Abstract
The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems. In this paper, we develop an integrated optimization model to link supply and demand together. A discrete choice model that includes vehicle availability as a parameter directly affecting user’s mode choice is introduced and incorporated within the optimization formulation. In this framework, carsharing travel demand is influenced by vehicle supply. The reaction of the demand further changes vehicle availability in the system. The incorporation of a discrete choice model with the Integer Linear Programming formulation leads to a nonlinear model. We propose a linearization scheme to reformulate it. We test the model in realistic case studies representative of an Australian carsharing operator. A sensitivity analysis on total travel demand, system capacity, one-way trip price, and vehicle availability coefficient is undertaken to evaluate their impacts on system profit. The results reveal that the pattern of profit over trip price varies across scenarios with different vehicle availability coefficients and travel demand. The profit-efficiency of enlarging carsharing network is also dependent on travel demand. We conclude that the interdependence between demand and supply should be considered when setting network development plans and pricing strategies in one-way carsharing systems. If there is a strong interaction between demand and supply, the supply of carsharing vehicles has a critical impact on system profit.
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References
Autolib.Eu. (2016) The service | Autolib' [Online]. Available: https://www.autolib.eu/en/how-does-it-work/service/ [Accessed May 1st 2016]
Barth M, Todd M (1999) Simulation model performance analysis of a multiple station shared vehicle system. Transport Res Part C: Emerg Technol 7:237–259
Barth M, Todd M, Xue, L (2004) User-based vehicle relocation techniques for multiple-station shared-use vehicle systems
Catalano M, Lo Casto B, Migliore M (2008) Car sharing demand estimation and urban transport demand modelling using stated preference techniques. Eur Transport 40:33–50
Cervero R, Golub A, Nee B (2007) City CarShare: longer-term travel demand and car ownership impacts. Transport Res Record: J Transport Res Board 1992:70–80
CITYOFSYDNEY.NSW.GOV.AU. (2016) Car sharing - City of Sydney [Online]. Available: http://www.cityofsydney.nsw.gov.au/live/residents/car-sharing [Accessed May 1st 2016]
Communauto.com. (2016) Communauto - Carsharing, a different kind of car use [Online]. Available: https://www.communauto.com/index_en.html [Accessed May 1st 2016]
De Almeida Correia GH, Antunes AP (2012) Optimization approach to depot location and trip selection in one-way carsharing systems. Transport Res Part E: Logist Transport Rev 48:233–247
De Almeida Correia GH, Van Arem B (2016) Solving the user optimum privately owned automated vehicles assignment problem (UO-POAVAP): a model to explore the impacts of self-driving vehicles on urban mobility. Transp Res B Methodol 87:64–88
DI Febbraro A, Sacco N, Saeednia M (2012) One-way carsharing: solving the relocation problem. Transp Res Rec 2319:113–120
Fan W, Machemehl R, Lownes N (2008a) Carsharing: dynamic decision-making problem for vehicle allocation. Transport Res Rec: J Transport Res Board, 97–104
Fan W, Machemehl RB, Lownes NE (2008b) Carsharing: dynamic decision-making problem for vehicle allocation. Transp Res Rec: J Transp Res Board 2063:97–104
Grossmann IE (2002) Review of nonlinear mixed-integer and disjunctive programming techniques. Optim Eng 3:227–252
IBM-ILOG. (2016) IBM CPLEX Optimizer - United States [Online]. Available: http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/ [Accessed May 1st 2016]
Inrosoftware.com. (2016) INRO | Emme [Online]. Available: https://www.inrosoftware.com/en/products/emme/ [Accessed 22 June 2016]
Jian S, Rey D, Dixit V (2016) Dynamic optimal vehicle relocation in Carshare systems. Transportation Research Board 95th annual meeting. Washington DC, United States, 2567, 1, 9
Jorge D, Barnhart C, De Almeida Correia GH (2015a) Assessing the viability of enabling a round-trip carsharing system to accept one-way trips: application to Logan Airport in Boston. Transp Res Part C: Emerg Technol 56:359–372
Jorge D, Correia G (2013) Carsharing systems demand estimation and defined operations: a literature review. EJTIR 13:201–220
Jorge D, Correia G, Barnhart C (2012) Testing the validity of the MIP approach for locating carsharing stations in one-way systems. Proc-Soc Behav Sci 54:138–148
Jorge D, Molnar G, De Almeida Correia GH (2015b) Trip pricing of one-way station-based carsharing networks with zone and time of day price variations. Transp Res B Methodol 81:461–482
Katzev R (2003) Car sharing: a new approach to urban transportation problems. Anal Soc Issues Publ Policy 3:65–86
Kek AG, Cheu RL, Chor ML (2006) Relocation simulation model for multiple-station shared-use vehicle systems. Transp Res Rec: J Transp Res Board 1986:81–88
Kek AG, Cheu RL, Meng Q, Fung CH (2009) A decision support system for vehicle relocation operations in carsharing systems. Transp Res Part E: Logist Transport Rev 45:149–158
Millard-Ball A (2005) Car-sharing: where and how it succeeds, Transportation Research Board
Nair R, Miller-Hooks E (2011) Fleet management for vehicle sharing operations. Transp Sci 45:524–540
Nair R, Miller-Hooks E, Hampshire RC, Bušić A (2013) Large-scale vehicle sharing systems: analysis of Vélib. Int J Sustain Transp 7:85–106
Nourinejad M, Roorda MJ (2014) A dynamic carsharing decision support system. Transp Res Part E: Logist Transp Rev 66:36–50
Prettenthaler FE, Steininger KW (1999) From ownership to service use lifestyle: the potential of car sharing. Ecol Econ 28:443–453
Shaheen SA, Cohen AP (2013) Carsharing and personal vehicle services: worldwide market developments and emerging trends. Int J Sustain Transp 7:5–34
Ter Schure J, Napolitan F, Hutchinson R (2012) Cumulative impacts of carsharing and unbundled parking on vehicle ownership and mode choice. Transportation Research Record: Journal of the Transportation Research Board, 96–104
Transportation Networks for Research Core Team (2018) Transportation Networks for Research [Online]. Available: https://github.com/bstabler/TransportationNetworks [Accessed April 7th 2018]
Uesugi K, Mukai N, Watanabe T (2007) Optimization of vehicle assignment for car sharing system. Knowledge-based intelligent information and engineering systems. Springer, 1105–1111
Weikl S, Bogenberger K (2015) A practice-ready relocation model for free-floating carsharing systems with electric vehicles–mesoscopic approach and field trial results. Transp Res Part C: Emerg Technol 57:206–223
Zipcar.com. (2016) A New Way to Zip | Zipcar [Online]. Available: http://www.zipcar.com/flexible [Accessed May 1st 2016]
Acknowledgements
The authors would like to thank Mr. Bruce Jeffreys and Ms. Rachel Moore from GoGet for providing data. We would also like to thank the Australian Research Council for their support under Linkage Grant # LP130100983.
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Jian, S., Rey, D. & Dixit, V. An Integrated Supply-Demand Approach to Solving Optimal Relocations in Station-Based Carsharing Systems. Netw Spat Econ 19, 611–632 (2019). https://doi.org/10.1007/s11067-018-9401-6
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DOI: https://doi.org/10.1007/s11067-018-9401-6