St6te of the 6rt. Historically, the nature of urban streets and their land uses are in correlation with the volume of the street's traffic flows. These two parameters are interdependent and together with other urban factors (e.g., social, cultural, etc.) create urban variety and dynamics that make the city a complex system (Portugali 2000, 2012, Batty, 2007). Understanding these interdependencies, may shed light on different urban phenomena and could be developed into new, real-time, decision-making tools that can be useful for planners under a new understanding that planning can no longer address long-term plans only, but should keep up with new phenomena and address the urban area as the complex, self-organizing system it is. We present a new framework, based on complex networks tools and in particular percolation theory. This interdisciplinary complex network field addresses, among other things, spatially embedded transportation networks that control many aspects of modern urban life and thus affect problems such as traffic jams, urban sprawl, epidemiology, etc. (Xxxxxxxxxx, 2011; Xxxxxxxxx and Xxxxxxxxxx, 2020). However, in these studies, most of the work focused on the network topology where the nodes and the links between them are fixed in time and set top-down (for a comprehensive review on this see Xxxxxxxxxx, 2011). Some studies on traffic networks defined the networks using fixed nodes with dynamics links (i.e. the links between the nodes vary, and are usually formed due to bottom-up forces) see for example Chowell et al. (2003) or Austwick et al. (2013). The influence of the street-networks topology on traffic volumes has also been studied extensively (Goh et al., 2001; Xxxxxx and Xxxxxx, 2002; Xxxxxx-Xxxxxx and Xxxxxxxxx, 2010; Xxxxxx-Xxxxxx et al., 2012; Xxxxxxx, 1977; Guimerà et al., 2005; Borgatti, 2006; Xxxxxxx et al., 2009; Xxxxx et al., 2012). Yet, here also, most of the work on that subject overlooked the dynamic of the traffic flow and the influence it has on the overall urban system. Recent work introduced an innovative approach that employs percolation processes to identify urban clusters (Xx et al. 2015, Xxxxxxx et al., 2016) and to identify links that act as significant and repetitive temporal bottlenecks in urban traffic networks (Xx et al. 2015). They defined the percolation threshold based on the maximal size of the second-largest component and then examined the resulted clusters and focused on the links that were identified as bottlenecks. This m...
