Forecasting interregional freight flows by gravity models : Utilising OLS-, NLS- estimations and Poisson-, Neural Network- specifications

• In this paper we compare four different specifications of gravity models for inter regional freight flow prediction. The most used specification with OLS estimation is compared with a model where errors are assumed to be Poisson distributed and a model similar to this but with errors assumed to be normally distributed, namely Non-linear Least Square (NLS). We also compare these with a Feed Forward Back Propagation Neural Network. (NN) Data consists of freight flows between Norwegian counties. The attribute describing the nodes is population and distance in kilometres is used as a proxy for costs on transport links. Since we here only are interested in inter regional flows intra regional flows are excluded. Results are also compared with an earlier study by Bergkvist and Westin (1997) were also intraregional data were used. Performance measures used here shows that OLS compared to Poisson, NLS and Neural Network specifications will produce worse predictions. However, the question on how to compare performance is not indisputable and of great importance since different measures can produce quite different results, not just in scale but also in ranking. When non-linear models are used the lack of a simple easily interpretable Rsquare measure as in linear regression is evident. We therefore use different measures of performance and discuss their pros and cons.

Subject headings

SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)

SOCIAL SCIENCES  -- Economics and Business -- Economics (hsv//eng)

Keyword

Gravity model

Transportation

Freight flows

Spatial interaction

OLS

Poisson-regression

Neural network

Economics

Nationalekonomi

Publication and Content Type

vet (subject category)

kon (subject category)