Reconstructing Gravitational Attractions of Major Cities in China from Air Passenger Flow Data, 2001-2008: A Particle Swarm Optimization Approach

In contrast to the conventional gravity model that estimates flows between cities of given sizes, the reverse gravity model attempts to reconstruct the theoretical gravitational attractions of cities from network flow data. This article introduces the particle swarm optimization (PSO) method in solving the reverse gravity model. With comparison to the linear programming (LP) and the simplified algebraic method (SAM), the advantages of the PSO method include flexibility in the impedance function and better goodness of fit. The method is applied to the case study of air passenger transport in China. The results indicate that estimated nodal attractions in cities are generally in line with their population sizes, but there are significant discrepancies between them. A close examination of the discrepancies reveals that a better developed tertiary sector and higher tourism revenue per capita are associated with overestimated attractions in air transport, and stronger railway dominance influences their underestimation. The changes of gravitational attractions in major cities from 2001 to 2008 indicate the declining dominance of the largest cities and a move toward a more balanced development in the air transport system of China.