Is there more traffic congestion in larger cities? -Scaling analysis of the 101 largest U.S. urban centers-

Chang, Yu Sang; Lee, Yong Joo; Choi, Sung S.

doi:10.1016/j.tranpol.2017.07.002

Cited by 64 publications

(33 citation statements)

References 14 publications

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“…(Bottom) Scaling exponent β(t) for the delay computed for each year separately, from 1982 to 2014. All these values are consistent with a superlinear behavior found in[57]. Figure taken from[56].…”

supporting

confidence: 88%

Modeling cities

Barthélemy

2019

Comptes Rendus Physique

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Cities are systems with a large number of constituents and agents interacting with each other and can be considered as emblematic of complex systems. Modeling these systems is a real challenge and triggered the interest of many disciplines such as quantitative geography, spatial economics, geomatics and urbanism, and more recently physics. (Statistical) Physics plays a major role by bringing tools and concepts able to bridge theory and empirical results, and we will illustrate this on some fundamental aspects of cities: the growth of their surface area and their population, their spatial organization, and the spatial distribution of activities. We will present state-of-the-art results and models but also open problems for which we still have a partial understanding and where physics approaches could be particularly helpful. We will end this short review with a discussion about the possibility of constructing a science of cities.

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supporting

confidence: 88%

Modeling cities

Barthélemy

2019

Comptes Rendus Physique

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“…If we take the population instead of the number of car commuters, our results are qualitatively the same and our conclusions remain unchanged, even if all the exponent values change slightly (a fit for all cities and all years shows that the number of car commuters is approximately a constant fraction of order 35% of the population). In [21], they used the least square method to estimate β and for the year 2014 (the last available year in the urban mobility report), we find with this method β = 1.23 ± 0.03. We plot the data and the corresponding fit on Fig.…”

Section: Aggregating All Cities: Global Scalingmentioning

confidence: 95%

“…Here, we study the dataset (freely available at [19]) published by the Texas A&M Transportation Institute (TTI) in the Urban Mobility Report (UMR), obtained for 101 cities in the United States over 33 years from 1982 to 2014 (the methodology used for constructing this dataset is described in [20], and we also give more details about this dataset in the SI). This database has been investigated in 2017 by [21] and in this study, the authors agglomerate all the data corresponding to different cities and performed the usual power law fit of the form…”

Section: Aggregating All Cities: Global Scalingmentioning

confidence: 99%

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From global scaling to the dynamics of individual cities

Depersin

Barthélemy

2018

Proc. Natl. Acad. Sci. U.S.A.

106

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Scaling has been proposed as a powerful tool to analyze the properties of complex systems and in particular for cities where it describes how various properties change with population. The empirical study of scaling on a wide range of urban datasets displays apparent nonlinear behaviors whose statistical validity and meaning were recently the focus of many debates. We discuss here another aspect, which is the implication of such scaling forms on individual cities and how they can be used for predicting the behavior of a city when its population changes. We illustrate this discussion in the case of delay due to traffic congestion with a dataset of 101 US cities in the years 1982-2014. We show that the scaling form obtained by agglomerating all of the available data for different cities and for different years does display a nonlinear behavior, but which appears to be unrelated to the dynamics of individual cities when their population grows. In other words, the congestion-induced delay in a given city does not depend on its population only, but also on its previous history. This strong path dependency prohibits the existence of a simple scaling form valid for all cities and shows that we cannot always agglomerate the data for many different systems. More generally, these results also challenge the use of transversal data for understanding longitudinal series for cities.

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“…This fact assigns cities a central role in pursuing solutions and mitigation strategies for the global climate change problem. Because of that, researchers from several disciplines have investigated the effects of urbanization on CO 2 emissions [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] . The question of whether urbanization promotes or mitigates climate change is ubiquitous among these works, and the approaches to probe such issues differ, but can be roughly organized into two groups.…”

Section: Introductionmentioning

confidence: 99%

Effects of changing population or density on urban carbon dioxide emissions

2019

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The question of whether urbanization contributes to increasing carbon dioxide emissions has been mainly investigated via scaling relationships with population or population density. However, these approaches overlook the correlations between population and area, and ignore possible interactions between these quantities. Here, we propose a generalized framework that simultaneously considers the effects of population and area along with possible interactions between these urban metrics. Our results significantly improve the description of emissions and reveal the coupled role between population and density on emissions. These models show that variations in emissions associated with proportionate changes in population or density may not only depend on the magnitude of these changes but also on the initial values of these quantities. For US areas, the larger the city, the higher is the impact of changing its population or density on its emissions; but population changes always have a greater effect on emissions than population density.

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Is there more traffic congestion in larger cities? -Scaling analysis of the 101 largest U.S. urban centers-

Cited by 64 publications

References 14 publications

Modeling cities

Modeling cities

From global scaling to the dynamics of individual cities

Effects of changing population or density on urban carbon dioxide emissions

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