Introduction to and calibration of a conceptual LUTI model based on neural networks

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This paper deals with Land-Use-Transport-Interaction (LUTI) and presents the background and the calibration of a conceptual data driven LUTI modeling tool which is based on neural networks. A literature survey reveals the opinion of experts on the state of the art LUTI models: currently used land use transport models are too aggregate in substance to match travel demand models. Therefore research is conducted into the refinement of the models; resulting in comprehensive models. Unfortunately lack of theoretical frameworks results in these models not being operational on a large scale. This paper looks for an alternative approach and therefore addresses the following questions: (i) what are solution methods to make LUTI models more applicable; (ii) is there a sound way to put into operation these solution methods; (iii) what modeling technique is suitable to be used in this context; (iv) how does the conceptual LUTI then look like; and finally (v) can we calibrate and test this model. This leads to a conceptual model with three building blocks; (i) accessibility; (ii) household location choice; and (iii) employer location choice. Based on the demands and the previously mentioned lack of clear theories, it is concluded that a data driven approach, using Artificial Neural Networks (ANNs), is suitable to fit the framework. The auto calibration of ANN(s) ensures that complex relationships are found without a theoretical framework. The calibration of the ANNs in the model shows good results. Further research has to result in the actual implementation of the model. For the covering abstract see ITRD E129315.