Show simple item record

dc.contributor.authorEdara, Praveen Kumaren_US
dc.date.accessioned2011-08-06T15:58:26Z
dc.date.available2011-08-06T15:58:26Z
dc.date.issued2003-10-02en_US
dc.identifier.otheretd-10152003-144051en_US
dc.identifier.urihttp://hdl.handle.net/10919/9845
dc.description.abstractArtificial 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.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis_etd.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectNeural Networksen_US
dc.subjectRegressionen_US
dc.subjectAmerican Travel Survey Dataen_US
dc.subjectLogiten_US
dc.subjectMode Choiceen_US
dc.titleMode Choice Modeling Using Artificial Neural Networksen_US
dc.typeThesisen_US
dc.contributor.departmentCivil Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineCivil Engineeringen_US
dc.contributor.committeechairTeodorovic, Dusanen_US
dc.contributor.committeememberCollura, Johnen_US
dc.contributor.committeememberTrani, Antoino A.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10152003-144051en_US
dc.date.sdate2003-10-15en_US
dc.date.rdate2004-10-27
dc.date.adate2003-10-27en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record