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

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Abstract

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.

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Notes

  1. We do not consider users parking and performing activities in the middle of their carsharing journey, so the intended drop-off time can be computed by the pick-up time and the travel time between intended pick-up and drop-off zones. The proposed framework is amenable to incorporating this type of usage behaviour; we leave this as an item for future refinement.

  2. In 2019 DriveNow merged with Car2Go and was renamed ShareNow; it suspended service in London in March 2020.

  3. In this study we used five interpolates on the nonlinear utility curves for all numerical examples in Sect. 3. The ‘elbow method’ shows that the mean square errors between the nonlinear curves and the piecewise linear approximation decrease very slowly after four interpolates. We performed additional experiments (available from the authors on request) of the sensitivity of the optimisation results to the number of interpolates, which found stability in results with greater than three interpolates.

  4. The £5 value was chosen as a representative central value by considering competing mode costs, including the London Underground Zone 1 fare of £2.40 (off-peak), the bus fare of £1.50, the daily Congestion Charge for private automobile travel of £15, and the flag-drop taxi fare of £3.20 (plus distance/time charges).

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Acknowledgements

The authors thank the anonymous peer reviewers and editor for helpful feedback on earlier versions of this manuscript. The authors thank DriveNow UK for providing access to FFCS users to take part in the survey component of this research, and helpful discussions regarding the results. However, the authors are solely responsibility for the content, including conclusions and any errors. This study was partially funded by the Southeast University Start-up funding (3221002109A1). An earlier version of this research was presented as a poster at the 99th Annual Meeting of the Transportation Research Board (Washington D.C., January 2020).

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, China

    Chenyang Wu

  2. Urban Systems Lab, Department of Civil and Environmental Engineering, Imperial College London, London, UK

    Chenyang Wu, Scott Le Vine, Aruna Sivakumar & John Polak

  3. Centre for Transport Studies, SUNY New Paltz, New Paltz, USA

    Scott Le Vine

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  1. Chenyang Wu
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  2. Scott Le Vine
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  4. John Polak
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Contributions

The authors confirm contributions to the paper as follows: Study conception and design: CW, SL, AS, JP. Mathematical modelling: CW. Manuscript preparation: CW, SL, AS. All authors confirm their approval of the final version of the manuscript.

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Correspondence to Chenyang Wu.

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John Polak: Deceased.

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Wu, C., Le Vine, S., Sivakumar, A. et al. Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk. Transportation 49, 679–702 (2022). https://doi.org/10.1007/s11116-021-10190-8

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