Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models

TL;DR

This study uses dual gravity models and Tencent location data to analyze interurban passenger flows in China's Beijing-Tianjin-Hebei region, revealing spatial interaction patterns, fractal properties, and recent shifts in traffic dynamics.

Contribution

It demonstrates the effectiveness of dual gravity models in describing passenger flows, handling missing data, and uncovering fractal and dynamic properties of urban traffic networks.

Findings

Both railway and highway flows fit the dual gravity model well.

Missing data can be accurately estimated using the model.

Traffic flow patterns exhibit fractal characteristics and dynamic shifts.

Abstract

Passenger flows in a traffic network reflect spatial interaction patterns in an urban systems. Gravity models can be employed to quantitatively describe and predict spatial flows. However, how to model passenger flows and reveal the deep structure of urban and traffic networks in the case of missing partial data is still a problem to be solved. This paper is devoted to characterizing the interurban passenger flows in the Beijing-Tianjin-Hebei region of China by means of dual gravity models and Tencent location big data. The method of parameter estimation is the least squares regression. The main results are as follows. First, both railway and highway passenger flows can be effectively described by the dual gravity model. A small part of missing spatial data can be made up by the predicted values. Second, the fractal properties of traffic flows can be revealed. The railway passenger flows more follow gravity scaling law than the highway passenger flows. Third, the prediction residuals indicate the changing trend of interurban connections in the study area in recent years. The center of gravity of spatial dynamics seems to shift from the Beijing-Tianjin-Tangshan triangle to the Beijing-Baoding-Shijiazhuang axis. A conclusion can be reached that the dual gravity models is an effective tools of for analyzing spatial structures and dynamics of traffic networks and flows. Moreover, the models provide a new approach to estimate fractal dimension of traffic network and spatial flow patterns.