Abstract

This article presents a new methodology to determine the hinterland of a port as the aggregation of port choices made by interactive exporters. The main assumption is that exporters and ports interact in space and time between themselves, generating a complex system. Hinterland emerges as the aggregation of zones from which interacting agents send their shipments. This methodology builds upon the theory of cellular automata (CA), incorporating probabilistic components to allow for a rational representation of spatio-temporal exporters decisions. The CA's transition function includes neighbourhood interaction, network effects and ports attributes. The model's parameters are calibrated through a genetic algorithm. This methodology allows the estimation of ports demand capture as a time-dependent variable after a change in the ports level of service. This framework was applied to the automobile exports in Brazil, from 1997 to 2015, with a 73% of prediction success.