Nowadays, the deployment of sensing technology permits to collect massive spatio-temporal data in urban cities. These data can provide comprehensive traffic state conditions for an urban network and for a particular day. However, they are often too numerous and too detailed to be of direct use, particularly for applications like delivery tour planning, trip advisors and dynamic route guidance. A rough estimation of travel times and their variability may be sufficient if the information is available at the full city scale. The concept of spatio-temporal speed cluster map is a promising avenue for these applications.