Modelling the relation between income and commuting distance

Carra, Giulia; Mulalic, Ismir; Fosgerau, Mogens; Barthélemy, Marc

doi:10.1098/rsif.2016.0306

Cited by 35 publications

(25 citation statements)

References 25 publications

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“…An interpretation of the radiation model in the context of job search and, consequently, for the formation of commuting flow networks has been given in [187]. The basic components are individuals residing within a demarcated geographical area, who are seeking employment.…”

Section: The Radiation Modelmentioning

confidence: 99%

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Human mobility: Models and applications

et al. 2018

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Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.

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Section: The Radiation Modelmentioning

confidence: 99%

“…In (b), (c) and (d) rescaled distributions of commuting distances. In blue, the empirical data for several years in the three countries, in dark blue the averaged empirical distributions, and superimposed in red the model fits using a single parameter.Figure from[187].…”

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Human mobility: Models and applications

et al. 2018

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“…Human mobility has been studied for decades due to the relevant role it plays in a wide spectrum of applications including economic questions and living conditions [1][2][3], city structure [4,5], forecasting epidemic spreading [6][7][8][9], traffic demand and design of new infrastructure [10], or urban pollution and air quality [11]. Data on people migrations dates back at least to 1871 when the United Kingdom registered the difference in inhabitants during a decade [12].…”

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Field theory for recurrent mobility

et al. 2019

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Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties.

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“…In some countries (e.g. US, UK, Denmark), this distance is broadly distributed [18] which suggests that we are far from an optimal spatial organization with many mixed land-use centers (such as 'urban villages') scattered throughout the city.…”

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confidence: 99%

A global take on congestion in urban areas

Barthélemy

2016

Environ Plann B Plann Des

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We analyze the congestion data collected by a GPS device company (TomTom) for almost 300 urban areas in the world. Using simple scaling arguments and data fitting we show that congestion during peak hours in large cities grows essentially as the square root of the population density. This result, at odds with previous publications showing that gasoline consumption decreases with density, confirms that density is indeed an important determinant of congestion, but also that we need urgently a better theoretical understanding of this phenomena. This incomplete view at the urban level leads thus to the idea that thinking about density by itself could be very misleading in congestion studies, and that it is probably more useful to focus on the spatial redistribution of activities and residences.The increasing likelihood that an ever larger number of urban inhabitants can afford a private car continues to structure the spatial organization of our cities [1] with dramatic effects on their efficiency and development. Even when urban infrastructures are drastically remodelled in favour of the automobile, congestion keeps growing and has become one of the most important challenge for politicians and planners. In addition, it is an ever more important cause of serious health problems [3], and congestion leads to significant loss of time through traffic jams which has a negative impact on the economical growth of cities.With new sources of data, we are hard at work in reaching out for a quantitative understanding of cities [4] and this science helps to make these statements about congestion more precise. In particular, the data provided (now on a yearly basis) by one of the major GPS navigation device companies [5] allows us to produce a certain number of results about congestion in world cities that are worth reflecting upon. Of particular interest is the estimate of extra travel time per day δ due to congestion. It is obtained by computing the increase of the average travel times during peak hours compared to a free flow situation. For London the extra travel time per day is 39 minutes for a 1 hour trip, and this can be compared to Mexico (57'), Los Angeles (43'), Beijing (42'), Paris (38') and Johannesbourg (35') which are immediate examples computed from such data. In other words, if a trip in free flow (without congestion) has a duration τ 0 , with congestion (during peak hours) it will take a time equal to (1 + δ)τ 0 (where δ and τ 0 are measured in units of one hour).This information allows us to discuss regional peculiarities and to monitor the yearly increases in congestion [6][7][8]. In principle, it also allows us to compare different cities with one other, but this has however to be done with care, and it could be misleading to compare the extra travel time per day directly. Indeed, with different sizes of city, a one hour trip could be close to the average duration of trips in one size of city, whereas in a smaller city, it could be above the average: thus the average commuting time clearly depends on the size ...

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Modelling the relation between income and commuting distance

Cited by 35 publications

References 25 publications

Human mobility: Models and applications

Human mobility: Models and applications

Field theory for recurrent mobility

A global take on congestion in urban areas

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