Exploratory Visual Analytics for Winter Road Management Using Statistically Preprocessed Probe-Car Data

Abstract

Social CPSs (Cyber-Physical Systems) denote the extended application of the idea of CPSs to the monitoring and control of urban-scale social infrastructure systems. They utilize both cyber data stored in databases and physical data coming from sensor networks in the target physical world for the analysis and optimized control of urban infrastructure systems such as traffic, energy, and water services. This paper focuses on the winter road management in Sapporo where we have the world biggest annual snow fall among the cities with more than 1 million populations. For monitoring the road conditions over the whole city, the use of probe-car data without violating personal data protection is fundamental. This paper first shows that probe car data statistically preprocessed over road links for an urban-scale area still allow us to visualize the dynamic change of the traffic flow in terms of the divergence and flow vector field. These give us sufficient information about the dynamic change of hotspots of traffic, main traffic streams, and route selection preference. The paper also shows more complex and advanced analyses of such data, especially for better winter road management in Sapporo. We extend the well-known multiple coordinated views framework for exploratory visual analytics to multiple coordinated views and analyses by integrating analysis tools with their result visualization views into the same environment. These newly added views may also coordinate with others, and allow users to directly select clusters or mined patterns calculated at runtime to further quantify the underlying database view. Exploratory visual analytics with such an environment enables us to detect road links for effective pinpoint snow removal.