Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983): Optimization by simulated
annealing, Science 220, 671-680
Knudsen, D.C. and Fotheringham, A.S. (1986): Matrix comparison, goodness-of-fit,
and spatial interaction modeling, International Regional Science Review 10(2), 127-
147
Ledent, J. (1985): The doubly constrained model of spatial interaction: A more general
formulation, Environment and Planning A 17, 253-262
Mozolin, M., Thill, J.-C. and Usery, E.L. (2000): Trip distribution forecasting with
multilayer perceptron neural networks: A critical evaluation, Transportation
Research B 34, 53-73
Openshaw, S. (1993): Modelling spatial interaction using a neural net. In Fischer, M.M.
and Nijkamp, P. (eds.): Geographic information systems, spatial modeling, and
policy evaluation, pp. 147-164. Springer, Berlin, Heidelberg and New York
Openshaw, S. (1998): Neural network, genetic, and fuzzy logic models of spatial
interaction, Environment and Planning A 30, 1857-1872
Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992): Numerical
recipes in C: The art of scientific computing. Cambridge University Press,
Cambridge
Reggiani, A. and Tritapepe, T. (2000): Neural networks and logit models applied to
commuters’ mobility in the metropolitan area of Milan. In Himanen, V., Nijkamp, P.
and Reggiani, A. (eds.): Neural networks in transport applications, pp. 111-129.
Ashgate, Aldershot
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986): Learning internal
representations by error propagation. In Rumelhart, D.E. and McClelland, J.L.
(eds.): Parallel Distributed Processing, pp. 318-362. MIT Press, Cambridge [MA]
Sen, A. and Smith T.E. (1995): Gravity models of spatial interaction behavior.
Springer, Berlin, Heidelberg and New York
Senior, M.L. (1979): From gravity modelling to entropy maximizing: A pedagogic
guide, Progress in Human Geography 3(2), 175-210
Tiefelsdorf, M. and Boots, B. (1995): The specification of constrained interaction
models using the SPSS loglinear procedure, Geographical Systems 3, 21-38
36