Mode Choice Models For Long Distance Travel In USA

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Title: Mode Choice Models For Long Distance Travel In USA
Author: Rasmidatta, Isaradatta
Abstract: The influences of technology and economic development on human activity are increasingly taking place on larger spatial scales . As a consequence , complementary interactions between urban regions are getting stronger , which causes urban regions to change . In order to stimulate the integration between regions and states level , policy makers need increased knowledge of the factors that influence long distance travel . From an environmental perspective , long distance trips can be very important because most of the trips are made in personal vehicles or airplanes that affect emissions and fuel consumption . Mode choice alternatives for long distance travel include : personal vehicle , air , and ground . Trip purposes (business , personal business , and pleasure ) are considered in modeling . Based on the research results , a household located in an urban area plays an important role in the mode choice decision . A traveler's occupation may affect the mode choice decision between personal vehicle and public carrier ; a traveler in the sales , service , or other occupational categories tends to travel by a personal vehicle rather than a public carrier . A traveler who travels over long weekends , has a household income below $20 ,000 , lives in an urban area , has many household members on the trip , or spends not many nights on the trip prefers to make a long distance trip by personal vehicle . Considering age , as the age of traveler increases , the traveler tends to travel by the air mode ; this is the same as route distance increases . In this study , variables that are exclusive to specific trip purposes between business , personal business , and pleasure include the number of vehicles in a household , traveler occupation , and household income . The prediction results show that Neural Network models (piecewise linear network floating search ) outperform the percent correct for two mode choice (personal vehicle and air mode ) cases and nested logit models outperform the percent correct for three mode choice (personal vehicle , air , and ground ) cases . The results indicate that Neural Networks are a possible model for estimating long distance travel mode choices ; however , for data mining , logistic regression provides better explanations of the variables , especially , for independence of irrelevant alternatives .
URI: http : / /hdl .handle .net /10106 /49
Date: 2007-08-23


Mode Choice Models For Long Distance Travel In USA. Available electronically from http : / /hdl .handle .net /10106 /49 .

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