Comparison Of The Coefficients Of RP And SP Models For Modal Choice (a Case Study Of Karachi City, Pakistan)
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A. Q. Memon, K. Sano & M. Adnan
This study attempted to find out the comparison between the coefficients of the similar exogenous variables of the two multinomial logit models. These models were developed for the city of Karachi, the business capital of Pakistan. The two models were developed with the Revealed Preference and Stated Preference data. The choice set for the model developed with PR data was car, motorcycle, Para transit, bus and coach. All the information about these modes was directly asked from the commuters in the survey carried out for modeling the travel behavior. However in the SP data model, the new mode of magnetic train was included in the choice set and the surveyors gave all the information to the commuters and they were asked about their preference. The coefficients of variables used in the utility functions were calibrated by using the HIELOW software. Chi-Square analysis was carried out to find out the difference between the coefficients of the similar exogenous variables of the two models. It was revealed that the difference between the coefficients of travel time public, car ownership, motorcycle ownership, income coach, age bus and age Para transit and house hold public for RP and SP data was found to be significant at 95 % confidence level. It was found that mostly variables of the RP data set related with public transportation were significantly different from the same variables of the SP data set, except car ownership and sex Para transit. As the magnetic train was included in the public mode and as it was observed that the major share from the (Bus and Coach users) will shift to the new magnetic train so the parameters related to public transportation were found to be significant. The reason for this unexpected result of car ownership was that, it was related with the modal constant of car, which was comparatively high in the SP data set. Keywords: multinomial logit model, RP data, SP data, magnetic train, Chi square.
multinomial logit model, RP data, SP data, magnetic train, Chi square.