods. Contrary to rail freight, road freight transportation allows the delivery of goods on time through alternative roads thanks to its flexible aspect of taking different routes when roads cannot be used due to weather conditions or congestion problems (Reis & Macário, 2019). Looking at its economic viability, it is comparatively easy to enter the market as a third-party operator, requiring less investments and initial capital compared to sea and rail transport. Additionally, an outspoken benefit is therefore that the road freight sector enjoys healthy competition, as well as innovation, with a constant improvement of services (Engström, 2016). Its adaptability in managing both large and small shipment sizes, from HCT transports to Less-Than Truckload (LTL), along with its importance in multimodal transport and connecting with other transport modes, makes it highly important. Road transport mostly manages the first and last segment for the other transport modes used (sea, air, rail, and inland transportation) as these transportation modes cannot offer delivery of goods door-to-door. This allows making products available in a flexible and timely way and makes road freight transport necessary, especially at the start and the 20 https://pdf.sciencedirectassets.com/320511/1-s2.0-S2590198223X00062/1-s2.0-S2590198223002142/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBoaCXVzLWVhc3QtMSJGMEQCIGaxOJxY6oY3PnXFGF0xDKnSHArvIEcKldRYKDYK06miAiAw7qR04pOAHvWqZvmwc3xYMcLwyyJDrzYH73OQl5cEASq8BQii%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMfDOLGbnWVO4vqehSKpAF3UrBtnppwJ1ba97gGl5UcXnCQ%2F6NVQdfcnvtoq%2BZkcKtcWHfpEZ8QCWyHn3h8QU%2FOU0oVOpvAlLW864mLVpB5OLCV72cA76pbQA%2FMZR74m3zzc2OEKuOpwxXGroDAduFyKbprcN7uzf%2F0V1ZyNBfaelI1KImVw6eaKV0FfvDBUyZzfYklc6RCd6EnRBMaHrd2WGtkI9raIfPc6XMyT46kHTj4N3jXVjeqChwMM8EsfcCotedaPgbJycSXGnMH7ub44qB%2F71BQYNW5SYv5xw4lIkkvlHg52iKWBD6lQQTa3lr7W5ljiutWq7wK5wEeJKdgYMVj2vO%2BCxc68vwEomeBYVobWAvLfcxDeWX6XYUsq73EB7OuBObG47oy%2BSx2KQnYt1V98T0F3qBuSPy%2FHzRyqyTx8%2BBjWuJosLTn698hZyjvVCiasooJzSPt0Nr%2BozrF%2BSBpM%2FfEf%2F%2B1oke1pqoPl3bDi7nkA5m31FH3oStfcYy96W7OjIWZNw%2B83US3Z69dVStE99zwjLPgli137K7unA6GLb%2B0wvD1VwHVY2tb59653Usl8B0xFDOe%2BNej0g0fiQk7607miV76%2Bkk04vslqS8K6f4EVK4Hr37W%2FaFj5tMDIFQRo8NSEM8m7bHOliqJlerh1sF9nkom9GrFxs6CkJrPEoMBvfeO%2F99L3IhZeotmzwWgm%2Fcss8LWmaMbeR3up1xucWYqCDqfk51LqBiIcfE90VVZzLo80vPy3d5x%2FC4GxYamxEMrGjGbcT6hbNkojs7pDooGAlwACuTqad0rhzOgZNl2R5xPxSecUxqWOwu0BYv3f0NFxF%2Bb%2FG6vteagBHWWE%2BzztqZrD7UD6LcZYIl6Z8fUsZOdogh1gqAalUwq5PHuQY6sgHpxaWmDfIzCzTx8QvUq5y9%2F7NECLiXvcN%2FigkqPTgRs0d2SMeSciT2jo5QAPrP9VHXertL52asbz3mjnsCLr%2Bnqr%2Brz7d7cpqfRf1JtyhA%2FIMe6G3EhNibzxi97oXmccxEso9fjb9nEuuoltPVBeYs6oEUZOrcs61zv5uEyDfMekjkEbYxIAa8ytEQqNoTDL%2Bv3v%2FaR6yRkzth4gGA2cwi7VZH%2FQDaChEtLeYkFXRUQGcr&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241111T101209Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYZYXTCC7P%2F20241111%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=48761aa4b01f931f74f9fcb2696038cc381d0e2b777b7b690dd8f7cdf36a1359&hash=eff4ac7746633443ea7b4ae69b08a4bcc9dfdcde6e90009eea31eb655f390675&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2590198223002142&tid=spdf-1174cdfd-43cc-42ad-813c-82427b1a8f32&sid=762abdcf138a6045514962e4c934318fe5f5gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=09005f0456065b54595658&rr=8e0d8098782792dc&cc=se https://www.sciencedirect.com/book/9780128144640/intermodal-freight-transportation end of a multimodal transport setup sometimes referred to as first and last-mile logistics. 2.5.3. Disadvantages of road freight transportation Despite the benefits of road freight transport, there are various outspoken disadvantages. As mentioned earlier in the report, road freight is a great contributor to GHGE, where CO2 is a major culprit and other polluting substances include particulate matter and NOx which not only accelerate climate change but also affect air quality leading to various health issues among citizens globally (Nkesah, 2023). Diesel road freight transportation engines in particular emit large quantities of harmful pollutants, resulting in respiratory issues, cardiovascular diseases, increased risk of cancer, and premature death (World Health Organization [WHO], 2005). Worth mentioning in addition to increased road freight transports, passenger vehicles are set to double by 2050 (Hao et al., 2016), posing even greater challenges to our environment and societies. As a result, businesses should focus more on sustainability and not only on reducing operating costs, which road freight transport can generally help with. New vehicle concepts such as HCT seem promising and get increasingly more interest from researchers. In brief, HCT refers to the concept of transporting goods with vehicles that are heavier and/or longer than accepted by existing regulations. The implementation of HCT vehicles could have a major impact in urban areas and reduce truck trips by 50%, while CO2 emissions can be reduced by as much as 40% (Cederstav et al., 2023), having a major impact on the environment in the areas. The implementation of alternative vehicles such as battery electric vehicles (BEV) and plug-in electric vehicles (PEVs) have a significant impact on the reduction of GHGE from transportation unless other alternatives are also introduced. 