Title page for ETD etd-10152003-144051


Type of Document Master's Thesis
Author Edara, Praveen Kumar
Author's Email Address praveen@vt.edu
URN etd-10152003-144051
Title Mode Choice Modeling Using Artificial Neural Networks
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Teodorovic, Dusan Committee Chair
Collura, John Committee Member
Trani, Antoino A. Committee Member
Keywords
  • Neural Networks
  • Regression
  • American Travel Survey Data
  • Logit
  • Mode Choice
Date of Defense 2003-10-02
Availability unrestricted
Abstract
Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available. The primary goal of this thesis is to develop mode choice models using artificial neural networks and compare the results with traditional mode choice models like the multinomial logit model and linear regression method. The data used for this modeling is extracted from the American Travel Survey data. Data mining procedures like clustering are used to process the extracted data. The results of three models are compared based on residuals and error criteria. It is found that neural network approach produces the best results for the chosen set of explanatory variables. The possible reasons for such results are identified and explained to the extent possible. The three major objectives of this thesis are to: present an approach to handle the data from a survey database, address the mode choice problem using artificial neural networks, and compare the results of this approach with the results of traditional models vis-à-vis logit model and linear regression approach. The results of this research work should encourage more transportation researchers and professionals to consider artificial intelligence tools for solving transportation planning problems.
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