The emergence of agent-based modelling from the field of artificial intelligence (Al) presents a new and alternative approach to geographical modelling. The vast potential offered by agent-based models in representing distributed complex systems, coupled with the increase in available computing power has resulted in agent-based models becoming an increasingly popular and powerful tool within geographical applications. These models offer distinct advantages over traditional empirical techniques through their characteristics of autonomy, flexibility and adaptability. There is an emerging recognition that the power of agent-based systems is enhanced when integrated\ud with other AI-based and conventional approaches. The resulting hybrid models are powerful tools that combine the flexibility of the agent-based methodology with the strengths of more traditional modelling.\ud \ud This research examines the application of a hybrid agent-based model to the case study of the retail petrol market. Detailed analysis of the real data was first performed before the construction of an agent-based model. Model performance was evaluated against real data from the UK for a three month period in 1999. On the basis of this evaluation, the agent model was further developed to incorporate consumer behaviour by the inclusion of a spatial interaction (SI) model and a network model. Suitable parameters for these models were derived through detailed analysis of the real data, numerical experimentation and experimentation on the real data. These developments improved the performance of the model. A genetic algorithm (GA) was constructed to provide an\ud objective approach to deriving optimal parameters. There was a close agreement in the values selected by the GA and those derived by hand. This research clearly demonstrates that agent-based modelling has the ability to improve on existing geographical models. Further investigation is\ud needed if this potential is to be fully realised for a range if geographical problems
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