Bibliometric Analysis as a Means of Efficiently Assessing Trends in Artificial Intelligence

Transportation planners are increasingly relying on AI to optimize logistics and solve persistent challenges. However, as AI advances rapidly, most software vendors are unable to evaluate and implement all new developments. This paper uses bibliometric methods to track and evaluate the emerging trend of neurosymbolic AI, which combines neural networks with symbolic AI to improve decision making. By analyzing literature and citation data, we gain insights into the development and impact of neurosymbolic AI. The results provide a scalable approach for practitioners to efficiently identify and evaluate AI trends to facilitate the strategic adoption of technologies and innovations in transportation planning.