On a Generalization of the Modified Gravity Model

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 114)


The modified gravity model is a nonlinear regression model for estimating passenger correspondences between pairs of spatial points [Andronov and Santalova, Simul. Comput. 41(2), 730–745 (2012)]. Unknown parameters of the model and correspondences are estimated using aggregated data, i.e. total number of passenger departures at each point in a considered time interval. The model estimation under the assumption that the error term is distributed normally is carried out in [Andronov and Santalova, Simul. Comput. 41(2), 730–745 (2012); Santalova, Autom. Control Comput. Sci. 47(2), 99–106 (2013)]. However non-symmetry of the passenger flows may violate the assumption about normality of error distribution. In the present research the model with normally distributed errors is generalized to a model with skew-normal error distribution, and a method of its estimation is proposed.


Nonlinear Regression Model Passenger Flow Considered Time Interval Standard Normal Density Correspondence Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research was supported by the European Union through the European Social Fund (Mobilitas grant No GMTMS280MJ) and Estonian Science Foundation (grant No ETF9127).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Tartu UniversityTartuEstonia

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