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Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models.

Zihan Wang1, Yanguang Chen1

  • 1Department of Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Dual gravity models effectively predict passenger flows using big data, even with missing information. This approach reveals fractal traffic network properties and shifting urban connectivity dynamics.

Keywords:
Tencent location big datadual gravity modelfractalinterurban passenger flows

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Area of Science:

  • Spatial analysis
  • Network science
  • Transportation geography

Background:

  • Geographical gravity models are used for spatial flow prediction.
  • Modeling spatial flows with missing data in urban traffic networks remains a challenge.

Purpose of the Study:

  • To characterize interurban passenger flows in Beijing-Tianjin-Hebei using dual gravity models.
  • To address challenges of missing data in spatial flow modeling.

Main Methods:

  • Application of dual gravity models.
  • Utilizing Tencent location big data.
  • Least squares regression for parameter estimation.

Main Results:

  • Dual gravity models effectively describe railway and highway passenger flows, compensating for missing data.
  • Fractal properties of traffic flows were revealed, with railway flows better fitting the gravity scaling law.
  • Analysis of prediction residuals indicated a spatial dynamics shift from the Beijing-Tianjin-Tangshan triangle to the Beijing-Baoding-Shijiazhuang axis.

Conclusions:

  • Dual gravity models are effective for analyzing traffic network structures and dynamics.
  • The model offers a novel method for estimating fractal dimensions of traffic networks and spatial flow patterns.