Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The national transportation systems are increasingly faced with old infrastructure, climate-induced stressors, and increasing demands that are leading to untimely failures, safety concerns, and rising costs. Pervasive sensing, connectivity, and computation have been developed into smart systems that can be central to resolving these issues, but the effectiveness of their incorporation into architectures and decision processes depends on successful integration. This review synthesizes literature in sensing ecosystems, data integration and fusion, analytics typologies, and integration architectures, and identifies applications in asset health, traffic safety, network reliability, and resilience within national transportation networks, with a focus on the U.S. It considers the role of predictive and prescriptive analytics in maintaining risks, proactive safety, and adaptive traffic control, and how such tools can be used to measure economic effects, such as cost-benefit analysis and life-cycle analysis. The article cites some primary technical, organizational, and societal issues, such as data heterogeneity, model robustness, workforce capacity, governance fragmentation, privacy, and cybersecurity, that limit scalable deployment. Based on these insights, it presents future research and implementation priorities associated with advanced analytics and artificial intelligence; digital twins; uncertainty-aware decision frameworks; and standardized cost-efficiency analysis. The results highlight the potential and challenge of bringing data analytics to smart systems and emphasize the necessity of concerted research, practice, and policy actions to achieve long-term gains in safety, reliability, resilience, and economic performance. Transportation agencies can leverage integrated analytics to prioritize maintenance, enhance safety interventions, and allocate resources more efficiently for long term, cost effective network performance.