A method of predicting a traffic behaviour in a road system, comprising the following steps carried out by at least one processor (9, 11, 14, 17) connected to a database (7, 12, 16), which contains a set of traffic profiles, each traffic profile representing a traffic measure of vehicles in the road system recorded over time (t), one or more pre-defined tags associated with a time of recording of the respective traffic profile being attributed to said traffic profile: clustering the traffic profiles with their tags into at least two clusters (CL i) as to similarity of traffic profiles; assigning at least one characteristic vector (CV j) to each cluster (CL i) based on the tags in this cluster (CL i), wherein each characteristic vector (CV j) contains a tag or a combination of tags of said cluster (CL i); determining which cluster (CL i) comprises the most traffic profiles and setting this cluster (CL i) as a reference cluster; for each cluster (CL i), determining a dissimilarity measure of this cluster (CL i) with respect to the reference cluster and assigning the dissimilarity measure to all characteristic vectors (CV j) of this cluster (CL i), wherein the dissimilarity measure is a measure of how much the traffic profiles of a cluster (CL i) differ from the traffic profiles of the reference cluster; receiving a request (Req) having one or more request tags; checking whether all request tags match one of the characteristic vectors (CV j) of the clusters (CL i), and if so, outputting a predicted traffic behaviour based on the cluster (CL i) with said matching characteristic vector (CV j), else, eliminating one of the request tags from the request (Req) and returning to the beginning of said step of checking; wherein the request tag to be eliminated is chosen by determining which of the characteristic vectors (CV j) corresponds to each request tag and choosing the request tag whose corresponding characteristic vector (CV j) has the lowest dissimilarity measure. clustering the traffic profiles with their tags into at least two clusters (CL i) as to similarity of traffic profiles; assigning at least one characteristic vector (CV j) to each cluster (CL i) based on the tags in this cluster (CL i), wherein each characteristic vector (CV j) contains a tag or a combination of tags of said cluster (CL i); determining which cluster (CL i) comprises the most traffic profiles and setting this cluster (CL i) as a reference cluster; for each cluster (CL i), determining a dissimilarity measure of this cluster (CL i) with respect to the reference cluster and assigning the dissimilarity measure to all characteristic vectors (CV j) of this cluster (CL i), wherein the dissimilarity measure is a measure of how much the traffic profiles of a cluster (CL i) differ from the traffic profiles of the reference cluster; receiving a request (Req) having one or more request tags; checking whether all request tags match one of the characteristic vectors (CV j) of the clusters (CL i), and if so, outputting a predicted traffic behaviour based on the cluster (CL i) with said matching characteristic vector (CV j), else, eliminating one of the request tags from the request (Req) and returning to the beginning of said step of checking; if so, outputting a predicted traffic behaviour based on the cluster (CL i) with said matching characteristic vector (CV j), else, eliminating one of the request tags from the request (Req) and returning to the beginning of said step of checking; wherein the request tag to be eliminated is chosen by determining which of the characteristic vectors (CV j) corresponds to each request tag and choosing the request tag whose corresponding characteristic vector (CV j) has the lowest dissimilarity measure.