Machine learning, as a subset of the tools of artificial intelligence, is a broad area of computer science that provides machines and autonomous systems with the ability to learn and improve on previous experience. In other words, machine learning focuses on the development of computer programs that can autonomously access data and use it for learning and self-improvement. This core objective of machine learning can be achieved through many types of learning, including supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, and association rules, among others. Various models that implement these machine learning types have been used and researched for machine learning systems. The best models are known to include architectures such as artificial neural networks, support vector machines, decision trees, evolutionary algorithms, Petri nets, and regression algorithms. It seems that machine learning is related to, and overlaps with, other fields of information theory, e.g., optimisation, statistics, and data mining.

The issues in transportation are becoming a challenge due to the increased globalisation of the economy, population growth, development of industrial production, safety concerns, and environmental degradation. Traditional transportation systems are pushed to the limit, and due to the ever-increasing traffic volume, urban development often reaches a bottleneck. With the current availability of computational power and massive progress in the theory of artificial intelligence, machine learning models seem to be a suitable tool to overcome the previously mentioned challenges. In some cases, such as logistic service planning, event and object detection from surveillance, video, or analysis of traveller’s behaviour, machine learning-based solutions have already proved themselves to have higher performance compared to conventional solutions. Other possible applications are still the object of research and development.

This Special Issue is focused on the reporting of new and innovative applications of machine learning methods to solve transportation problems. Submissions, which provide reliable, robust, and verified approaches, are especially welcome. Among others, researchers and academics, who intensely cooperate with industrial and transportation companies, are encouraged to publish their contributions. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Traffic analysis and control, including monitoring, public transportation management, etc.
• Decision support for intelligent transportation systems
• Modelling of aspects of transportation systems including user behaviour, travel demand, and infrastructure usage
• Applications of modelling to traffic analysis, control, and optimisation
• Smart city logistics