Traffic 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.
|Number of pages||15|
|Journal||Environment and Planning B: Urban Analytics and City Science|
|State||Published - 1 Sep 2019|
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the PMO Foundation for Innovations in Transportation.
© The Author(s) 2019.
- Network theory
- Time–space analysis
- big data
- traffic analysis
- urban design