Spatio-Temporal Graphs in Transportation: Challenges, Optimization, and Prospects

Intelligent and information systems in transportation record and accumulate large volumes of raw data on dynamic transportation processes. However, these data are not fully utilized for forecasting, real-time planning, and transportation management. Spatio-temporal graphs allow describing simultaneously both the structure of transportation systems of different modes of transportation and the dynamics of transportation flows. Optimization of such graphs makes it possible to justify management decisions in real time, as well as to forecast the parameters of traffic flows and transportation processes. The purpose of the study is to identify trends in the use of spatio-temporal graphs for solving various problems in transportation, as well as the most common methods of optimization of such graphs. The sample papers studied include 114 publications from the Scopus database over 25 years, from 1999 to 2024. First, a bibliometric analysis was conducted to establish the increase in the number of publications, journals, countries, institutions, subject areas, articles, authors, and keyword matches, to understand the amount of literature generated. Secondly, a literature review was conducted based on content analysis to predict future research directions in the field. We have found that the development of deep learning methods and approaches for designing graph neural networks based on spatio-temporal graphs is a promising direction. Such methods are mostly used to solve the tasks of real-time control of urban transportation systems. There are fewer publications in areas that require in-depth knowledge of transportation technology, such as air, sea, and rail transportation. This study contributes to the expansion of scientific knowledge about methods of spatio-temporal optimization of transport systems based on bibliometric analysis.