A multi-layered safety model that integrates emerging technologies with human-centred practices, emphasising resilience engineering, adaptive training, transparent AI governance, and continuous learning across transportation ecosystems is proposed, arguing that technological innovation must be framed not as a replacement for human expertise but as an enabler of enhanced human performance.
Abstract
Emerging technologies are reshaping safety performance across all transportation domains, transforming traditional risk models and redefining the interaction between humans, systems, and complex operational environments. From artificial intelligence and machine learning to advanced sensors, digital twins, predictive analytics, and autonomous platforms, these technologies offer unprecedented opportunities to enhance situational awareness, optimise decision-making, and strengthen organisational resilience. At the same time, their rapid integration introduces new socio-technical challenges, including algorithmic opacity, demands for human–machine coordination, cybersecurity vulnerabilities, and the need for a workforce capable of navigating increasingly intelligent systems. This paper examines the evolving role of emerging technologies in transportation safety, analysing how their adoption both augments and complicates human performance in safety-critical operations. The analysis begins by exploring how emerging technologies enable a shift from reactive to predictive and preventative safety paradigms. AI-driven data analytics now allow organisations to detect weak signals, forecast failure patterns, and identify precursors to incidents long before they manifest operationally. Advanced sensing technologies, such as real-time physiological monitoring, (Light Detection and Ranging) LiDAR, and high-fidelity environmental perception systems, enable continuous surveillance of operational risk conditions. Digital twins provide dynamic, real-world simulations that support scenario testing, training optimisation, and risk assessment. Together, these tools significantly expand system observability and provide a foundation for proactive risk management across aviation, rail, maritime, and surface transport. The paper then critically evaluates the human factors implications associated with this technological advancement. As systems become more autonomous and decision pathways increasingly algorithmic, human operators are required to manage higher levels of abstraction, supervise complex machine behaviour, and integrate diverse streams of automated information. These shifts can reduce manual proficiencies, intensify monitoring burdens, and create new forms of cognitive strain, especially during system anomalies or degraded modes. Trust calibration becomes central: both over-reliance and under-reliance on intelligent technologies pose risks to safety performance. Successful integration requires human-centred design, explainable AI interfaces, transparent communication architectures, and training that strengthens resilience, critical thinking, and understanding of system limitations. Organisational and regulatory implications are also analysed. Transportation safety frameworks, such as Safety Management Systems (SMS), Fatigue Risk Management Systems (FRMS), and competency-based training programmes, must evolve to incorporate technology-driven safety indicators, algorithmic risk assessments, and new classifications of human–machine interaction hazards. Regulatory bodies face the challenge of establishing performance requirements, certification pathways, and oversight mechanisms for systems that are no longer deterministic. Organisations must also invest in workforce capabilities that align with emerging roles in AI supervision, data-driven decision support, and autonomous operations management. The paper concludes by proposing a multi-layered safety model that integrates emerging technologies with human-centred practices, emphasising resilience engineering, adaptive training, transparent AI governance, and continuous learning across transportation ecosystems. It argues that technological innovation must be framed not as a replacement for human expertise but as an enabler of enhanced human performance. The long-term success of emerging technologies in transportation safety depends on designing systems, organisations, and regulatory structures that maintain human responsibility, support human cognitive strengths, and ensure safe coordination between people and intelligent machines. This paper contributes a cross-domain, resilience-oriented synthesis that positions emerging technologies as socio-technical amplifiers rather than deterministic safety solutions.
