Urban and municipalities management
A. Edrisi; H. Rezaei
Abstract
BACKGROUN AND OBJECTIVES: Ride-hailing is a term to describe booking rides and paying for car services through a smartphone app with a Transportation Network Company. As an innovation in the ride-hailing investigation in Iran, this paper is sought to analyze the influence of individual's demographic ...
Read More
BACKGROUN AND OBJECTIVES: Ride-hailing is a term to describe booking rides and paying for car services through a smartphone app with a Transportation Network Company. As an innovation in the ride-hailing investigation in Iran, this paper is sought to analyze the influence of individual's demographic characteristics on their travel mode choice between ride-hailing, traditional taxi and private car. For this purpose, questionnaires in six different statuses have been designed, and 414 questionnaires have been completed in 22 districts of Tehran metropolitan region. METHODS: To check the utility of choosing private car and traditional taxi compared to ride-hailing, on short, medium, and, long travel distances with commuting and non-commuting purposes in the peak hours of morning and evening, the six multinomial logit models have been done by considering the ride-hailing option as reference alternative, and the private car and traditional taxi options as the first and second ` FINDING:Initially, six logit models were generated, which fitted models are all appropriate. All of the variables used in these models in choosing private car or traditional taxis compared to ride-hailing in different models were statistically significance. But, gender, household dimension, and individuals' educational level didn’t affect the individual's choice. CONCLUSION: The results showed that ride-hailing is more acceptable to younger people, and high-income people attract more to it. Therefore, ride-hailing services can be considered as a wealthy phenomenon and for the young generation. In addition, given the 67% response of individuals incline to use ride-hailing services in a shared way, because of the interest of individuals to use this mode of travel due to its lower cost in some situations, which can be considered as a separate mode of transportation.
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 ...
Read More
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.