Free-floating carsharing systems (FFCS) are characterised by the volatile spatial-temporal distribution
of fleet and user demand, as well as users’ heterogenous price sensitivity and spatial-temporal
flexibility. Hence, it is possible to apply dynamic pricing-based revenue management in a FFCS
system. By dynamically updating prices on different origin-destination pairs according to the realtime fleet and user demand distribution, operators can redistribute user demand as well as increase
their own revenue. In addition to dynamic pricing, advance reservation also has the potential to reduce
the volatility of FFCS system and make the carsharing service more profitable. By having advance
reservation, the operator has better knowledge of future user demand and can prepare in advance for
possible demand peaks. This increase in service reliability can better serve the current FFCS user and
attract potential new FFCS users.
These two approaches have been investigated by some of the literature in the carsharing research,
especially dynamic pricing. The success of both approaches requires the participation of users,
however, and we are still unclear about users’ preferences in respect to the two approaches. Without a
clear understanding of user behaviour, it is too early to assume that the two approaches can help the
operator in revenue management. Dynamic pricing, especially, adds an additional dimension of
uncertainty to the volatile free-floating carsharing system, which may raise users’ aversion and not
help the operator increase the revenue. This thesis contributes to the wider literature by bridging user
behaviour modelling and carsharing system operational management. We estimate users’ preference
for/against dynamic pricing and advance reservation, and develop a decision support tool that helps
the carsharing operator to maximise revenue while taking user behaviour into consideration.
We design a stated choice (SC) survey to collect carsharing users’ responses to two interacting
dimensions of uncertainties. For choice-behaviour under multiple dimensions of uncertainty, we
propose two levels of transformation (attribute- and utility-level transformation). We estimate
carsharing user behaviour under uncertainty through a discrete choice model (DCM). The modelling
results demonstrate that our SC survey design approach can successfully capture carsharing user
behaviour under two interactive dimensions of uncertainty. The modelling results from the two levels
of transformation are consistent with each other, and help the carsharing operator understand the user
behaviour from different angles. This demonstrates the importance of considering both levels of
transformation in risky-choice behaviour modelling with more than one dimension of uncertainty.
We also provide a choice-based optimisation framework that considers users’ risky-choice behaviour.
Choice behaviour as predicted by the risky-choice models and estimated from the SC survey data
feeds into the choice-based optimisation model. We demonstrate the importance of having a correct
understanding of users’ risk preference in dynamic pricing-based revenue management by a numerical
analysis. The choice-based optimisation framework can serve as a decision support tool for carsharing
operators to generate more revenue and better serve the users.