Advanced Air Mobility (AAM) signals a transformative shift in aviation, introducing new vehicle types, operational models, and urban–regional transport that challenge traditional airspace management, regulation, and human performance. As AAM systems become more automated, data-driven, and distributed, resilience becomes key for safe, sustainable deployment. This paper explores resilience at the individual, team, organisational, and system levels, which are crucial for anticipating, absorbing, adapting to, and recovering from disruptions in complex environments.It places AAM within emerging mobility systems, leveraging technologies such as electric propulsion, autonomous systems, urban vertiports, airspace algorithms, and AI traffic management (UTM/UTM-X). These introduce operational interdependence, variable data quality, rapid scaling, evolving regulations, and unique failure modes. Resilience is vital for managing disruptions and ensuring safe operations amid system unpredictability, weather, cyber threats, and human–machine interactions. Resilience is also viewed as a human-centered and socio-technical trait. Operator and team resilience depends on adaptability, awareness, cross-monitoring, improvisation, and workload management, primarily as remote pilots and controllers oversee autonomous networks. Training should include scenario-based learning, degraded-mode simulations, and strategies for uncertainty and automation surprises. At the organisational level, resilience involves adaptive Safety Management Systems (SMS), predictive analytics, communication, and coordination among urban planners, regulators, air navigation service providers, manufacturers, and emergency services. Organizations must learn quickly from signals, adapt procedures in real time, and align strategies with human and urban limits. Governance must go beyond compliance to continuous monitoring, foresight, and proactive risk management.System resilience involves infrastructure, airspace design, digital ecosystems, and policies. Resilience in vertiport design, UAM corridors, networks, energy, and multimodal interfaces is crucial, requiring principles such as redundancy, diversity, modularity, and graceful degradation to keep systems operational in the face of failures. The paper concludes with a resilience framework emphasising human–machine teamwork, adaptive governance, cross-sector learning, and socio-technical integration. Success depends on technological innovation and the ability of organisations and ecosystems to adapt, remain human-centered, and resilient. Operational models like Single Pilot Operations (SiPO) and AI-supported supervision highlight early resilience challenges in AAM.
The development of disruptive technologies such as artificial intelligence (AI), autonomous and electric vehicles, and smart grids challenges legal frameworks to remain relevant, adaptive, and coherent over time. This is evident in the transport sector, where technological innovation has the potential to fundamentally reshape transportation, logistics, and urban design, or is already doing so. Against this backdrop, the present study examines the legal ramifications of such transformations by exploring whether AI-based tools can support forward-looking legal research. Specifically, it aims to identify and map potentially complex or unclear legal situations that may arise from the deployment of disruptive technologies. This study employs two large language models (LLMs), Microsoft 365 Copilot and Claude Sonnet 4.6, to generate possible future legal scenarios for the period 2026 – 2037, using a scenario-based research design primarily situated within a Swedish and EU legal context. Several methods for engaging with the LLMs are applied, including mixed prompting, Chain-of-Thought prompting, the RACE framework, and meta-prompting. The results show that both LLMs kept well to the prompt instructions regarding output format, including the integration of future scenarios, external drivers, and potentially challenging legal themes. Additionally, while differences between the responses of the two LLMs were observed, these were primarily differences of degree rather than of kind. The overall conclusions suggest that both LLMs demonstrate potential usefulness for further research and may serve as exploratory tools in legal research as a point of departure. However, although the scenarios produced by the LLMs were grounded in contemporary legal research “hot topics”, a more thorough examination of the validity of their claims is required to support well-founded decisions regarding future research. Finally, it is recommended that researchers should inter alia continue refining their “prompt literacy” while still relying on human judgment, especially for critically evaluating the validity and robustness of LLM-assisted research.
Traffic accidents are still a major threat to public safety, causing a lot of deaths, damage to property, and problems in society around the world. Intelligent, data-driven, and proactive traffic management systems have been made possible by the fast development of AI and ML, which has revolutionized traditional road safety practice. This paper presents a comprehensive review of recent AI-based approaches for enhancing road safety, with emphasis on accident prediction, driver behavior analysis, and connected vehicle technologies. In addition, the review examines the major factors contributing to road accidents and discusses the Safe System approach as a framework for improving transportation safety. Current challenges, including data quality, model interpretability, cybersecurity, privacy preservation, and regulatory constraints, are critically analyzed to highlight existing research limitations. Furthermore, emerging research directions explored as potential solutions for developing robust, scalable, and trustworthy intelligent transportation systems. The findings indicate that AI-driven technologies have considerable potential to improve accident prevention, traffic efficiency, and decision-making while supporting the development of safer and more sustainable road transportation systems.
