Systematic Analysis of the Literature Addressing the Use of Machine Learning Techniques in Transportation—A Methodology and Its Application

Abstract

Advances in the field of machine learning (ML) have been reflected in the intensity of research studies exploiting these techniques for a better understanding of existing phenomena, and for predicting future ones, as a mean for promoting a more efficient and sustainable transportation system. The present study aims to understand the trends of utilizing diverse ML approaches to tackle issues within sub-domains of transportation and to identify underutilized potentials among them. This paper presents a methodology for the bi-dimensional classification of a large corpus of scientific articles. The articles are classified into six transport-related sub-domains, based on the definition of the Israeli Smart Transport Research Center, whose aim is a transportation system with zero externalities, and the ML techniques used in each of them is identified. A fuzzy KNN model is implemented for the multi-classification of articles into the transportation sub-domains and an ontology-based reasoning for identifying the share of each applied ML approach is employed. The application of these methodologies to a corpus of 1718 articles revealed, among other findings, an increasing share of artificial neural networks and deep learning techniques from 2018 until 2022, particularly in the traffic management sub-domain. A significant contribution of the development of these automatic methodologies is the ability to reuse them for ongoing exploration of trends regarding the use of ML techniques for transportation sub-domains.