Data intelligence, along with artificial intelligence (AI), have been paid much attention in the fields of traffic engineering and transport planning. These disciplines allow transport experts to plan, design, and operate transportation systems more efficiently and intuitively. It has been proven that data intelligence has changed the transportation sector tremendously. AI technology makes our lives easier and helps all human transportation systems become safer and more efficient.

Since the road network consists of so many elements interacting dynamically, it becomes complicated to analyse the experimental traffic studies using conventional methodologies. This leads to data intelligence becoming a critical skill for analysing complex mobility. New forms of transportation modes such as shared mobility, micro-mobility, non-motorized transport, sensor-embedded vehicles, semi/fully connected or automated vehicles have entered a conventional transport network. They pour out a vast amount of data every second. Vehicles but also infrastructure on road networks are producing large amounts of data through numerous sensors. Combination of AI and machine learning with cloud-based storage, featuring large sizes and speeds, is becoming more efficient every day. It allows us to understand more accurately the current traffic states. Furthermore, it helps the industry enhance safety, reduce unforeseen accidents, reduce traffic, carbon emissions, and lower overall financial costs.

The aim of this Special Issue is to collate original research addressing challenges in contemporary data pre- and post-processing, data management, data fusion, data-driven AI applications, and extensibility of data application in the transportation domain. This Special Issue also encourages submissions from practitioners and academics working in research fields related to data intelligence issues. Published articles must have clear relevance to data intelligence issues. Review articles discussing the state of the art of data intelligence are also welcome.

Potential topics include but are not limited to the following:

• Big data framework for better decision-making
• Traffic prediction and control through data
• Data manipulation/imputation
• Data-driven machine learning
• Metadata management
• Data cataloguing
• Data visualisation
• Data fusion from multisensors
• Innovative mobility data pre-processing methodology
• Surveys in data intelligence of transport