TRB Paper No. 03-4348 WORK TRAVEL MODE CHOICE MODELING USING DATA MINING: DECISION TREES AND NEURAL NETWORKS

Travel mode choice modeling has received the most attention among discrete choice problems in travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavior patterns reflected from explanatory variables determine the choices between alternatives or classes. This paper investigates the capability and performance on work travel mode choice modeling of two emerging pattern-recognition data mining methods: decision trees (DT) and neural networks (NN). Models based on these two techniques are specified, estimated, and comparatively evaluated with a traditional multinomial logit (MNL) model. For comparison, the paper presents a unique three-layer formulation of the MNL model, and identifies the similarities and differences of the models ’ mechanisms and structures, comparing them in the model specifications and estimations. Two performance measures, individual prediction rate and aggregate prediction rate, respectively representing the prediction accuracies on individual an