Dynamic pricing of free-floating carsharing networks with sensitivity to travellers' attitudes towards risk

Free-floating carsharing (FFCS) systems are characterised by volatile fleet distribution as well as customers' heterogeneous price sensitivity and spatiotemporal flexibility. There is thus an opportunity for operators to employ dynamic pricing to manage various aspects of fleet allocation: which customer is provided which vehicle, at what time and price, and the agreed pick-up and drop-off location. While there are emerging examples of dynamic pricing in FFCS, there is as yet no general framework for the interaction of consumer and operator behaviours in this context, most particularly consumer response to the inherent risks and uncertainties in the journey characteristics noted above. In this study, we propose a choice-based framework for modelling the supply/demand interaction, drawing on behavioural models of decision-making in risky choice contexts and empirical stated-choice data of user preferences in a dynamically priced FFCS market. In addition to the 'spot market' mechanism of dynamic pricing, the proposed framework is capable of evaluating operator strategies of allowing (at an agreed price) customers to make guaranteed advance reservations. We demonstrate that this approach allows the system operator to set an optimal pricing strategy regardless of whether user risk preferences are risk-seeking or risk-averse. We also demonstrate the applicability of the proposed framework when the operator seeks to maximise revenue (as with a private operator) vs social welfare (as with a public operator). In the case study which employs empirical user preferences, we show that users' risk preferences have a relatively small impact on revenue, however the impacts are much larger if there is a mismatch between users' actual risk preferences and the system operator's assumptions regarding users' risk preferences.

Automated summary

Free-floating carsharing systems are characterized by volatile fleet distribution and diverse customer preferences, creating opportunities for operators to utilize dynamic pricing for fleet allocation. This study proposes a choice-based framework for modeling supply/demand interaction in a dynamically priced FFCS market, considering user risk preferences and behavioral decision-making models. The framework can evaluate operator strategies beyond spot market pricing, including advance reservation options, to set optimal pricing strategies regardless of user risk preferences.