Data intelligence, along with artificial intelligence (AI), has 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 analyze experimental traffic studies using conventional methodologies. This leads to data intelligence becoming a critical skill for analyzing complex mobility. New forms of transportation modes such as shared mobility, micro-mobility, non-motorized transport, sensor-embedded vehicles, and semi/fully connected or automated vehicles have entered conventional transport networks. They produce vast amounts of data every second. Vehicles, but also infrastructure on road networks, are producing large amounts of data through numerous sensors. The 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 current traffic states. They can tell us what happens in urban contexts before, during, and after unexpected events. Furthermore, it helps the industry enhance safety, reduce unforeseen accidents, reduce traffic and 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 the extensibility of data applications 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
• Data-driven prediction and control in transportation and mobility systems
• Data manipulation/imputation
• Metadata management
• Data cataloguing
• Data visualization
• Data fusion from multi-sensors
• Pre-processing methodology for innovative mobility data
• Surveys on data intelligence in transport
• Data security and cybersecurity for AI in autonomous vehicles and intelligent transportation systems
• AI ethics and data privacy in transportation
• Applications of generative models in transportation and mobility data