Application of gravity model on the Korean urban bus network

Hong, Inho; Jung, Woo-Sung

doi:10.1016/j.physa.2016.06.055

Cited by 41 publications

(25 citation statements)

References 32 publications

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“…For more than 100 years, researchers have proposed a variety of models for predicting mobility between locations [1][2][3][4], and these models are called spatial interaction models in economic geography [5] and trip distribution models in transportation science [6]. The most widely used model is the gravity model [7] because a mobility pattern similar to Newton's law of universal gravitation is observed in many fields, such as human travel [3,[8][9][10], migration [11], goods transportation [12], international trade [13], mobile communications [14], and even scientific collaborations [15]. In this shared pattern, the mobility (or interaction) between two locations is proportional to the location masses (e.g., populations) and decays with their distance.…”

Section: Introductionmentioning

confidence: 99%

New parameter-free mobility model: Opportunity priority selection model

Liu

Yan

2019

Physica A: Statistical Mechanics and its Applications

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Predicting human mobility patterns has many practical applications in urban planning, traffic engineering, infectious disease epidemiology, emergency management and location-based services. Developing a universal model capable of accurately predicting the mobility fluxes between locations is a fundamental and challenging problem in regional economics and transportation science. Here, we propose a new parameter-free model named opportunity priority selection model as an alternative in human mobility prediction. The basic assumption of the model is that an individual will select destination locations that present higher opportunity benefits than the location opportunities of the origin and the intervening opportunities between the origin and destination. We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model. The results show that the new model offers universal predictions of intracity and intercity mobility patterns that are consistent with real observations, thus suggesting that the proposed model better captures the mechanism underlying human mobility than previous models.

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Section: Introductionmentioning

confidence: 99%

New parameter-free mobility model: Opportunity priority selection model

Liu

Yan

2019

Physica A: Statistical Mechanics and its Applications

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“…where it is assumed that the error distribution (the probability that the flow is found to be given a predicted flow ) is Gaussian for any i , j . A common example of a measure of this type is the coefficient of determination 17 , 27 , 28 but other examples include mean squared errors 29 , and Pearson correlation coefficients 30 , 31 . However, real data sets give integer valued data, feature no negative flows, and usually have a high proportion of very small flows.…”

Section: Methodsmentioning

confidence: 99%

Predictive limitations of spatial interaction models: a non-Gaussian analysis

Hilton

Sood

Evans

2020

Sci Rep

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We present a method to compare spatial interaction models against data based on well known statistical measures that are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the US Census 2000. We find that the radiation model performs significantly worse than an appropriately chosen simple gravity model. Various conclusions are made regarding the development and use of spatial interaction models, including: that spatial interaction models fit badly to data in an absolute sense, that therefore the risk of over-fitting is small and adding additional fitted parameters improves the predictive power of models, and that appropriate choices of input data can improve model fit.

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“…Kwon and Jung investigated the express bus flow in Korea consisting of 74 cities and 170 bus routes with 6692 operating buses per day and confirmed the presence of the gravity law [25]. Hong and Jung studied the urban bus networks of Korean cities and confirmed the presence of the gravity law [26].…”

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confidence: 89%

Gravity law in the Chinese highway freight transportation networks

Wang

Jiang

et al. 2019

EPJ Data Sci.

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The gravity law has been documented in many socioeconomic networks, which states that the flow between two nodes positively correlates with the strengths of the nodes and negatively correlates with the distance between the two nodes. However, such research on highway freight transportation networks (HFTNs) is rare. We construct the directed and undirected highway freight transportation networks between 338 Chinese cities using about 15.06 million truck transportation records in five months and test the traditional and modified gravity laws using GDP, population, and per capita GDP as the node strength. It is found that the gravity law holds over about two orders of magnitude for the whole sample, as well as the daily samples, except for the days around the Spring Festival during which the daily sample sizes are significantly small. Accordingly, the daily exponents of the gravity law are stable except during the Spring Festival period. The results also show that the gravity law has higher explanatory power for the undirected HFTNs than for the directed HFTNs. However, the traditional and modified gravity laws have comparable explanatory power.

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Application of gravity model on the Korean urban bus network

Cited by 41 publications

References 32 publications

New parameter-free mobility model: Opportunity priority selection model

New parameter-free mobility model: Opportunity priority selection model

Predictive limitations of spatial interaction models: a non-Gaussian analysis

Gravity law in the Chinese highway freight transportation networks

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