چكيده به لاتين
Travel demand forecasting modeling began in the 1950s. The first models built in a aggregate manner have four independent modeling stages of trip generation, trip distribution, mode split and route assignment. But after observing the shortcomings of this process in the analysis of strategies and the incorrect results of demand forecasting, since the 1970s, the development of dissaggregate methods, that is, activity-based models that have a behavioral basis, was welcomed by planners and decision makers in the transportation industry. In transportation models, it is people's activity schedule and their characteristics that shape their trips and travel-related behaviors. Therefore, the aim of this study is to investigate the impact of socio-economic variables on the activity pattern of people during the day to predict their travel behavior. The data included in this study was obtained through 2007/2008 household travel survey conducted among families in Washington. The models employed in this study are based on the multinomial logit framework. These models take into account the daily activity patterns of individuals and their corresponding trip sequences as determined by the household activities schedule. By doing so, the models are able to assess the attractiveness of various options available to each individual and evaluate them against alternative choices. Using seven activity purpose (work, school, escort, personal business (freelance work and virtual work with phone and internet), shopping, meal, and social/recreational) and variables in several categories (type of people, age group, household income, household composition, gender/child), a daily activity pattern model was created. For each of the seven activity purpose, three alternatives (one tour, two tours, and three or more tours) were created for the second model, which calculated an exact number of tours for each of the seven tour activity purpose. The third model, which is the model of the number and purpose of work-based sub-tours, looks at how certain aspects of people's daily activity patterns and some socioeconomic variables influence the selection of work-based sub-tours. According to the results of the model, the value of ρ^2 (C) in the daily activity pattern model was equal to 0.1527. According to similar studies in the field of activity pattern models as well as the large number of parameters of the present model, the value of 0.1527 is evaluated as very favorable. Finally, each of these models was analyzed and examined and it was observed that the sign and value of each of the estimated parameters of the model seems reasonable and significantly influences people's preferences and choices.
