Widely deployed traffic monitoring facilities generate tons of traffic data every day to make sure traffic is safe. Various data sources are accessible for different transportation manners (e.g., roadway traffic, maritime transportation, railway, etc.). Video surveillance data, global positioning system (GPS), automatic identification system (AIS), floating car data, Wi-Fi probe data, inductive loop detector data, and simulation data are data sources that can be analyzed. Large-scale multiple-source traffic data provide us important information to analyze traffic safety and prevent potential traffic accidents.

It is not an easy task to fully exploit historical spatial-temporal information from multiple traffic data sources. We can fail to identify the traffic flow variation tendency from noisy loop detector data. Moreover, the collected GPS data can be quite inaccurate when the vehicles drive under strong interference conditions (e.g., port area, mountainous regions). As such, we may not easily identify the main factors leading to traffic accidents with a single type of noise traffic data.

The aim of this Special Issue is to attract both original research articles and review articles highlighting the implementation of traffic safety analysis and prevention through multiple data sources and artificial intelligence techniques. Submissions considering the aspects of traffic flow modelling, traffic safety analysis, traffic control and traffic simulation are welcome.

Potential topics include but are not limited to the following:

• Video data (maritime surveillance video, unmanned aerial vehicle video, etc.)
• Supported traffic safety analysis
• Traffic flow modelling and analysis via multiple traffic data (e.g., inductive loop data, floating car probe data)
• Deep learning aided traffic safety analysis
• High-resolution traffic data collection via artificial intelligence techniques
• Traffic simulation data supported traffic safety analysis
• Intelligent traffic collision avoidance