  Clustering of urban traffic patterns is an essential task in many different
areas of traffic management and planning. In this paper, two significant
applications in the clustering of urban traffic patterns are described. The
first application estimates the missing speed values using the speed of road
segments with similar traffic patterns to colorify map tiles. The second one is
the estimation of essential road segments for generating addresses for a local
point on the map, using the similarity patterns of different road segments. The
speed time series extracts the traffic pattern in different road segments. In
this paper, we proposed the time series clustering algorithm based on K-Means
and Dynamic Time Warping. The case study of our proposed algorithm is based on
the Snapp application's driver speed time series data. The results of the two
applications illustrate that the proposed method can extract similar urban
traffic patterns.
