Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification

Shen, Guobin

doi:10.1016/j.jtrangeo.2003.12.006

Cited by 44 publications

(22 citation statements)

References 31 publications

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“…We use the reverse gravity model to estimate the geographical distance resistance coefficients of the four travel and migration networks. The coefficients are 1.32, 3.55, 3.01, and 1.02 for the census, Baidu, Tencent, and Qihoo, respectively [30,31]. This observation further validates our inference.…”

Section: Discussionsupporting

confidence: 77%

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Deng

et al. 2016

Sustainability

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Spring Festival travel rush is a phenomenon in China that population travel intensively surges in a short time around Chinese Spring Festival. This phenomenon, which is a special one in the urbanization process of China, brings a large traffic burden and various kinds of social problems, thereby causing widespread public concern. This study investigates the spatial-temporal characteristics of Spring Festival travel rush in 2015 through time series analysis and complex network analysis based on multisource big travel data derived from Baidu, Tencent, and Qihoo. The main results are as follows: First, big travel data of Baidu and Tencent obtained from location-based services might be more accurate and scientific than that of Qihoo. Second, two travel peaks appeared at five days before and six days after the Spring Festival, respectively, and the travel valley appeared on the Spring Festival. The Spring Festival travel network at the provincial scale did not have small-world and scale-free characteristics. Instead, the travel network showed a multicenter characteristic and a significant geographic clustering characteristic. Moreover, some travel path chains played a leading role in the network. Third, economic and social factors had more influence on the travel network than geographical location factors. The problem of Spring Festival travel rush will not be effectively improved in a short time because of the unbalanced urban-rural development and the unbalanced regional development. However, the development of the modern high-speed transport system and the modern information and communication technology can alleviate problems brought by Spring Festival travel rush. We suggest that a unified real-time traffic platform for Spring Festival travel rush should be established through the government's integration of mobile big data and the official authority data of the transportation department.

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Section: Discussionsupporting

confidence: 77%

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Deng

et al. 2016

Sustainability

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“…We compute the size P i , P j for nodes i and j and the distance decay exponent α for the RSN by fitting the gravity model using the algebraic method (Shen 2004). The resulting distance decay exponent,α D , is 0.4 for the communication intensity weight, W D ij .…”

Section: Fitting the Gravity Modelmentioning

confidence: 99%

“…Two factors, city size and the geographical proximity between cities, are intensively investigated in these studies. Empirically observed intercity interactions have led to models recalling Newton's gravity law, where the entity size plays the role of mass as reliable information about the internal properties of a node, and the geographical proximity plays a critical role in deciding how to connect the nodes as the 'interaction scheme' (Fortheringham and O'Kelly 1989, O'Kelly et al 1995, Shen 2004). …”

Section: Introductionmentioning

confidence: 99%

Inferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set

Kang

Zhang

et al. 2013

International Journal of Geographical Information Science

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This article provides a novel and practical approach for investigating the characteristics of intercity telecommunication network whose overall and complete information is unavailable. Using a mobile phone call data set covering 4.39 million subscribers registered in a particular region, we construct two intercity mobile communication subnets and infer characteristics of the whole intercity mobile communication network of China. Results confirm that intercity communication intensity is characterized by the gravity model. The communication intensity based on mobile call number decreases along the distance with a scaling exponent 0.5, whereas the scaling exponent for the communication intensity based on mobile call duration is 0.4. Moreover, we uncover the rank-size distribution of tie strength (mobile call number and duration) between a city and its neighbours. The rank-size law of tie strengths between cities is mainly determined by the rank-size distribution of cities. The distance between cities plays a less decisive role than the size distribution in the network, but significantly impacts mobile communication patterns. The call duration of individual intercity mobile communication is generally positively correlated to the communication distance, explaining why the distance decay of communication intensity based on call durations is slower than that based on call numbers. The contribution of this research is twofold. First, we identify the distance decay effect in intercity mobile communications of China and uncover the dominant impact of the rank-size distribution of cities. Second, a method for estimating the properties of the whole network according to the observed interactions of its subnets is developed.

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“…The second approach is the algebraic method [12], which is an approximation algorithm by conducting series multiplication of Equation 1 as…”

Section: B Gravity Model Fittingmentioning

confidence: 99%

Delineating intra-urban spatial connectivity patterns by travel-activities: A case study of Beijing, China

Kang

2015

2015 23rd International Conference on Geoinformatics

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Abstract-Travel activities have been widely applied to quantify spatial interactions between places, regions and nations. In this paper, we model the spatial connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi OD dataset. First, we unveil the gravitational structure of intra-urban spatial connectivities of Beijing. On overall, the inter-TAZ interactions are well governed by the Gravity Model Gij = λpipj/dij, where pi, pj are degrees of TAZ i, j and dij the distance between them, with a goodness-of-fit around 0.8. Second, the network based analysis well reveals the polycentric form of Beijing. Last, we detect the semantics of inter-TAZ connectivities based on their spatiotemporal patterns. We further find that inter-TAZ connections deviating from the Gravity Model can be well explained by link semantics.

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Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification

Cited by 44 publications

References 31 publications

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Inferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set

Delineating intra-urban spatial connectivity patterns by travel-activities: A case study of Beijing, China

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