Abstract

Abstract Sapporo is a city with two million citizens that gets six meters of snow per year. This means that winter road management is very important for sustaining economic and social activities during the winter. We believe that an exploratory and iterative analysis and visualization approach is useful to support the decision mak-ing, to improve the winter road management strategies. We propose using a huge library of tools and services, and a framework that allows users to freely federate tools and services improvisationally (“mash-up”) to create custom visualization and analysis environments and to apply these on appropriately selected data sets. Un-like conventional macro analysis approaches, we focus on micro analysis of winter road conditions. We use probe car data, speed readings etc., automatically collected from taxis and private cars. Geospatial visualization of the average speeds of all the road segments shows how different roads are affected by heavy snowfall, by snow plowing, and by snow removal. Combining geospatial visualization with knowl-edge discovery algorithms is a potential approach in this area. An example would be clustering the road segments based on similarity of the impact snowfall has to group roads into groups that can be maintained using similar strategies.