Jul 2026· Smart and Resilient Transportation· pp. 1-18· 0 citations· 35 references
TL;DR
A smart resilient transportation architecture that integrates sensing, AI-enabled intelligence, adaptive control, governance and resilience capabilities within a unified framework is developed and shows that resilience emerges from architectural integration rather than isolated smart technologies.
Abstract
Transportation systems are increasingly exposed to climate-related, operational and cyber disruptions that threaten the continuity of supply chains. Although smart transportation technologies enhance efficiency and visibility, they do not inherently ensure resilience. This study aims to develop and refine a smart resilient transportation architecture that explains how artificial intelligence (AI) and adaptive capacity can be systematically integrated to enable supply chain continuity under disruption conditions.
This study adopts a conceptual and systems-based design approach grounded in smart transportation systems and transportation resilience research. A multilayer architecture is developed that integrates sensing and data acquisition, AI-driven intelligence, adaptive control mechanisms and resilience capabilities.
This study shows that resilience emerges from architectural integration rather than isolated smart technologies. AI-enabled prediction and learning enhance adaptive capacity only when embedded within coordinated control mechanisms such as dynamic routing, intermodal switching and resource reallocation. The interaction of sensing, intelligence and adaptive control strengthens absorptive, adaptive and recovery capacities of transportation systems, thereby stabilizing physical flows and enabling supply chain continuity in the presence of disruptions.
This study develops a smart resilient transportation architecture that integrates sensing, AI-enabled intelligence, adaptive control, governance and resilience capabilities within a unified framework. The architecture explains how transportation systems can anticipate, respond to and recover from disruptions while maintaining logistics flows. By linking transportation resilience to supply chain continuity, this study provides a transportation-centric perspective that extends existing smart transportation and resilience research.
Digital technologies are transforming modern supply chains by improving efficiency, visibility, and responsiveness while helping organizations address disruptions caused by natural disasters, geopolitical conflicts, pandemics, cyberattacks, and market uncertainties. Supply chain resilience has evolved from traditional reactive risk management to proactive and adaptive approaches supported by technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, Big Data Analytics, Cloud Computing, and Digital Twins. These technologies enhance real-time visibility, predictive capabilities, collaboration, and resource optimization, enabling organizations to identify risks and respond effectively to disruptions. This study explores key resilience strategies in digitally enabled supply chains, focusing on technological enablers, organizational capabilities, and resilience dimensions including visibility, flexibility, agility, adaptability, and recovery capability. A conceptual framework is developed to examine the relationship between digital technologies and supply chain resilience. Using qualitative and quantitative assessment methods, the study evaluates resilience performance under various disruption scenarios through resilience metrics and performance indicators. The findings reveal that organizations adopting integrated digital resilience strategies achieve superior disruption management, risk mitigation, and recovery performance compared to traditional supply chain systems. The study highlights the critical role of digital technologies in strengthening supply chain resilience and provides practical insights for managers, policymakers, and researchers seeking to build sustainable and resilient supply chain ecosystems.
Suresh Babu Reddy· International Journal of Com...· 0 citations
The automotive industry is evolving rapidly, but many supply chains still operate through disconnected systems that limit visibility, delay decisions, and reduce resilience. This research presents a practical blueprint for transforming traditional supply chains into an AI-native synchronized ecosystem.
Readers will gain insights into how Multi-Agent Artificial Intelligence (MAAI), Digital Twins, Reinforcement Learning, predictive analytics, and mathematical optimization can work together to synchronize production, supplier collaboration, inventory, warehousing, yard operations, and transportation within a unified enterprise architecture.
The paper goes beyond theory by introducing a vendor-neutral reference architecture, implementation roadmap, governance framework, and technology stack that organizations can adapt to their own digital transformation journey. It also identifies key operational inefficiencies observed across global automotive OEMs and demonstrates how AI can improve visibility, resilience, planning agility, and enterprise-wide decision-making.
I look forward to engaging with researchers, supply chain professionals, and industry leaders to exchange ideas and shape the future of intelligent automotive supply chains.
P. Mishra· International Journal For Mu...· 0 citations
Rapid digitalization, geopolitical uncertainty, and climate challenges have transformed the dynamics of global logistics. This paper conducts a narrative literature review to examine how resilience in logistics is evolving into a transportation system capability. The review synthesizes recent studies across logistics, transportation, and sustainability fields to identify key technological, organizational, and policy enablers of resilience. Findings show that technological innovation including artificial intelligence, the Internet of Things, blockchain, and digital twins enhances visibility, prediction, and adaptability across multimodal networks. Automation and smart infrastructure improve operational continuity, while collaborative governance and public–private partnerships help address infrastructure and regulatory gaps. The integration of sustainability and resilience also emerges as a major priority, particularly through green mobility and climate-adaptive transport solutions. Overall, the study highlights how science and technology can strengthen the design, planning, and operation of resilient transportation systems, ensuring continuity, reliability, and sustainability in an era of uncertainty.
