  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 level-of-service attributes. Finally, the model was
calibrated and then used to forecast adoption of the carsharing system for
potential investment strategy scenarios.