2.6. Intelligent Access IA can be described as a dynamic system that is designed to monitor and regulate vehicle access to different roads by using advanced technologies such as geofencing, telematics, and real-time data collection (Kural et al., 2021). The main goal of IA is to ensure compatibility between road infrastructure and vehicles by considering vehicle dimensions, weight, and emissions. By comparing and matching road conditions with these vehicle capabilities, IA can assist in increasing road safety, reducing infrastructure wear in bridges and tunnels, and supporting environmental sustainability. The Department of Transport and Main Roads in Australia (Transport Certification Australia, 2021), a country that has been very active in the area of IA, defines it as a technology-driven system, using satellite-based tracking (GNSS) and telematics to manage and monitor heavy vehicle transports on road systems. The main goal of IA is to make sure that vehicles use roads according to their weight, size, and operational needs, improving safety and protecting infrastructure at the same time. The National Heavy Vehicle Regulator in Australia explains more about the Intelligent Access Program (IAP) which is a partnership between road agencies in Australia (National Heavy Vehicle Regulator, n.d.) and that started in 2009. The IAP is considered one of the pioneering large-scale implementations of IA systems globally and was among the first government-run programs to integrate telematics, regulation, and industry participation into a unified framework. The IAP was created together with Australian road agencies which provides operators access or improved access to the road network 21 https://pdf.sciencedirectassets.com/320511/1-s2.0-S2590198223X00062/1-s2.0-S2590198223002142/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBoaCXVzLWVhc3QtMSJGMEQCIGaxOJxY6oY3PnXFGF0xDKnSHArvIEcKldRYKDYK06miAiAw7qR04pOAHvWqZvmwc3xYMcLwyyJDrzYH73OQl5cEASq8BQii%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMfDOLGbnWVO4vqehSKpAF3UrBtnppwJ1ba97gGl5UcXnCQ%2F6NVQdfcnvtoq%2BZkcKtcWHfpEZ8QCWyHn3h8QU%2FOU0oVOpvAlLW864mLVpB5OLCV72cA76pbQA%2FMZR74m3zzc2OEKuOpwxXGroDAduFyKbprcN7uzf%2F0V1ZyNBfaelI1KImVw6eaKV0FfvDBUyZzfYklc6RCd6EnRBMaHrd2WGtkI9raIfPc6XMyT46kHTj4N3jXVjeqChwMM8EsfcCotedaPgbJycSXGnMH7ub44qB%2F71BQYNW5SYv5xw4lIkkvlHg52iKWBD6lQQTa3lr7W5ljiutWq7wK5wEeJKdgYMVj2vO%2BCxc68vwEomeBYVobWAvLfcxDeWX6XYUsq73EB7OuBObG47oy%2BSx2KQnYt1V98T0F3qBuSPy%2FHzRyqyTx8%2BBjWuJosLTn698hZyjvVCiasooJzSPt0Nr%2BozrF%2BSBpM%2FfEf%2F%2B1oke1pqoPl3bDi7nkA5m31FH3oStfcYy96W7OjIWZNw%2B83US3Z69dVStE99zwjLPgli137K7unA6GLb%2B0wvD1VwHVY2tb59653Usl8B0xFDOe%2BNej0g0fiQk7607miV76%2Bkk04vslqS8K6f4EVK4Hr37W%2FaFj5tMDIFQRo8NSEM8m7bHOliqJlerh1sF9nkom9GrFxs6CkJrPEoMBvfeO%2F99L3IhZeotmzwWgm%2Fcss8LWmaMbeR3up1xucWYqCDqfk51LqBiIcfE90VVZzLo80vPy3d5x%2FC4GxYamxEMrGjGbcT6hbNkojs7pDooGAlwACuTqad0rhzOgZNl2R5xPxSecUxqWOwu0BYv3f0NFxF%2Bb%2FG6vteagBHWWE%2BzztqZrD7UD6LcZYIl6Z8fUsZOdogh1gqAalUwq5PHuQY6sgHpxaWmDfIzCzTx8QvUq5y9%2F7NECLiXvcN%2FigkqPTgRs0d2SMeSciT2jo5QAPrP9VHXertL52asbz3mjnsCLr%2Bnqr%2Brz7d7cpqfRf1JtyhA%2FIMe6G3EhNibzxi97oXmccxEso9fjb9nEuuoltPVBeYs6oEUZOrcs61zv5uEyDfMekjkEbYxIAa8ytEQqNoTDL%2Bv3v%2FaR6yRkzth4gGA2cwi7VZH%2FQDaChEtLeYkFXRUQGcr&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241111T101209Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYZYXTCC7P%2F20241111%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=48761aa4b01f931f74f9fcb2696038cc381d0e2b777b7b690dd8f7cdf36a1359&hash=eff4ac7746633443ea7b4ae69b08a4bcc9dfdcde6e90009eea31eb655f390675&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2590198223002142&tid=spdf-1174cdfd-43cc-42ad-813c-82427b1a8f32&sid=762abdcf138a6045514962e4c934318fe5f5gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=09005f0456065b54595658&rr=8e0d8098782792dc&cc=se https://www.researchgate.net/publication/356493956_Intelligent_Access_Policy_ensuring_the_right_vehicle_on_the_right_road_at_the_right_time and supports in monitoring and assuring compliance with access conditions provided by road managers. By using wireless communication and satellite tracking via the In-Vehicle Unit (IVU), the time, location and identity of vehicles can be monitored remotely. Thanks to IAP, operators can use heavier or larger vehicles, or bridges or roads that wouldn’t be possible otherwise. The applications of IA are plentyfold, which has clearly been shown in the IAP project. It has proven to increase road safety by monitoring trucks on designated roads, reducing the risk of accidents and assuring that vehicles assure safety regulations. This is particularly important for heavy vehicles such as trucks that present major risks to public safety because of their larger size, weight, and operational complexity. Through control and monitoring, the IAP can manage access to specific roads and highways, which helps in preserving infrastructure and reducing damage. This is especially important in Australia where the road network has older roads and bridges which are not designed to withstand the stress caused by modern heavy vehicles. Thanks to enforcing regulations on regulatory compliance such as those related to the weight of vehicles, the IAP can subsequently increase road safety and reduce wear and tear on infrastructure. Not to forget, thanks to the monitoring of vehicles, the IAP also enhances efficiency by reducing congestion and improving logistics (National Transport Commission, 2018). Many NRAs see IA as an enforcement tool, supporting making sure that policies and road usage regulations are followed (Aarts et al., 2023). This is further supported by the Transport Committee in the EU with members of the parliament pushing for the introduction of IA and to introduce automatic control systems along main roads for the verification of dimension and weight limits for trucks (European Parliament, 2024). While some claim that IA can assure that regulations are being followed, from a practical point of view, it is primarily a monitoring tool. As European NRAs generally don’t have the legal powers needed nor enforcement roles, utilizing IA as an enforcement tool would be difficult. 