SCIENTIA GEOGRAPHICA SINICA ›› 2017, Vol. 37 ›› Issue (2): 181-189.doi: 10.13249/j.cnki.sgs.2017.02.003

• Orginal Article • Previous Articles     Next Articles

Competing Effects Among Destinations in Spatial Interaction Models:An Empirical Study Based on Intercity Railway Passenger Data of China

Zhuolin Tao1,2(), Teqi Dai3(), Qingjing Zheng3, Jinshe Liang3   

  1. 1.College of Urban and Environmental Sciences, Peking University, Beijing 100871,China
    2.School of Urban Planning and Design, Peking University, Shenzhen 518055, Guangdong, China
    3.School of Geography, Beijing Normal University, Beijing 100875, China
  • Received:2016-01-19 Revised:2016-10-25 Online:2017-02-25 Published:2017-02-25
  • Supported by:
    National Natural Sciences Foundation of China(41401170)


Spatial interaction model is an important research field. Existing studies indicate spatial structure of destinations has a significant impact on spatial flow. Thus, traditional spatial interaction models suffer model misspecification problem because the absence of spatial structure variable. Among the modified models introduced to solve the misspecification problem, the competing destinations model is the most widely-used one.The competing destinations modelassumes that the travelers’ destinations selecting process adopts a hierarchical information processing strategy.Based on this strategy, the spatial decision process is divided into two stages. In the first stage, travelers select a destinations cluster containing a set of destinations; in the second stage, travelers select an individual destination from the cluster selected in the first stage. The competing destinations model has been empirically applied in numerous studies in foreign countries.However, the empirical conclusions with respect to the validity of the competing destinations model are still far from agreement. Moreover, none empirical study of this model has been conductedin China. This study applies the competing destinations model based on intercity railway passenger data in 2010 in China, and test its validity by comparing it with traditional spatial interaction models. The estimations of the competing destinations model as well as the traditional spatial interaction model are conducted by the maximum likelihood method, which is calculated by a new method distinguishing from existing studies, i.e. the Particle Swarm Optimization (PSO) algorithm. The conclusions can be drawn as follows. 1) Spatial structure has a significant impact on intercity railway passenger flow of China, and there exists a significant competing effect among destinations both in the system-wide estimation results and in the origin-specific estimation results. The system-wide distance-decay parameter estimated in the competing destinations model (-1.165) is more negative than in the traditional spatial interaction model (-1.108). In the other hand, 124out of a total number of 177 (the ratio is 70% ) origin-specific distance-decay parameter estimationsare more negative in the competing destinations model than in the traditional spatial interaction model, while 140 out of 177 (79%) origin-specific destinations accessibility indicator estimations are negative in the competing destinations model. These characteristics have not ever been reported in Chinese context in existing studies. 2) The competing destinations model reduces the spatial autocorrelation among distance-decay parameters, thus significantly corrects the misspecification problem of traditional spatial interaction models. These results illustrate that the competing destinations model performs significantly better than the traditional spatial interaction model, and thus the improvements by the competing destinations model are empirically valid in Chinese context. 3) The parameters estimation and empirical analysis of traditional spatial interaction models (i.e. gravity model) in existing literature may be biased, while the competing destinations model is an efficient improvement and can play an important part in empirical analysis.

Key words: spatial interaction models, gravity model, competing destinations model, spatial structure, distance-decay parameter

CLC Number: 

  • K902