Comparison of Neural and Conventional Approaches to Mode Choice Analysis

Abstract

This paper describes a new approach to behavioral mode choice modeling using neurofuzzy models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. In addition, the approach only selects the variables that significantly influence the mode choice and displays the stored knowledge in terms of fuzzy linguistic rules. This allows the modal decision-making process to be examined and understood in great detail. The neurofuzzy model is tested on the U.S. freight transport market using information on individual shipper and individual shipments. Shipments are disaggregated at the five-digit Standard Transportation Commodity Code level. Results obtained from this exercise are compared with similar results obtained from the conventional logit mode choice model and the standard back-propagation artificial neural network. The advantages of using the neurofuzzy approach are described.

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