2.7. National Road Authorities In Europe, NRAs are important organizations that supervise and manage road networks in order to ensure safety, efficiency, and sustainability in transport systems across their respective countries. These entities, which are typically public or semi-public bodies, operate under the guidance of national ministries of transport or infrastructure, implementing policies and maintaining critical road infrastructure. Notable examples include the Highways Agency in the UK, Rijkswaterstaat in the Netherlands, and Dirección General de Carreteras in Spain. They collaborate with both local and regional road agencies to align national objectives with local transportation needs (CEDR, 2023). The primary responsibilities of NRAs include long-term infrastructure planning, construction, operation, and maintenance of essential road systems, such as highways, bridges, and tunnels. They regulate traffic, enforce road safety standards, and promote sustainable practices, such as reducing carbon emissions and incorporating renewable energy into road designs. In addition to these tasks, NRAs gather and manage data related to traffic flow, road conditions, and accidents, using these insights to enhance decision-making and optimize road network operations (Regeringen och Regeringskansliet, s. f.). Many NRAs are also involved in international organizations, such as the Conference of European Directors of Roads (CEDR), which fosters 22 collaboration and knowledge exchange among European road authorities (CEDR, 2023). Currently, NRAs face several challenges, including aging infrastructure, increasing traffic demand, and the need to meet strict environmental targets. Digitalization is key when transforming the management of road systems such as real real-time traffic monitoring, automated toll systems, and digital twin modeling. These innovations are complemented by sustainability initiatives, which aim to integrate multimodal transport systems and adapt infrastructure to improve climate resilience (NAPCORE, 2024). Furthermore, NRAs will need to strengthen cross-border collaboration to harmonize standards, facilitate innovation, and address shared transportation challenges (Towards a Common European Mobility Data Space, 2023). 2.8. EU Regulations The European Union is obliged to move towards an intelligent and sustainable mobility and transport sector in the interests of the environment, competitiveness and resilience. According to the Commission’s Sustainable and Smart Mobility Strategy (SSMS) (Sustainable And Smart Mobility Strategy – Putting European Transport On Track For The Future, 2020), digitalization can help drive this transition, establishing a truly efficient and interconnected multimodal transport system for both passengers and freight that will help the EU to meet its European Green Deal, focusing on increasing the EU’s climate ambition for 2030 and 2050 (A Europe Fit For The Digital Age, 2020).. The deal also focuses on accelerating the shift to sustainable and smart mobility, positioning the EU as a global leader, and implementing a European Climate Pact (COMMUNICATION FROM THE COMMISSION, 2019). While the EU transport sector generates substantial data, the current data landscape remains fragmented across various ecosystems, making accessibility and interoperability challenging (Creation of a Common European Mobility Data Space, 2023). In order to resolve this, the EU has proposed the creation of a Common European Mobility Data Space (EMDS), which aims to facilitate access and share mobility and transport data by harmonizing technical and legal frameworks by supporting sustainable and smart mobility through efficient transport services and reduced emissions (Creation of a Common European Mobility Data Space, 2023). By involving all Member States in order to enhance the reuse of ITS data, NAPCORE activity aims to ensure harmonized data-sharing standards and interoperability (NAPCORE, 2024; "On The Framework For The Deployment Of Intelligent Transport Systems," 2010) via National Access Points (NAPs). Being DATEX II part of NAPCORE framework, it enables harmonized and interoperable data-sharing for traffic and travel information exchange by accommodating light and heavy-duty vehicles by ensuring standardized communication of road conditions, temporary changes, and usage rules, reducing misunderstandings and enhancing data usability while aligning with EU delegated regulations, supporting the digitization and automation of road transport systems (DATEX II Organisation, 2021). Additionally, regulations on dimensions and weights for HCT have also been outlined in Council Directive 96/53/EC, commonly referred to as the Weights and Dimensions Directive (Directive - 96/53 - EN - EUR-LEX, s. f.), which have been 23 https://eur-lex.europa.eu/legal-content/EN/AUTO/?uri=CELEX:52020DC0789 https://eur-lex.europa.eu/legal-content/EN/AUTO/?uri=COM:2019:640:FIN updated through several amendments, specifically Directive (EU) 2015/719 (Directive - 2015/719 - EN - EUR-LEX, s. f.-b), Decision (EU) 2019/984 (Decision - 2019/984 - EN - EUR-LEX, s. f.), and Regulation (EU) 2019/1242 (Regulation - 2019/1242 - EN - EUR-LEX, s. f.). These updates introduced specific exemptions from the established maximum weights and dimensions for vehicles and vehicle combinations. The purpose of these changes is to encourage the use of alternative fuel powertrains, including zero-emission options, improve vehicle aerodynamics, support trials of modular systems (longer and/or heavier vehicle combinations made up of standard vehicle units, also known as European Modular Systems), and promote intermodal transport operations (Directive - 96/53 - EN - EUR-LEX, s. f.). 2.9. General Data Protection Regulation The General Data Protection Regulation (GDPR) governs how personal data of individuals within the EU is collected, stored, and processed. It is a data protection and security law, the strongest in the world, and with a purpose to protect rights and freedoms of people, related to personal data and ensuring free movement of the data within the EU (Council of the European Union, n.d.). 