Prediction of Commuter Choice Behavior Using Neural Networks

In order to reduce air pollution and reduce the amount of traffic on highways in the western United States, certain states have set up worksite trip reduction programs. Employers in these states must comply with worksite trip reduction laws and submit trip reduction plans to their respective regulatory agency each year. These plans are currently evaluated manually, and are either rejected or accepted by the agency. There are two major flaws in this system; the first is the amount of time required by the agency to review a plan could be a matter of months, and the second is that human reviewers have subjective opinions regarding the effectiveness of plans. The purpose of this thesis is to develop computer models using Radial Basis Function neural networks, with centers built using the k-means clustering algorithm. These networks will be compared against the performance of a commercial neural network-modeling program known as Predict, as well as the traditional method of selecting RBF neurons from the training set