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100			_aSerok, Nimrod _946002
245			_aUnveiling the inter-relations between the urban streets network and its dynamic traffic flows: Planning implication
260			_bSage, _c2019.
300			_aVol 46, Issue 7, 2019(1362-1376 p.)
520			_aTraffic flows have always been a major element affecting the nature of urban streets. Traffic flows influence the location of businesses, residences, and the development of real estate, land values, and built-density. In this study, we suggest that revealing the relations between the static street network and dynamic traffic flows may provide meaningful and useful insights that could be applied in planning processes. Thus, the objective of this work is to unveil the inter-relations between the dynamics of traffic flows and urban street networks in different areas of a city and between cities. We use network percolation analysis (i.e., removal of links with a speed value lower than a pre-defined threshold) to develop an innovative method to identify functional spatio-temporal street clusters that represent fluent traffic flow. We employed our method on two data sets of London and Tel Aviv centers and analyzed the dynamics of these clusters, based on their size (in terms of street length) and their spatial stability over time. Our findings revealed both the differences between the two cities as well as differences and similarities between different areas within each city. Thus, our method can be used to develop new, real-time, decision-making tools for urban and transportation planners. Today, new technologies provide big data on urban traffic flow, which can be used in developing new, adaptive tools for planning. However, urban and transportation planning are currently being challenged by real-time navigation apps that aim to find the fastest routes for their users. To be able to intervene and affect urban life quality, planners should adopt new tools that are based on real-time, short-term approaches. These will bridge the gap between static long-term urban planning and the flexible and dynamic urban rhythm, and will enable planners to keep their role in the formation of better cities.
650			_aTime–space analysis, _946003
650			_a traffic analysis, _946004
650			_aurban design, _946005
650			_abig data, _942128
650			_a Network theory _946006
700			_a Levy, Orr _946007
700			_aHavlin, Shlomo _946008
700			_aBlumenfeld-Lieberthal, Efrat _946009
773	0		_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science
856			_uhttps://doi.org/10.1177/2399808319837982
942			_2ddc _cART