Transportation Network Companies (TNCs) are changing the transportation ecosystem, but micro-decisions of drivers and users need to be better understood to assess the system-level impacts of TNCs. In this regard in the first paper, we contribute to the literature by estimating a) individuals’ preferences of being a rider, a driver, or a non-user of TNC services; b) preferences of ridehailing users for ridepooling; c) TNC drivers’ choice to switch to vehicles with better fuel economy, and also d) the drivers’ decision to buy, rent or lease new vehicles with driving for TNCs being a major consideration. We use a unique sample (N= 11,902) of the U.S. population residing in TNC-served areas. Elicitation of drivers’ preferences using a large sample is the key feature of this study. The population-weighted statistical analysis indicates that ridehailing services are mainly attracting personal vehicle users as riders, without substantially affecting demand for transit. Moreover, around 10% of ridehailing users reported postponing the purchase of a new car due to the availability of TNC services. The model estimation results indicate that the likelihood of being a TNC user increases with the increase in age for someone younger than 44 years, but the pattern is reversed post 44 years. This change in direction of the marginal effect of age is insightful as the previous studies have reported a negative association. Moreover, older ridehailing users with higher household vehicle ownership who live in suburban areas are less likely to pool rides. On the supply side, 65% of TNC drivers who work daily indicated that driving for TNCs was a consideration in vehicle purchase decisions. We also find that households with postgraduate drivers who drive daily and live in metropolitan regions are more likely to switch to fuel-efficient vehicles. These findings can inform transportation planners and TNCs in developing policies to encourage ridepooling and to improve the average fuel economy of the TNC fleet. In the second paper, we contribute to the literature by estimating a) Safety of children to use autonomous vehicles without any adult; specifically the question that we are analyzing is, “Once automated vehicles are running safely and reliably on all roadways, should a child of age 8, without a driver’s license, be permitted to travel alone in a driverless vehicle on trips up to 3 miles from his/her home?”; b) Preference to use either taxi in which the driver is unknown; ride-hailing services in which the rating of the driver is known or autonomous vehicles in which there is no driver; specifically, the question that we are analyzing is, “If the price and waiting time is the same, what would be the most preferred option among regular taxi, ride-hailing services and autonomous taxis?” and; c) The preference for the price of autonomous ride-hailing specifically Uber services. The question that we are analyzing is, “Should the cost of an automated Uber trip be higher or lower than the one served by a human driver and why?”. Our findings align with the previous work in that we find that males and younger individuals are more likely to use automated vehicles. They are also more likely to allow child to ride in an AV unsupervised. This information is useful for vehicle manufacturers to adjust their manufacturing standards and pricing, so that the modules they produce meet the preferences of the consumers.