Visual interface for exploring caution spots from vehicle recorder big data

It is vital for the transportation industry, which performs most of their work by automobiles, to reduce its number of traffic accidents. Many local governments in Japan have made potential risk maps of traffic accident spots. However, making such maps in wide areas and with the time information had been difficult because most of them are made based on an investigation. Utilizing long-term driving records can extract wide area spatio-temporal caution spots. This paper proposes a visual interaction method for exploring caution spots from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations using various combinations of attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution spots. We demonstrate the usefulness of our novel visual exploration environment using real data given by one of the biggest transportation companies in Japan. Exploration results show our environments can extract caution spots where some accidents have actually occurred or that are on very narrow roads with bad visibility.