Visual analytics for movement behavior in traffic and transportation

Abstract

Understanding movement of vehicles, people, goods, or any type of object is important for making knowledgeable decisions regarding public transportation planning. However, movement is a complex and dynamic phenomenon, and until recently, movement data was difficult to exploit for such planning purposes. The widespread adoption of location-aware devices such as Global Positioning System (GPS) sensors in public transportation systems and the adoption of open data principles have set the stage for new methods and tools for data collection and analysis of movement patterns. This paper illustrates the value and benefit of applying visual analytics techniques to movement data to create valuable insight for public transportation planning using vehicle-mounted devices on buses and trams. The contribution of the paper is three distinct visual analytics solutions that we developed using a real-world open data feed published by the Helsinki Public Transport Authority. The current work addresses encounters between objects, stops that interrupt movement, and flow dynamics of a large number of moving objects. We instantiated the described methods by showing that our findings can be applied in real-world use cases.