Massive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.

This study investigates the use of augmented reality technology (AR) in the field of maritime navigation and how researchers and designers have addressed AR data visualisation. The paper presents a systematic review analysing the publication type, the AR device, which information elements are visualised and how, the validation method and technological readiness. Eleven AR maritime solutions identified from scientific papers are studied and discussed in relation to previous navigation tools. It is found that primitive information such as course, compass degrees, boat speed and geographic coordinates continue to be fundamental information to be represented even with AR maritime solutions.