2.10. Artificial Intelligence There’s been a sharp rise in the research and adoption of Artificial Intelligence (AI) (European Central Bank, 2024), and the field of transportation is not an exception. While there is no exact definition of AI, the European Parliament (2020) defines it as: “AI is the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity.”. It furthers explains that technical systems can observe their surroundings thanks to AI, gather and interpret information, identify challenges, as well as taking actions to manage certain objectives. The technical systems collect pre-processed or collected data with the help of sensors, analyze the data, and create suitable responses. Worth mentioning is also that AI systems can change their behaviors and adapt based on the results from past actions, allowing for a degree of autonomy in decision making and when executing tasks. 24 3. Methodology This chapter explains the process used when conducting the research for the project. The data collection and analysis processes used during the project are first presented below. This part is followed by a brief introduction of the state-of-the-art for the research activities and how they were selected, the research approach and the research questions. 3.1. Data Collection Developing research questions in research activities provides guidance and to set a structure on how research activities should be executed, what data should be collected, and how the data should be analyzed (Bryman, 2007). In short, the research questions give a point of departure and support in the orientation when conducting the thesis. For this thesis project, two research questions were created to guide the project and better understand both the concept of and developments of IA in road freight transports. The research questions helped in better understanding the characteristics of IA, as well as in obtaining more information about current and future activities in IA, which support its development. To address the research questions, a combination of both literature reviews and expert interviews was conducted. Expert interviews are widely used and the success depends on both the quality and knowledge of the interviewees, as well as the number of interviewees. A minimum of ten interviews is advised (Mergel et al., 2019). A semi-structured guide was created for the interviews, including questions that directly related to the research questions, RQ1 and RQ2. The interview guide can be found in Appendix B Interview Guide. In total, twelve interviews were conducted, including six interviews with experts and six interviews with project coordinators or activity managers. The activity coordinators or managers were responsible for or belonging to the shortlisted activities. The interviews aimed to provide a comprehensive understanding of IA, to see if there’s a lack of research, and how IA can support in strengthening ongoing automation and digitization processes at NRAs and in the transport sector. The structure of the research questions and their specific objectives are outlined below. A ranking of the 86 activities was conducted, resulting in shortlisted activities. The scores attributed to the highest ranking activities were based on both the search word density as described earlier, as well as a personal evaluation performed by the authors by reading the abstract of the research activity and considering how it can help IA. Subsequently, the project coordinators and experts in the field of ITS and/or IA were contacted to participate in semi-structured interviews, which were conducted using a predefined guide shown in Appendix B to ensure consistency and uniformity across all participants. Conducting semi-structured interviews was deemed the most suitable option to collect data for the thesis. First, conducting interviews offers a more effective method of data gathering compared to methods like systematic quantitative surveys or participatory observation (Bogner et al., 2009). Besides, experts can share insider information that is otherwise difficult if not impossible to find, particularly for future research activities, or where research conducted is limited. Prior to choosing an interview format, the authors identified the three main interview formats as structured, semi-structured, and unstructured. The authors chose the semi-structured interview format as it allows for follow-up questions and for greater information exchange (Adams, 2015), and these allow interviewees to answer direct questions rather than be 25 able to speak freely (Fox, 2000). For qualitative research, semi-structured interviews are also more useful as to obtain more detailed and in-depth information, while allowing for better adaptability and flexibility (Ruslin et al., 2022). The questions were open-ended questions and not closed, allowing for the interviewees to elaborate more freely in their replies. The meetings were held through video calls rather than in person as this helps to avoid travel costs (Mergel et al., 2019), saving time, and the capability of gathering several persons at a specific time. This was particularly useful as many interviewees were based outside of Sweden and in countries such as Belgium, Italy, Australia, and Greece. All responses were captured accurately and rather than relying on note-taking, which allowed both the interviewers to focus fully on the conversation and ask follow-up or clarifying questions when necessary. After completing the interviews, each recording was transcribed to facilitate the data analysis and ensure that the information was documented. By transcribing the video calls, a substantial time could be saved (McMullin, 2021) and the authors could make sure that all relevant details were collected, assuring the quality of the information collected. 