Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models

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.