چكيده به لاتين
Self-driving vehicles will change the supply of transportation and affect travel demand. Self-driving vehicles have the potential to increase car travel by reducing travel and parking costs, and by making travel more accessible to people too young to drive and the elderly. The increase in vehicle trips can be due to the transfer of trips from other modes of travel or the generation of entirely new trips. The purpose of this research is to investigate the impact of self-driving cars on changes in travel. The important issue is how these cars, by providing more access, affect the activities performed by people and estimate the demand of a specific age group that includes people under the legal age to obtain a driver's license and the elderly. To do the work, first, the data related to persons, households, land use, and skim matrices between the studied areas of Washington DC were entered into Activitysim software. The information obtained from the models without the presence of self-driving cars was examined, then the way the self-driving car traveled was added to the software, and the information resulting from the effects of these cars on the changes in trips, which is derived from the changes in the variables of the specific age group, the travel time in the accessibility, choosing more distant destinations for non-mandatory tours and the frequency of non-mandatory tours, in considered These changes are in such a way that different intervals were considered for each variable and their impact on the changes in the number of trips was investigated. The results in the one-dimensional analysis of the variables show that among the selected variables, the variable of non- mandatory activities of people in the frequency model of non- mandatory tours with 11.7% and the variable of travel time in accessibility model with 5.5% have the greatest effect on the changes in the number of trips. Also, the presence of self driving cars has the greatest impact on shopping and leisure trips. another goal of this research is to build a quick response model, which leads to reducing the long time of implementing the base activity models and identifying the most important variables to investigate the interactive effect of the variables. After examining the interactive effect of the variables, it was found that the travel time variables and the activities of the working people, the age group under 18 years, and the retired are the most important variables influencing the changes in the number of trips.
