Predictive user-based relocation through incentives in one-way car-sharing systems

Stokkink, Patrick; Geroliminis, Nikolas

doi:10.1016/j.trb.2021.05.008

Cited by 41 publications

(10 citation statements)

References 26 publications

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“…The problem is reformulated as a k‐disjoint shortest path problem , solved by an exact algorithm, and a real case study is taken from Car2go in Vancouver (Canada). Moreover, the success of a user‐based VR policy also depends on the availability of the vehicles, for example, close to the origin/destination of a trip as observed in Stokkink and Geroliminis (2021). To properly address this aspect, in an OW ECSS, they propose a predictive user‐based VR policy, i.e., a Markov chain‐based model for predicting user‐based relocation tasks, maximizing the expected additional profit due to the incentives offered to users.…”

Section: State‐of‐the‐art Of Classic Optimization Problems In Csssmentioning

confidence: 99%

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

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This paper reviews the most recent literature on the optimization of car‐sharing systems. Unlike other surveys, we do not focus on a single aspect of car‐sharing systems, but we consider a wide range of optimization problems with a global view. Our aim is threefold. First, we revise the classic decision problems arising in car‐sharing systems with conventional and electric vehicles, such as the fleet size, the station location, and the vehicle relocation. Then, we discuss some of the most recent decision problems arising in more innovative car‐sharing systems, like those with autonomous vehicles, with mixed fleets, and the multi‐modal and the time‐dependent ones. Finally, we focus on new perspectives arising in the optimization of some innovative car‐sharing services that are not yet (or little) investigated in the literature and that are worthy of attention from the operations research point of view. For each of them, we also highlight new and challenging open decision problems that deserve academic attention.

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Section: State‐of‐the‐art Of Classic Optimization Problems In Csssmentioning

confidence: 99%

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

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“…The majority of the literature focuses on incentives to convince the user to change the origin or the destination. Stokkink et al [14] proposed a user-based relocation strategy based on the current distribution of vehicles and expected future demands, also at the single-user level. The customers are stimulated to relocate the vehicles from over-saturated locations to under-saturated locations, through discounted prices.…”

Section: User-based Relocationmentioning

confidence: 99%

Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits

et al. 2022

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In many developed cities around the world, vehicle sharing is becoming an increasingly popular form of green transportation. While such services are associated with lower emissions and easier mobility, their management poses a significant challenge. In this paper, we examine a dataset collected in Barcelona during the months of august and september 2020 in order to investigate relocation strategies and user clustering. By proposing a neighborhood area split and relating it to user demand, we propose two different areas based on majority demand and users’ requests and provide interpretations of both. We then aim to identify groups of similar users using a variant of Recency Frequency Monetary/Duration (RFM or RFD) clustering that extends to GPS coordinates of voyages in order to differentiate scores based on economic and geographical factors; furthermore, a user-based clustering approach was used to maximize client preferences. As a result of our analysis, the sharing company may be able to make more informed decisions regarding where to focus its resources. In fact, we find that the majority of the demand is concentrated in an area that represents 7.47 percent of the city’s area. Additionally, we propose a discount-based approach in order to influence the user’s behavior in parking the vehicle where it is most needed.

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“…This suggests that the cost can be further reduced by using a pricing policy that adapts the reward to the actual detour, rather than the maximum detour. However, this raises the concern of truthfulness in the reporting behaviour of crowd-shippers which has been a concern for many incentive-based policies in mobility systems (Asghari and Shahabi, 2017;Stokkink and Geroliminis, 2021).…”

Section: Comparison Of Assignment Policies For Endogenous Supplymentioning

confidence: 99%

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

Stokkink¹,

Geroliminis²

2022

Preprint

Self Cite

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Last-mile delivery in the logistics chain contributes to emissions and increased congestion. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery, but relies heavily on the availability of occasional couriers. In this work, we propose a hub-based crowd-shipping system that aims to attract sufficient potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, a scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of hub locations. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we are able to efficiently find a good set of hub locations, even for large scale networks. Furthermore, on top of determining good hub locations, our methods allow to identify the expected number of delivered parcels in every region, which can be used to design a smart dynamic assignment strategy. A case study on the Washington DC network shows that hubs are built at locations that are both geographically central, but most importantly are popular origins for crowd-shippers. The optimal number of hubs is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific hub, relative to the costs involved in opening that hub. The performance of our algorithm is close to that of a simulation-optimization algorithm, yet being up to 25 times faster. Thereby, the results show a dynamic assignment policy based on continuum approximation estimates outperforms existing assignment strategies.

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Predictive user-based relocation through incentives in one-way car-sharing systems

Cited by 41 publications

References 26 publications

A survey on emergent trends in the optimization of car‐sharing systems

A survey on emergent trends in the optimization of car‐sharing systems

Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

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