A new dynamic repositioning approach for bike sharing systems

Jiménez, Enrique; Soriguera, Francesc

doi:10.1016/j.trpro.2020.03.093

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The average fraction of users that can take/return a bike immediately upon arrival obtained via simulation-optimization is 90%. This value is similar to the best results found in Datner et al (2019) and greater than the results in Jiménez and Soriguera (2020) for smaller BSSs Ecobici. The performance of Sim-Opt on the average-user waiting time is almost the same as the Sim Profile-based policies.…”

Section: Initial Inventory In Bssssupporting

confidence: 89%

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Simulation analysis of initial inventory in BSSs

Ramírez–Nafarrate

Moncayo–Martínez

Munguía-Williams

2021

JM2

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Purpose This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand. Design/methodology/approach This paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system. Findings The findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results. Research limitations/implications The findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific. Practical implications The insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand. Social implications Increasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles. Originality/value This paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

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Section: Initial Inventory In Bssssupporting

confidence: 89%

“…Recent works include Jiménez and Soriguera (2020), who developed a simulation-based approach to test preventive and reactive repositioning strategies. Analyzing the BSS from Barcelona, they found that reposition operations based on mixed routes, i.e.…”

Section: Initial Inventory In Bsssmentioning

confidence: 99%

Simulation analysis of initial inventory in BSSs

Ramírez–Nafarrate

Moncayo–Martínez

Munguía-Williams

2021

JM2

View full text Add to dashboard Cite

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“…However, results showed that although the pricing strategy could improve the imbalance issue, it would reduce travelers' willingness to use shared bikes. Jiménez et al [ 30 ] combined the real-time changes in shared bike demand by optimizing the deployment paths. A station-based hybrid deployment model was proposed to effectively reduce the deployment cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations

Feng

Li³

et al. 2022

Urban Rail Transit

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The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.

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“…Soriguera et al [4] and Soriguera and Jiménez [13] are two of four papers that present a model for the optimal design of public BSS that also considers e-bikes. A model was also tested on a real case example in Barcelona with respect to traditional bikes.…”

Section: A Literature Review On the Design Of E-bssmentioning

confidence: 99%

A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes

Tuljak-Suban

Bajec

2022

Energies

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An e-bike sharing system (e-BSS) solves many of the shortcomings of BSS but requires high financial investments compared to BSS. This article proposes a sustainable and targeted extension of the existing BSS with e-bikes and charging piles. The existing BSS in the selected city area is divided into sub-areas using the Voronoi diagram and reference points (landmarks). Then, the integrated approach of the Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) is used to assess the adequacy of the existing bike-sharing stations for updating with e-bikes and charging piles. The joint approach allows decision-makers to look at the whole process and highlight the link between the criteria assessment and user preferences in the context of the chosen reference point. This can encourage future users to use e-BSSs. Based on a thorough literature review, the defined system of criteria takes into account all dimensions of sustainability: the requirements of most stakeholders and the structural features and needs of e-BSS. Finally, the super-efficiency DEA is used to classify the suitable candidates for bike-sharing so that only the most suitable stations are updated. The test of the proposed algorithm in Ljubljana city centre confirms several suitable options for updating the BSS, depending on the reference point.

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A new dynamic repositioning approach for bike sharing systems

Cited by 5 publications

References 7 publications

Simulation analysis of initial inventory in BSSs

Simulation analysis of initial inventory in BSSs

Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations

A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes

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