The statistical physics of cities

Barthélemy, Marc

doi:10.1038/s42254-019-0054-2

Cited by 103 publications

(85 citation statements)

References 97 publications

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“…Next we show the effect of the screening parameter B appearing in the social force model given in Eq. (2). Precisely the order parameter Φ versus the angle ϕ is shown in Fig.…”

Section: S4 Supplemental Resultsmentioning

confidence: 99%

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Membranes for spontaneous separation of pedestrian counterflows

Koyama¹,

Inoue²,

Okada³

et al. 2020

EPL

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Designing efficient traffic lanes for pedestrians is a critical aspect of urban planning as walking remains the most common form of mobility among the increasingly diverse methods of transportation. Herein, we investigate pedestrian counter flows in a straight corridor, in which two groups of people are walking in opposite directions. We demonstrate, using a molecular dynamics approach applying the social force model, that a simple array of obstacles improves flow rates by producing flow separations even in crowded situations. We also report on a developed model describing the separation behavior that regards an array of obstacles as a membrane and induces spontaneous separation of pedestrians groups. When appropriately designed, those obstacles are fully capable of controlling the filtering direction so that pedestrians tend to keep moving to their left (or right) spontaneously. These results have the potential to provide useful guidelines for industrial designs aimed at improving ubiquitous human mobility.

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“…Next we show the effect of the screening parameter B appearing in the social force model given in Eq. (2). Precisely the order parameter Φ versus the angle ϕ is shown in Fig.…”

Section: S4 Supplemental Resultsmentioning

confidence: 99%

“…However, the original LAMMPS package does not include pairwise interactions corresponding to Eq. (2). Therefore, in order to realize the social force model with LAMMPS, we modify the subroutine source codes, which are named pair_buck.cpp and pair_gran_hooke.cpp in the original package.…”

Section: S1 Molecular Dynamics Simulationsmentioning

confidence: 99%

Membranes for spontaneous separation of pedestrian counterflows

Koyama¹,

Inoue²,

Okada³

et al. 2020

EPL

View full text Add to dashboard Cite

show abstract

“…The science of city is mainly devoted to the application of concepts and methods of complex systems to cities [6][7][8][9]. A city is indeed an adaptive dynamical system which grows in size and population, and consumes energy to maintain its function and structure [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Efficiency and irreversibility of movements in a city

Biazzo

Ramezanpour

2020

Sci Rep

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We know that maximal efficiency in physical systems is attained by reversible processes. It is then interesting to see how irreversibility affects efficiency in other systems, e.g., in a city. In this study, we focus on a cyclic process of movements (home to workplace and back to home) in a city to investigate the above question. To this end, we present a minimal model of the movements, along with plausible definitions for the efficiency and irreversibility of the process; more precisely, we take the inverse of the total travel time per number of trips for efficiency and the relative entropy of the forward and backward flow distributions for the process irreversibility. We perform numerical simulations of the model for reasonable choices of the population distribution, the mobility law, and the movement strategy. The results show that the efficiency of movements is indeed negatively correlated with the above measure of irreversibility. The structure of the network and the impact of the flows on the travel times are the main factors here that affect the time intervals of arriving to destinations and returning to origins, which are usually larger than the time interval of the departures. This in turn gives rise to diverging of the backward flows from the forward ones and results to entropy (disorder or uncertainty) production in the system. The findings of this study might be helpful in characterizing more accurately the city efficiency and in better understanding of the main working principles of these complex systems.

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“…Quantitative understanding of collective human behavior and its dynamics has been the focus of a long-standing, interdisciplinary collection of research. Such efforts span across physics [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], engineering [16][17][18][19][20], economics [21][22][23][24][25][26][27], and planning [28][29][30]. Cities, as agglomerations of human beings, are the natural setting for such studies.…”

Section: Introductionmentioning

confidence: 99%

Drivers learn city-scale dynamic equilibrium

Zhang

Ghanem

2020

Preprint

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Understanding collective human behavior and dynamics at urban-scale has drawn broad interest in physics, engineering, and social sciences. Social physics often adopts a statistical perspective and treats individuals as interactive elementary units, while the economics perspective sees individuals as strategic decision makers. Here we provide a microscopic mechanism of city-scale dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. We capture the decisions of taxi drivers in a game-theoretic model, prove the existence, uniqueness, and global asymptotic stability of Nash equilibrium. We offer a macroscopic view of this equilibrium with laws of thermodynamics. With 870 million trips of over 50k drivers in New York City, we verify this equilibrium in space and time, estimate an empirical constitutive relation, and examine the learning process at individual and collective levels. Connecting two perspectives, our work shows a promising approach to understand collective behavior of subpopulations.

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The statistical physics of cities

Cited by 103 publications

References 97 publications

Membranes for spontaneous separation of pedestrian counterflows

Membranes for spontaneous separation of pedestrian counterflows

Efficiency and irreversibility of movements in a city

Drivers learn city-scale dynamic equilibrium

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