D. Upadhyay· International Research Journ...· 0 citations
: The rapid increase in vehicle numbers and resulting traffic congestion have amplified critical challenges related to safety, environmental impact, and transportation efficiency. Road accidents account for approximately 1.19 million deaths annually, with an additional 20 to 50 million people injured. Moreover, congestion leads to the loss of nearly 50 billion hours and around 3 billion gallons of fuel each year. These pressing issues necessitate innovative and integrated solutions that can enhance the overall performance of the road. This study investigates the integration of Emerging Computing Technologies (ECT) into smart road infrastructures as a potential response to these challenges. It explores key enabling technologies (e.g., advanced communication systems, the Internet of Things (IoT), sensors, Artificial Intelligence (AI), big data, blockchain, and energy harvesting) for their capacity to support intelligent transportation systems. The paper further reviews real-world applications of smart roads, such as smart traffic lights, automated road maintenance, intelligent lighting, and energy-efficient infrastructure, illustrating their contribution to sustainable urban mobility. In parallel, it examines challenges impeding widespread adoption, including technical limitations, financial and regulatory constraints, cybersecurity risks, and societal acceptance. Finally, the study highlights future opportunities by discussing ongoing advancements in ECT and renewable energy integration, outlining their potential to revolutionize road network efficiency, safety, and sustainability.
Afzal Badshah, Ali Daud, S. Arafat et al.· Computers, Materials & C...· 0 citations
Security infrastructure has evolved from the use of reactive models toward the adoption of predictive technologies, transforming and enhancing threat anticipation, improving monitoring capabilities, and strengthening strategic decision-making driven by the integration of emerging technologies and artificial intelligence. This review study synthesizes the changes occurring across spatial, maritime, aerial, border, and cybersecurity infrastructure, evidencing the impact of the transition toward predictive technologies; it addresses the challenges and limitations these technologies introduce as a consequence of their operational deployment, and concludes by examining future lines of development in this domain. The review was conducted following the PRISMA® methodology, analyzing the scientific literature retrieved from seven indexed databases—SCOPUS, ScienceDirect, Web of Science, IEEE Xplore, Taylor & Francis, ProQuest, and PubMed—consistent with the full search scope. Articles were independently assessed by two reviewers, yielding an initial Cohen’s Kappa coefficient of 0.458; following structured discussion and consensus resolution, the final inter-rater reliability reached κ = 0.71, meeting the accepted threshold for scoping review methodology. The findings demonstrate a global transition toward intelligent security infrastructures, with measurable improvements in performance, accuracy, and response times in the generation of actionable intelligence to support strategic decision-making.
Omar Flor-Unda, David Puga, Hugo Alomoto et al.· Technologies· 0 citations
Advanced Metering Infrastructure (AMI) is a key enabler of digital and intelligent power systems, particularly in microgrid environments. However, existing research often addresses AMI from fragmented perspectives, limiting a comprehensive understanding of its role within integrated and data-driven energy systems. This paper presents a structured analysis of AMI based on a bibliometric and thematic review of recent literature, identifying the main research trends, technological drivers, and emerging directions in the field. The results reveal a transition of AMI toward a data-centric platform that supports real-time monitoring, bidirectional energy management, and intelligent decision-making. Key domains include cybersecurity, data analytics, communication systems, and distributed energy integration, while emerging technologies such as artificial intelligence and the Internet of Energy play a critical role in future developments. Finally, the paper outlines key challenges and provides strategic recommendations to support the effective deployment of AMI in microgrids, contributing to the development of resilient and sustainable energy systems.
Juan Camilo Riaño-Rueda, Melisa de Jesús Barrera-Durango, N. Muñoz-Galeano et al.· Electricity· 0 citations