Urb6fi Fufictiofi6l Clusters Sample Clauses

Urb6fi Fufictiofi6l Clusters. In the first stage, we transform the road network into a topological network where for each road, eij, the velocity xxx(t) varies during a day according to time- dependent traffic operations. We use speed rather than flow as this data can be easily extracted from the API of different navigation apps and thus, this method can be easily implemented on such datasets. For each road eij, we set the 95th percentile of its measured velocity as its limited maximal velocity and define rij(t) as the ratio between its current velocity and its limited 95% percentile measured velocity. For a given threshold q, the road eij can be classified into either functional when rij > q or dysfunctional for rij < q. In other words, whether a road is functional depends on the extent in which traffic can drive at its free or maximum speed. Note that for q = 0, we have the situation where the traffic network is the same as the original road network and q = 1 represents and situation where it becomes (generally) completely fragmented. For a certain value of q, the hierarchical organization of traffic in different scales emerges, where only clusters of connected roads with rij higher than q appear. These clusters represent functional modules composed of connected roads with speed values higher than q. As q increases, the size of the giant component decreases, and the second-largest cluster reaches a maximum at the critical threshold (qc) separating the fragmented phase from the connected phase of the traffic network. For example, the giant cluster exists for q=0.19 (see figure 1A). As the value of q increases, this giant cluster is fragmented into smaller clusters and for q = 0.69, only small clusters of connected roads with high velocity emerge. These cannot maintain the global network traffic (figure 1C). For q = 0.38 (Fig. 1B), the size of the second-largest cluster is in its maximal size, which indicates the phase transition point for network connectivity of a functional traffic network (according to percolation theory).