Urban transportation systems and traffic management
A. Rasaizadi; M. Askari
Abstract
The modal split model is one of the steps of the classical four-step travel demand planning. Predictive, descriptive, and prescriptive modal split models are essential to make a balance between travel demand and supply. To calibrate these models, it is necessary to detect and employ influential independent ...
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The modal split model is one of the steps of the classical four-step travel demand planning. Predictive, descriptive, and prescriptive modal split models are essential to make a balance between travel demand and supply. To calibrate these models, it is necessary to detect and employ influential independent variables that are related to characteristics of travel modes, individual and family attributes, zones land use, etc. In previous studies, researchers used the household size, the number of children, and the number of employees as independent variables to show the role of family structure on the modal split. These variables cannot discriminate between different families with different structures. This paper uses the life cycle concept to categorize families based on their structures, and the effectiveness of these new variables on modal split models is examined. For this purpose, five types of family structures are considered that differences between them are based on the age of the family’s children. The Multinomial Logit model is used for mode choice modeling for different trip aims. The mode choice model has been calibrated using the origin-destination data of Qazvin-Iran. Results show the critical role of life cycle dummies in the mode choice models compared to household size, for work, educational, personal, and social- recreational trip aims. Life cycle variables are more active on the work trips mode choice model by estimating 14 significant coefficients, in a 90 percent level of significance. The number of life cycle significant coefficients is decreased to 3 for the shopping trips model.
Urban transportation systems and traffic management
A. Edrisi; N. Javanbakht; H. Ganjipour
Abstract
In this study, the manner of private taxis drivers has been investigated for choosing passenger and destination from a fixed point. Therefore, two models called Multinomial and Nested Logit Models have been utilized. The information gained by scrolling in 2016 is the input data, which are in the format ...
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In this study, the manner of private taxis drivers has been investigated for choosing passenger and destination from a fixed point. Therefore, two models called Multinomial and Nested Logit Models have been utilized. The information gained by scrolling in 2016 is the input data, which are in the format of revealed preference, acquired by the verbal interview in Vanak Square in Tehran (Iran). Based on data resulted by 120 questionnaires the selection modeling of passenger and destination was done. The results of the descriptive analysis show that 96.7% of respondents are men and only 3.3% are women. In addition, 15% of them are illiterate, 16.7% have under Diploma degree, 52.5% have Diploma degree and 15.8% have Bachelor degree or higher. On average, the verification was 44%, and the results achieved by this research show that the parameters consisting of searching time, the working time of drivers and the traveling time were the most important factors resulted from the calibration of the Logit models. Nested Logit model has a better performance compared with the Multinomial Logit model. The Nested Logit Model has ρ2=0.45 while this value is 0.35 for the Multinomial Logit Model. Finally, the suitable decision has been made in the various path based on gained results.
