A Comparison Of Four Work Trip Distribution Models

Lawson, Howard C.; Dearinger, John A.

doi:10.1061/jhcea2.0000245

Cited by 8 publications

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“…Several studies and variants of intervening opportunities models have been developed on this concept [173,174,175,159,176,177]. The gravity and the intervening opportunities models have been compared several times during the second half of the twentieth century, showing that generally both models perform comparably [178,179,180,181]. In fact one can cast the intervening opportunities model as a special variant of the gravity model with the friction factor f (r i j ) replaced by a function of the number of opportunities between the two locations, f (V i j ) = e −L V i j−1 [182,181].…”

Section: Intervening Opportunities Modelsmentioning

confidence: 99%

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: Intervening Opportunities Modelsmentioning

confidence: 99%

Human mobility: Models and applications

et al. 2018

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“…Several works have focused on the comparison between the two families of models and their performance when compared with empirical data [39][40][41][42][43][44][45][46][47]. It is worth mentioning that a fair comparison requires to be carried out over the same type of mobility data (daily or sporadic trips behave differently) and with the same constraints.…”

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confidence: 99%

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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“…The original law proposed by Stouffer has been reformulated by Schneider [22] and extensively studied since then [19,[23][24][25][26][27]. The two approaches have been widely compared during the second half of the twentieth century [28][29][30][31] showing that generally both approaches performed comparably. However, the simplicity of the mathematical form of the gravity approach appears to have weighted in its favor [11].…”

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confidence: 99%

Systematic comparison of trip distribution laws and models

Lenormand

Bassolas

Ramasco

2016

Journal of Transport Geography

150

149

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Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.Comment: 15 pages, 10 figure

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A Comparison Of Four Work Trip Distribution Models

Cited by 8 publications

References 0 publications

Human mobility: Models and applications

Human mobility: Models and applications

Field theory for recurrent mobility

Systematic comparison of trip distribution laws and models

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