3.1.1. RQ1: What are the characteristics of IA in Road Freight Transportation? The first research question supported the development of knowledge of the characteristics of IA in road freight transports. To understand how IA can support NRAs in utilizing existing infrastructure as efficiently as possible, it is required to know what IA is and what characterizes it. To ask the question to both experts and project coordinators, analysis words were used in interviews to reply to whether these could facilitate IA. The analysis words included: Automation, Connectivity, Smart Infrastructure, and Connectivity since, based on the MCDA in chapter 3.2, these were the ones that obtained the highest score value (100%). 3.1.2. RQ2: What are ongoing and future research activities in ITS and how can these contribute to IA? The second research question was created to support identifying what ongoing or planned future research activities in ITS can contribute to IA. The search engines or databases used to search the activities are shown in Appendix A, considering ITS, IA, CCAM, Cooperative, Smart Infrastructure, Automation, Connectivity, Data, Road, Trucks, Freight, and HCT as relevant topics to search for research activities. ●​ Ongoing research activities: A focus on mapping existing activities and activities that could support IA. Evaluations of their scope, objectives, and outcomes were performed to identify whether the activities were suitable or not.x ●​ Future research activities: Identifying planned or proposed research activities that could address gaps or challenges in current IA implementations. 3.1.3. State-of-the-art A state-of-the-art is useful in providing a holistic overview of the latest developments in selected research areas and can be described as the highest level of development for a 26 device, technique or scientific field at a specific time (Haase, 2010). While literature reviews are crucial in the area of scientific research, helping in collecting, explaining, analyzing, and integrating vast amounts of data and information (Barry et al., 2022), a state-of-the-art for the ongoing and future research activities within IA was deemed needed and searched for, either alone or in combination with each other. 3.2. Data Analysis The methodology followed for the data analysis was divided into two different sections regarding RQ1 and RQ2. For RQ1, all recurrent insights from the interviewees (both experts and project coordinators) were stated individually for each analysis word. After this, a review of each analysis word was performed based on the previous literature study, by providing relevant references and proving its importance. For RQ2, the analysis words for each ITS research activity (attached on appendix D) was compared with the insights of all interviewees. The aim of this was to analyze if the research activities were on the right track from what the experts and project coordinators stated in order to facilitate IA in road freight transportation. For this, chapter 3.2.1. explains the procedure to choose the relevant analysis words from the search words used for the ongoing and future planned research activities within ITS. Additionally, chapter 3.2.2. explains the selection of activities and the scoring system applied to contact project coordinators. The aim of this is to find the relevance for IA in road freight transportation. 3.2.1. Selection of search words and analysis words To search for the research activities, ten search words were selected to search the activities on the search engines shown in Appendix A. By following the Multi-Criteria Decision Analysis (MCDA) framework approach (Marco Dean, 2022) , each search word was scored based on five predefined criteria: ●​ Relevance (C1): Does the search word directly relate to the core concept of "Intelligent Access"? ●​ Coverage (C2): Does it cover subthemes or related topics within the domain of ITS? ●​ Applicability (C3): Is it commonly used or recognized in the context of ITS or Intelligent Access research? ●​ Impact (C4): Does the search word yield significant and relevant results in academic or practical applications? ●​ Originality (C5): Does it provide unique value or avoid redundancy with other selected search words? Each criteria was equally weighted, and a binary scoring system (0 = no, 1 = yes) was 27 applied for simplicity. The binary scoring and equal weights eliminated subjective biases and ensured a fair comparison across all search words, shown in table 1. Search words Relevance (C1) Coverage (C2) Applicability (C3) Impact (C4) Originality (C5) Total IA 1 1 1 0 1 4 ITS 1 1 1 1 0 4 CCAM 1 1 1 1 0 4 Cooperative 1 1 1 1 0 4 Smart Infrastructure 1 1 1 1 1 5 Automation 1 1 1 1 1 5 Connectivity 1 1 1 1 1 5 Data 1 1 1 1 1 5 Road/Trucks/Freight 0 1 1 1 0 3 HCT 0 1 1 1 0 3 Table 1: MCDA for the search word selection. In a multi-criteria evaluation, it is common to define a minimum total score value. Terms meeting 60% (score of 3/5) can be considered to have reached a sufficient standard to add value (Samo Drobne & Anka Lisec, 2009). From the result, the chosen analysis words for the interviews were Automation, Connectivity, Smart Infrastructure, and Data. 3.2.2. Selection of activities To evaluate the relevance of each activity for IA shown in appendix D, a structured prioritization scoring system was developed. The scoring system was based on two selection criteria: the presence of the chosen search words identified in the activity abstracts (search word density), as well as a personal evaluation performed by the authors. The methodology proposed by Kipper et al. (2014) demonstrates the value of assigning numerical scores by applying a semi-quantitative approach which combines both qualitative insights and structured quantitative assessments in order to gain an effective activity prioritization by integrating strategic criteria and assigning weights based on relevance. Similarly, Multi-Criteria Analysis (MCA), as outlined in Multi-Criteria Analysis: A Manual (Department for Communities and Local Government, 2009), provides a transparent and consistent framework for evaluating