Transportation systems are complex, diverse, and dynamic in nature and operation. Researchers and practitioners have recently been faced with difficulties in obtaining comprehensive and current data needed to tackle rapidly emerging challenges, such as congestion of infrastructures, safety problems, environmental impacts, energy dependency, and social equity. With the rapid digitization and implementation of sensors in transport systems (e.g., personal devices, vehicles, infrastructures—including streets and sidewalks at the urban scale), there is a substantial wealth of data related to transport complex phenomena. Due to their advanced computation and data collection processes, machine learning is a fast and powerful tool that breaks down such complex problems into more straightforward and manageable mathematical operations. Researchers have developed machine learning methods to approach more traditional and novel transportation research problems with varying levels of success.

Machine learning encompasses many methodologies (e.g., supervised learning, unsupervised learning, reinforcement learning, and self-supervised learning, among others) and models (e.g., deep learning, support vector machines, decision trees, and evolutionary algorithms, among others) to explore new data sources and applications. Besides their improved performance compared to more conventional methods, machine learning could evolve to support planning and policy making in the transport field and, therefore, achieve more interpretable models and results (i.e., explainable artificial intelligence).

This Special Issue aims to collect and report new and innovative applications of machine learning methods to solve challenges presented by transportation systems. The scope of the research is diverse; topics of interest include, but are not limited to, the application of machine learning in various transportation fields and the following topics:

– Safety of transport infrastructures, particularly road users and vulnerable road users (pedestrians, cyclists, and scooter users);

– Monitoring, operation control, and management of mobility services, including shared-mobility services, public transportation management, Mobility-as-a-Service (MaaS), etc.;

– Intelligent transportation systems;

– Smart city logistics and micro-logistics;

– Management of public space management at the urban scale, including the intermittent and dynamic usage of road carriageways;

– Case studies in which machine learning was effectively used to make transportation systems more effective;

– Comparison of different approaches of machine learning methods with conventional approaches;

We welcome both original research and review articles. All submissions will be peer-reviewed according to the high standards of the journal.

Dr. Filipe Moura
Dr. Manuel Marques
Guest Editors