Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

Abstract

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.