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This thesis investigates consumer acceptance of subscription drive-it-yourself car services [SDCSs], which are an evolution of car hire that began entering the commercial marketplace in the mid- 1990s. The aim of this research is to develop techniques to forecast how consumer demand for SDCSs may develop. On the basis of research reported in this thesis, it is argued that a person’s [strategic] decision to subscribe to an SDCS can be reasonably considered to have a dependency with their expectation of [tactically] using it to access particular out-of-home personal activities. It is shown that people can also be thought to view subscribing to an SDCS as part of a larger ‘portfolio’ choice of travel options. Traditional analyses of people’s travel choices are insensitive to these two issues. Two datasets, one revealed-choice and the other stated-choice, were designed in order to provide empirical data to test the proposed ‘strategic/tactical’ and ‘portfolio’ analytical form. The revealed-choice dataset made use of web-based data-mining techniques, whilst the stated-choice survey is novel in several respects to address the challenges presented by the SDCS context. The methodological innovations proposed in this research proved successful in forecasting consumer demand for SDCSs in the empirical application, and appear promising for wider use within the transport domain and related research fields.

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.