Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and levelof-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were:(1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.

The introduction and adoption of autonomous vehicles (AVs) will likely reshape the transportation system and many economic activities. The economic literature on technology adoption, based on studies in agriculture and other sectors, provides lessons on the diffusion of AVs and its social and economic impacts. We rely on the threshold model of diffusion, where heterogeneous agents make decisions pursuing their self-interests. Applications of the threshold model point to case studies of other technologies where one can gain information and make predictions about the future of AVs. We find that private ownership of AVs may prevail after a transition period, as was the case in other technologies like computers, tractors, and conventional vehicles. With technological progress, the cost of privately owning AVs may decline. Further, there will be an increase in vehicle miles traveled (VMT) per capita, there may be more vehicles on the road, and perhaps the transportation user-base will expand to include those currently facing limited mobility. Congestion is likely to depend on the tradeoff between the expansion of VMT and increased efficiency of AVs to communicate and help regulate traffic. Furthermore, differentiation of vehicles will increase as driving time becomes freed for other activities. These trends may lead to increased greenhouse gas emissions and expansion of the transportation sector. Finally, the technology will evolve and may result in complementary innovations needing to be addressed, including the "last 10 feet" problem. It is evident that the future of the transportation system governed by AVs is most likely not going to be sustainable. This necessitates the importance of developing and enforcing rigorous policies at the metropolitan level and TNC levels to ensure a sustainable evolution of the future of transportation mobility.

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