activities. Using techniques like weighting, MCA combines quantitative measures with subjective judgments to create an overall ranking, supporting objective and flexible decision-making processes. In order to find ongoing and future activities on IA, the authors used a set of selected search words, which were either IA in itself or search words that were deemed relevant to IA: Intelligent Transport Systems (ITS), Cooperative, Automation, Connected, Data, 28 High-Capacity Transports (HCT), as well as any of the search words Road, Truck, or Freight. Each activity website and abstract were reviewed and for every search word found, a score of 1 was given to the activity. If any of the search words Road, Truck or Freight were found alone or in combination, a point of only 1 was assigned. The accumulated score for each activity was the sum of all the search words identified in its abstract or the presentation of the activity on its respective website. In addition to the evaluation done via the search word research, and after conducting extensive literature reviews to gain a deeper understanding of the relevant concepts and topics, a personal evaluation was also done for each activity. The evaluation was done independently by both authors, expressed in the formula as Personal Valuation, where subindex n=1,2 considers both valuations from the thesis partners, being: 1 (Marcus) and 2 (Albert). The scale ranged from 1 to 4 and where 1 was given to activities with no relevance to IA in road freight transportation, and where 4 was given to activities with a high relevance to IA. Each activity was expressed as i=1…86 where the valuations were applied for each of them, obtaining individual scores. It is noted that for proper function optimization, a binary variable was considered: Where the Personal Valuation by the thesis partners was described as: Following the restriction: To combine these factors, a weighted scoring formula was applied. In this formula, the search word score was given a weight of 20%, while the average personal valuation was weighted at 80%, which aligns with the principles of the Analytical Hierarchy Process (AHP) described by Saaty (1980) in The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. AHP enables the assignment of subjective weights to factors deemed more critical through judgment. This systematic and defensible approach emphasizes evaluation over objective data to determine the relevance of activities, ensuring a well-founded prioritization process. Hence, the formula was expressed as follows: By using the formula above, each activity was given a total score, resulting in a balance between both the frequency of search words found and a subjective evaluation done by the thesis partners. The activity coordinators of the highest-scoring activities were subsequently shortlisted and contacted for the purpose of setting up interviews. If they were unavailable or did not respond, an activity coordinator associated with a slightly 29 lower-scoring activity was contacted, continuing sequentially down the ranking until a sufficient number of participants were found. 3.2.3. Selection of Project Coordinators This chapter outlines the methodology used to interview project coordinators. As described in Chapter 3.2.2, "Selection of Activities," research activities were ranked based on their total score. Following this ranking, project coordinators were contacted according to their respective scores, prioritizing the most relevant research activities. The process continued until a total of six project coordinators had been reached. 3.2.4. Selection of Experts In contrast, experts were selected and interviewed through a word-of-mouth approach rather than a structured ranking process. This method relied on recommendations and referrals from relevant stakeholders, ensuring that the selected experts had significant knowledge and experience in the field. A total of six experts were interviewed. Experts were defined as professionals with robust and specialized knowledge, strong academic records, practical experience, or decision-making authority in areas relevant to Intelligent Access (IA) and transport systems. 30 4. Empirical findings The collected data is presented in this chapter without any subjective comments, considering both experts' and activity coordinators' insights and answers to the questions from the Interview guide attached in Appendix B. The findings are divided into activity coordinators and experts. 4.1. Experts In interviews with experts, both research questions were addressed to gain IA insights. Discussions covered its definition, characteristics, relevant research activities, and how these activities contribute to advancing IA systems. 4.1.1. Expert 1 Title: Post-Doctoral Researcher. RQ1 1. Automation The interviewee expressed skepticism about automation's direct role in IA. Automation was related to autonomous vehicles by the interviewee, who noted that it does not directly enable IA, which is fundamentally a digital service. She emphasized that IA is more about data and regulatory processes than automation technology. 2. Connectivity Connectivity was highlighted as essential for IA, particularly for V2I. For IT-infrastructure, the interviewee mentioned that platform implementation for communication between stakeholders (e.g., municipalities, regulatory authorities, and OEMs) is crucial for IA implementation. 3. Smart Infrastructure Not considering smart infrastructure as a strict prerequisite for IA since it can function without significant infrastructure investments. It is more relevant to focus instead on digital platforms on the vehicles although geofencing and advanced sensors could enhance IA in specific use cases like regulating vehicle entry into sensitive urban zones or icy roads. 