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where I (·) is the indicator function. Each observation contributes two terms to theloglikelihood function. The first isjWtj , wherey tj , and the second is minus thelogarithm of the denominator that appears in equation (1). The above explanation dealswith multinomial probability and loglinear models. However our study is based on thenested MNL model (McFadden, 1981). There is a range of advantages to employingMNL model instead of a series of regular logistic regressions. First, when applying allthe data for the estimation rather than subsets so, the estimates are prompter. Secondly,there follows a logical relationship amidst the choices, for instance, if an individualprefers to travel by car to bus and prefers motorcycle to car, he should prefer amotorcycle to a bus, and his relative orientation should be a definite variety ofcombination of the individual preferences. The coefficients once generated representhow the logarithmized probability of choosing one alternative instead of the baseoutcome changes if variable changes from 0 to 1.Various determinants of the mode to transport to work are considered. Table 1 present adescription of variables used in the MNL model. The choice of transport mode isclassified under three distinct categories namely: car, bus and others (motorcycle, taxis,company van/car and on foot). Demographic characteristics include age, gender,household size, education and income level. The travel characteristics described so farconceal significant differences between genders. Males travelling by car outweigh femalecar travelers by more than the double as revealed by the survey. A slightly higherproportion of women are bus passengers. Travelling by motorcycle and other mode tendsto be more of a male characteristic. Next, education level which includes secondary,Risø International Energy Conference 2011 Proceedings Page 392