Current Challenges and Issues in Car Traffic Forecasting

Many methods have been proposed for predicting vehicular traffic, using various approaches that are more often dependent on the available data type. In addition, many review papers have addressed the solutions available in this area. However, there has been no collective summary of the challenges and unresolved problems that are indicated as particularly important and which, in the authors’ opinion, are worth addressing in the near future. The aim of this work is to fill this gap. In this paper, we analyze these challenges and identify potential directions for developing more efficient, scalable, and comprehensive traffic prediction methodologies. In addition to an in-depth discussion covering the most important challenges, issues, and available datasets, we also formally analyzed the relationship between the content of individual articles using a keyword linkage graph, which is a valuable tool to visualize key aspects of the discussed topic. The analysis covers 76 survey papers published between 2012 and 2025 and quantitatively identifies the most frequently cited challenges and future research directions.