4. Data Data is considered a main pillar of IA, emphasizing the need for both real-time and historical data. While real-time data can support dynamic decision-making, historical data is essential for long-term planning and regulatory compliance as well as vehicle characteristics such as vehicle weight, height, load, and route information were mentioned as critical for optimizing freight systems and reducing infrastructure wear and tear. 31 5. General comments ●​ Defining IA: digital service enabling the right vehicle on the right road at the right time, aligning with traffic regulations and infrastructure conditions. However, she noted that IA lacks a clear, universally accepted definition and varies based on its purpose and stakeholders. ●​ Digitalization as the Key Trend: Among automation, electrification, and digitalization, the interviewee identified digitalization as the most relevant trend for IA. It serves as the foundation for the service by enabling efficient data sharing and regulatory oversight. ●​ Purpose-Oriented Nature of IA: designed to reduce road maintenance costs and manage environmental zones as well as enhance traffic management in urban areas. ●​ Global Examples: The IAP Program IAP was cited as a leading example, demonstrating how IA can function as a regulatory framework for heavy vehicle access. ●​ Barriers to Adoption: challenges such as privacy concerns, regulatory fragmentation, and resistance from freight companies and drivers who may perceive IA as overly restrictive or time-consuming. ●​ Additional analysis word: Geofencing was mentioned as a specific application of IA for urban areas, allowing authorities to control vehicle access to sensitive zones based on predefined conditions. RQ2 The interviewee did not mention any specific ongoing or future activities tied to IA for truck road transport, although activities related to digitalization, connectivity, and regulatory processes were discussed: ●​ Ongoing efforts with Trafikverket in Sweden: Exploration of digital platforms to reduce gradual deterioration to infrastructure by regulating heavy truck access. While this is not labeled as a specific activity, it reflects a focus area relevant to IA. ●​ Potential Vehicle Trials: The interviewee referred to discussions about conducting vehicle trials where trucks share data (e.g., weight, height, cargo) via a common platform for real-time access management and permit approvals. She indicated that this idea remains conceptual and has not yet materialized into a concrete activity. ●​ The IAP Program: The interviewee highlighted the Australian IAP as an established example of IA implementation. Mention of Lack of Existing Activities: The interviewee also highlighted that IA-related services often exist only as small trials or conceptual discussions without substantial real-world implementation or strong government and industry backing in Sweden or Europe. 4.1.2. Expert 2 Title: Professor Emeritus in Engineering Logistics. RQ1 1. Automation 32 The interviewee emphasized that automation and IA are interdependent. Automated vehicles require IA systems to ensure compliance with regulations, such as routing and load limits, while automation was defined as crucial for enabling efficiency in vehicle loading and unloading, particularly in controlled environments like mining and tunneling activities but no mention in road freight transportation. Sensor integration on vehicles and infrastructure is vital for automation systems to function properly. 2. Connectivity Detected as a fundamental component of IA, the interviewee discussed its importance in enabling data exchange between vehicles (V2V), infrastructure (V2I), and centralized systems like cloud platforms and databases to provide dynamic routing based on conditions such as load limits and traffic by integrating vehicle sensors while accomplishing regulations. Connectivity also extends to V2V and V2I communication, particularly in contexts like HCT, where vehicles interact with infrastructure like loaders, cranes, or dynamic traffic control systems. mentioning the importance of creating an "information ecosystem" that integrates vehicle, infrastructure, and loading equipment data. He also noted ongoing work on connectivity standards in Sweden and Europe to enable consistent IA implementation. 3. Smart Infrastructure Essential for supporting IA. Examples included: ●​ Weight in Motion Sensors: Systems that measure loads on axles as vehicles pass over bridges in order to ensure weight restriction limits. ●​ Dynamic Traffic Control: Adaptive systems to redirect traffic in case a major event occurs in order to prevent congestion and ensure smooth logistics. ●​ Road Condition Sensors: Sensors implemented on roads to detect frozen ground in order to adapt or modify load capacities. Databases were also emphasized to store detailed information about infrastructure capabilities, such as bridge load limits and road conditions, to aid in route planning. Although infrastructure is critical, the most important element is maintaining accurate and accessible databases for infrastructure and vehicle data. 4. Data Described as a key factor for IA, the interviewee mentioned vehicle weight, axle loads, road conditions, and environmental factors. The necessity of integrating vehicle data to optimize routing for managing infrastructure (e.g., bridges) onto a single database was mentioned. The importance of both static and real-time data for effective decision-making was also stated: ●​ Static Data: Infrastructure capacity, legal restrictions, and vehicle characteristics. ●​ Real-Time Data: Road conditions, vehicle positioning, and traffic flow. The interviewee noted that weight data is among the most crucial for IA, as improper weight distribution causes safety and infrastructure risks. He also mentioned that data governance and ensuring trust among stakeholders are critical challenges for effective data sharing. 