Nurmunirah Binti Azami, F. M. Muharam, Zulkffli Mohamad Salleh et al.· Veredas do Direito· 0 citations
The competitiveness of modern gateway ports increasingly depends on external factors beyond the port domain, such as the performance of hinterland connections. However, despite extensive research on port competitiveness, a theoretical gap persists regarding the role of road transport networks, whose efficiency and resilience remain a largely underexplored exogenous driver. This study contributes to addressing this gap by conceptualizing road-network performance through three analytical dimensions: traffic management, maintenance and infrastructure development. The study introduces a data-driven Decision Support System (DSS) architecture developed using Power Query and Power BI, thereby contributing to the existing methodological debate by illustrating how these analytical dimensions can be translated into a structured and data-driven assessment framework. The functioning of the DSS is illustrated through a structured simulation-based application using a synthetic dataset designed to reproduce realistic traffic dynamics.
The efficiency, resilience and overall quality of services provided by international supply chains focused on sea-land logistics increasingly depend on the ability to implement IT-based applications. In this perspective, several emerging digital technologies enable the development of IT-based DSS that operate through real-time data integration, offering timely and accurate information to a wide range of stakeholders.
Key findings reveal that even moderate reductions in infrastructure capacity due to roadwork sites can significantly deteriorate Levels of Service (LOS), especially during peak hours, impacting freight movements and port accessibility. The proposed DSS enables real-time monitoring, scenario comparison and predictive planning, offering concrete benefits across three strategic domains that emerge from the theoretical framing.
Despite the original contribution provided, the present study is subject to some limitations that open up relevant avenues for future research. In particular, the current application relies on a synthetic dataset designed to illustrate the operational logic of the proposed DSS rather than to provide a full empirical validation of its performance. Future studies are called to perform simulations and analysis by applying real data concerning motorway infrastructural endowment and capacity, demand patterns and associated LOS. In addition, it could be worth testing the impact of critical events, including force majeure, that might provoke sudden limitations to capacity and/or heavy traffic perturbation across the port logistics chain, in order to assess the effectiveness of potential recovery mechanisms to be implemented by business operators. In this perspective, future empirical research grounded on real-world case studies could explicitly adopt structured design-oriented methodological approaches, such as Design Science Research Methodology, to iteratively test, refine and assess DSS artefacts under operational conditions, moving beyond the illustrative and exploratory scope of the present study.
Overall, the proposed framework suggests that the adoption of IT-based DSS solutions can generate several benefits for a wide range of stakeholders involved in the management and utilization of highway infrastructures. For highway concessionaires (and public awarding authorities), the DSS serves as a powerful tool to enhance decision-making processes at multiple levels. Port authorities and logistics operators can benefit from the availability of such decision-support too. Reliable and efficient hinterland connectivity is essential for maintaining port competitiveness and expanding hinterland boundaries, and by reducing the risks of congestion-related delays, the DSS helps ensure that cargo flows can move seamlessly between ports and inland destinations. For public decision-makers and regulators, the availability of accurate data-driven information provides a solid foundation for informed policy and investment decisions. The DSS offers clear visibility into current infrastructure performance, helping to identify where new investments are most needed (such as the expansion of highway sections or the development of new connections). The benefits of the DSS extend to road users themselves, encompassing both B2B users, such as freight transport operators, and B2C users, including private vehicle drivers. For these users, improved travel conditions, reduced congestion and more predictable journey times translate into lower costs, greater reliability and an overall better travel experience. Finally, local communities and urban authorities play a key role in the broader ecosystem. The DSS contributes to mitigating the negative externalities associated with heavy port-related traffic, such as pollution, noise and urban congestion.
Hence, the study provides policy and managerial implications about the benefits of implementing IT-based DSS conceived to manage highway capacity efficiently, while offering insights to Port Authorities, regulators and road users for improving travel choices and mobility behaviors.
Roberto Viviani, F. Parola, Antonella Ferri et al.· The TQM Journal· 0 citations
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.
Smart mobility represents a transformative approach to designing and managing transportation systems by integrating information and communication technologies, artificial intelligence, sustainable engineering, urban planning, and human-centered design. Traditional transportation models, characterized by static infrastructure and reactive management, struggle to address rapid urbanization, environmental pressures, and evolving user expectations. Smart mobility enables adaptive, data-driven, and interconnected transport ecosystems through real-time system awareness and predictive control. This multidisciplinary study examines smart mobility solutions at the intersection of technological, social, environmental, and economic dimensions. It integrates insights from intelligent transportation systems (ITS), IoT-based sensing, machine learning-driven decision systems, and sustainable mobility engineering. A conceptual framework is proposed for evaluating smart mobility architectures using distributed computing environments such as edge intelligence and cloud coordination. Key performance indicators (KPIs) are developed to measure congestion reduction, emission control, safety improvement, and user satisfaction. Simulation results indicate that learning-enabled mobility systems outperform traditional models in responsiveness, scalability, and sustainability. However, challenges related to interoperability, data governance, cybersecurity, and equitable access remain. Future research directions include federated learning, cross-layer optimization, and socio-technical policy integration.
Donna Coleman· International Journal of Eme...· 0 citations
