Trip pricing of one-way station-based carsharing networks with zone and time of day price variations

Jorge, Diana; Molnar, Goran; Correia, Gonçalo Homem de Almeida

doi:10.1016/j.trb.2015.06.003

Cited by 164 publications

(48 citation statements)

References 50 publications

(66 reference statements)

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“…Along the evolution process, companies faced management problems and research started addressing these by introducing models to analyze and solve these issues (de Lorimier & El-Geneidy, 2013). One of the most relevant, in one-way carsharing systems management, occurs with the imbalance that results from such systems and there is a considerable body of work on studying ways to solve this problem mainly through simulation and optimization techniques (Barth & Todd, 1999;Barth, Todd, & Xue, 2004;Correia & Antunes, 2012;Fan, Machemehl, & Lownes, 2008;Jorge, Molnar, & de Almeida Correia, 2015;Kek, Cheu, & Chor, 2006). They either use travelers as a balancing factor, a staff of drivers Kek, Cheu, Meng, & Fung, 2009;Weikl & Bogenberger, 2013), or even changing the network of stations for obtaining a more favorable balanced trip pattern (Correia & Antunes, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon, Portugal

Martínez

Correia

Moura

et al. 2016

International Journal of Sustainable Transportation

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Two important claims for carsharing systems are their increased flexibility and potential contribution to reducing transport externalities such as pollution. Carsharing typically involves a fleet of vehicles in stations around a city that clients may use on an hourly-payment basis. Classical round-trip systems address a niche market of shopping and errand trips. However, a growing market is now arising providing one-way trips to clients. Great uncertainty remains on the economic viability of this type of carsharing given the complex relation between supply and demand, and how this may influence the level of service provided. Realistic modeling tools that include both supply and demand characterization and allow testing several carsharing operational parameters are scarce. In this sense, a detailed agent-based model was developed to simulate one-way carsharing systems. The simulation incorporates a stochastic demand model discretized in time and space and a detailed environment characterization with realistic travel times. The operation includes maintenance operations, relocations and reservations. The model was applied to the case-study city of Lisbon. Our results show that comparing to other modes, carsharing performs worse than private cars both in terms of time and cost. Nevertheless, it clearly outperforms taxis in terms of cost, and outperforms buses, metro and walking in terms of travel time. The competitiveness of carsharing is highly determined by trip length, becoming more competitive than other modes (traveltime wise) as trips become longer. The operational policies as car-fleet relocation and car reservation showed significant effects in enhancing profit while preserving good customers' satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon, Portugal

Martínez

Correia

Moura

et al. 2016

International Journal of Sustainable Transportation

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“…She states that "carsharing provides a flexible alternative that meets the diverse transportation needs across the globe while reducing the negative impacts of private vehicle ownership." Automation is a game changer for car-sharing, various studies from Correia and Antunes (2012), Jorge and Correia (2013), Jorge et al (2015) show the importance of the fact that automation enables providing this mode at a lower price since the system can be operated without the high costs and management complexity of relocating vehicles in the city, which currently has to be done with normal cars.…”

Section: Carsharingmentioning

confidence: 99%

Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics

Nieuwenhuijsen

Correia

Milakis

et al. 2018

Transportation Research Part C: Emerging Technologies

108

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This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named "AV in-bloom". In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an 'AV in bloom' ecosystem for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.

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“…A mixed integer programming (MIP) model was developed to determine the optimum sequence of pick-up and delivery operations that should be performed by vehicle-relocation personnel. Jorge et al (2015) presented a mathematical programming model for maximizing profitability of a carsharing system optimizing relocation operations for one-way carsharing system. In addition a discrete-event time-driven simulation model was proposed to evaluate alternative relocation policies.…”

Section: Introductionmentioning

confidence: 99%

“…The solution of the VRO and PIO determine the customer requests that will be satisfied, the vehicles that will be relocated, and the time that these relocations will take place. Jorge et al (2015) influenced vehicle imbalances by integrating a pricing scheme with a demand choice model that charges high prices for the trips that contribute to imbalances and lower prices for the ones that bring the system to more equilibrium states. This analysis showed that pricing without any relocation strategies can improve the balance of the system, but with less demand served, which might create less attractiveness in the service.…”

Section: Introductionmentioning

confidence: 99%

An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations

Boyacı

Zografos

Geroliminis

2017

Transportation Research Part B: Methodological

186

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a b s t r a c tOne-way electric vehicle carsharing systems are receiving increasing attention due to their mobility, environmental, and societal benefits. One of the major issues faced by the operators of these systems is the optimization of the relocation operations of personnel and vehicles. These relocation operations are essential in order to ensure that vehicles are available for use at the right place at the right time. Vehicle availability is a key indicator expressing the level of service offered to customers. However, the relocation operations, that ensure this availability, constitute a major cost component for the provision of these services. Therefore, clearly there is a trade-off between the cost of vehicle and personnel relocation and the level of service offered. In this paper we are developing, solving, and applying, in a real world context, an integrated multi-objective mixed integer linear programming (MMILP) optimization and discrete event simulation framework to optimize operational decisions for vehicle and personnel relocation in a carsharing system with reservations. We are using a clustering procedure to cope with the dimensionality of the operational problem without compromising on the quality of the obtained results. The optimization framework involves three mathematical models: (i) station clustering, (ii) operations optimization and (iii) personnel flow. The output of the optimization is used by the simulation in order to test the feasibility of the optimization outcome in terms of vehicle recharging requirements. The optimization model is solved iteratively considering the new constraints restricting the vehicles that require further charging to stay in the station until the results of the simulation are feasible in terms of electric vehicles' battery charging levels. The application of the proposed framework using data from a real world system operating in Nice, France sheds light to trade-offs existing between the level of service offered, resource utilization, and certainty of fulfilling a trip reservation.

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Trip pricing of one-way station-based carsharing networks with zone and time of day price variations

Cited by 164 publications

References 50 publications

Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon, Portugal

Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon, Portugal

Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics

An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations

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