Urban transportation has become a global challenge that plagues all global metropolises. Visual analytics techniques have recently become an important intelligent transportation technology that is crucial to the analysis and utilization of big transportation data. This study attempts to review the state-of-the-art of visual analytics techniques in urban transportation data comprehensively. These techniques have been developed since the proliferation of information visualization and visual analytics. The discussion mainly focuses on two broad aspects: road traffic analysis problems and other intelligent transportation-related problems. A detailed presentation of the visualization techniques and visual analytics systems is organized according to the transportation data type and transportation problem category. Recent research trends are succinctly summarized. Many early studies have focused on designing techniques

such as arrow graph

mosaic map

and traffic wall

to visualize road traffic. With these techniques

current research on road traffic analysis has placed emphasis on traffic events. However

the definition of traffic events remains limited to traffic congestion. Other application areas of visual analytics in the intelligent transportation domain include public transportation

traffic accidents

and human mobility. In recent years

a new research trend of mining and utilizing the social dimensions or social contextual information of vehicle trajectories or traffic events has emerged. From traffic flow visualization to visual analysis traffic incidents

from analyzing road traffic status to other urban transportation-related social problems

from analyzing single-source transportation data to multi-source data that are rich in social semantics

and from the traditional interactive visualization paradigm at the PC end to novel media and devices for visual presentation

the depth and breadth of research on transportation data visualization have significantly broadened. The research trend of this domain is also evident.