Acquiring Diverse Knowledge for Predictive Academic Excellence in the Field of Transportation

Research is being conducted to examine whether gathering information may help students achieve extraordinary academic outcomes in the transportation business. Three major components of a comprehensive solution will be presented through a revolutionary technique that integrates previously different approaches: knowledge graph construction (KGC), knowledge embedding (KE), and predictive modeling (PM). During the KGC stage, data is gathered from many sources and arranged into a knowledge graph. However, when the KE stage is reached, this data is stored in vector spaces. These embeddings are used in transportation research for PM tasks that need prediction. The suggested technique is superior at capturing the intricate links seen in transportation data as well as providing a more comprehensive predictive modeling tool. Traditional approaches, while perhaps easier to use, are not as capable of dealing with complicated patterns and a diverse set of data sources. This work proposes a unique research approach based on knowledge representation and embedding, which greatly contributes to the advancement of progressive scholarship in the field of transportation studies. This illustrates the need for the sector to adopt creative tactics to meet everchanging difficulties.