33 5. Additional Insights ●​ HCT: IA enables a safer and efficient operation for heavy and large vehicles in order to operate on designated or specific routes and therefore have robust monitoring systems. ●​ Regulatory and Institutional Challenges: Consistent regulations and institutions to manage IA systems are needed. Following the Australian model as an example where institutions like Transport Certification Australia (TCA) ensure rules are followed can be a potential framework for Europe. ●​ Fragmented regulations in Europe were identified as a barrier to the adoption of IA, especially on cross-border transport. ●​ Electrification and IA: The integration of electrified or hydrogen-powered trucks with IA systems was stated, emphasizing the charging infrastructure, where electric trucks require careful planning when charging and facilitating efficient route planning by integrating charging schedules together with freight operations. ●​ Social Acceptance and Stakeholder Incentives: Public perception and stakeholder benefits were seen as the main factors for IA adoption since transport operators might resist its adoption due to concerns about increased monitoring and potential penalties. ●​ Emerging Innovations: The interviewee highlighted innovations like "El-On-Road" systems, where vehicles are charged dynamically while driving, and battery-swapping systems as promising solutions to challenges in electrification and IA. RQ2 The interviewee mentioned the following activities as ongoing activities: ●​ HCT: The interviewee is working on a project that involves high-capacity trucks for transporting heavy materials such as rocks and sand. This project involves testing connection systems, sensors, and data-driven platforms that align with IA to find the appropriate route and comply with access permissions. ●​ Aeroflex and CEFES: The interviewee mentioned the Aeroflex project, which has evolved into the CEFES project. These projects explore ways to integrate high-capacity vehicles and electrification into European freight transport systems. ●​ Frozen Roads: The interviewee refers to experiments using road temperature sensors to allow heavier trucks during winter when roads are frozen and more stable. The following future activities and developments were also mentioned in order to support IA: ●​ Self-Service Permit System: discussions about creating a digital platform for self-service permits were highlighted. The system would allow transport operators to request access for oversized or overweight vehicles in real-time. ●​ Information System Development: development of an information platform in order to integrate road and bridge data with access management systems to provide planners with real-time insights into where heavy trucks can travel safely. ●​ Global Initiatives: The interviewee briefly refers to global efforts in developing IA frameworks, including projects in Australia and Europe (e.g., ISAC and P-ARC initiatives). 34 4.1.3. Expert 3 Title: Project Leader in HCT. RQ1 1. Automation The interviewee linked automation to IA by noting that machine-readable traffic regulations are essential for both automated vehicles and IA. Automation could ease the adoption of IA since automated vehicles inherently require data sharing, such as vehicle position, speed, and route details. The interviewee emphasized that while automation is not strictly necessary for IA, its presence reduces resistance to IA by normalizing data-sharing practices. 2. Connectivity Connectivity was identified as critical, with an emphasis on V2V and V2I technologies. Ongoing discussions about standardized connected systems across Europe to ensure better communication were stated. Examples such as icy road warnings via connected vehicles with the ability to regulate speed dynamically through geofencing were mentioned. 3. Smart Infrastructure Smart infrastructure in urban areas and sensitive locations like bridges were mentioned in addition to geofencing and advanced systems for frozen roads to enforce speed limits. However, it was stated that IA can operate without extensive smart infrastructure investments by utilizing existing vehicle technologies, such as fleet management systems. 4. Data Described as a pillar for IA, key data types mentioned included vehicle weight, position, speed, and road conditions. The interviewee stressed the importance of integrating vehicle-generated data with infrastructure data to enable dynamic decision-making, such as rerouting heavy trucks to roads with higher capacity or frozen roads during winter. 5. General comments ●​ Geofencing: highlighted as a critical application of IA to enforce speed limits and route restrictions. ●​ Digital freight documentation: Was mentioned as a key enabler for cross-border IA adoption, with examples from Estonia and Italy. ●​ Traffic flow optimization: The interviewee discussed the potential for IA to reroute traffic dynamically, minimizing congestion and optimizing urban mobility. RQ2 The interviewee mentioned the following ongoing research activities that could 35 potentially facilitate IA. ●​ Frozen roads project: This project explores allowing heavier trucks to operate on frozen roads, which are more stable during winter. Sensors monitor road and bridge conditions to determine the allowable weight limits. ●​ Geofencing projects: Projects testing geofencing technologies to regulate vehicle speeds and weights in specific zones, such as bridges. These projects involve Volvo and Scania and include passive and active geofencing. ●​ Special transport projects: Projects focused on abnormal or special transports, requiring additional data collection and permit systems to manage oversized or overweight vehicles. The interviewee also mentioned the following future research activities that could potentially support IA: ●​ Frozen Roads Project: A follow-up phase of the Frozen Roads Project, scheduled for the winter season of 2025-2026, aims to implement pilot testing of the technology