Subject Areas and Methods of Spatiotemporal Optimization in Transport: A Systematic Literature Review

The accumulation of big data by intelligent and information systems in transportation has created a basis for the use of various methods of forecasting and operational management of the transportation process. One of the promising directions in this area is the description of both traffic flows and the structure of transportation systems in the form of spatiotemporal graphs. 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 transport process. The purpose of the study is to identify trends in the use of spatiotemporal 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 Scopus Database for 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. It was found that a promising direction is the development of deep learning methods and ways to form graph neural networks based on the use of spatiotemporal graph. Such methods are most often used to solve the tasks of real-time control of urban transportation systems. The least number of publications is in areas that require in-depth knowledge of transportation technology, such as air, sea, and rail transportation. This study has contributed to the expansion of scientific knowledge on methods of spatiotemporal optimization of transport systems based on bibliometric analysis.