The transportation sector constitutes a fundamental pillar in fostering economic development and maintaining societal functionality through its pivotal role in ensuring connectivity, facilitating resource distribution, and enhancing quality of life. However, the increasing complexity of transportation networks, coupled with accelerating globalization, has intensified the challenges associated with human factor safety risks, encompassing operator fatigue, procedural errors, and external vulnerabilities such as adverse meteorological conditions and cybersecurity threats. Contemporary safety management paradigms, predicated on static analytical frameworks, prove insufficient in addressing these dynamic and multifaceted risk factors. The emergence of advanced technologies, including big data analytics, artificial intelligence, and computational behavioral science, enables the development of predictive risk assessment methodologies through real-time data acquisition and behavioral pattern analysis. Through the integration of multidimensional monitoring systems, systematic risk identification protocols, and advanced decision support frameworks, a comprehensive human factor safety management architecture founded on behavioral modeling technologies can substantially enhance the safety parameters and systematic resilience of transportation infrastructure networks.