A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Cipriano, Marco; Colomba, Luca; Garza, Paolo

doi:10.3390/app11156967

Cited by 11 publications

(5 citation statements)

References 45 publications

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“…Demand prediction models are different from traditional time series analysis, as they consider both spatial and external factors. Association Rule Learning [182] Clustering [183,184]…”

Section: ) Demand Predictionmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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“…Demand prediction models are different from traditional time series analysis, as they consider both spatial and external factors. Association Rule Learning [182] Clustering [183,184]…”

Section: ) Demand Predictionmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…The imbalance of supply and demand is ubiquitous in bike share systems, especially in peak hours. The bike rebalancing problem is meaningful in both docked and dockless bike share systems and has been considered in previous studies [8,14,24,42,75]. With an effective rebalancing strategy, both the bike utilization rate and the revenue of the operator would increase.…”

Section: Applications Within Bike Share Systemsmentioning

confidence: 99%

Bike sharing usage prediction with deep learning: a survey

Jiang

2022

Neural Comput & Applic

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As a representative of shared mobility, bike sharing has become a green and convenient way to travel in cities in recent years. Bike usage prediction becomes more important for supporting efficient operation and management in bike share systems as the basis of inventory management and bike rebalancing. The essential of usage prediction in bike sharing systems is to model the spatial interactions of nearby stations, the temporal dependence of demands, and the impacts of environmental and societal factors. Deep learning has shown a great advantage of making a precise prediction for bike sharing usage. Recurrent neural networks capture the temporal dependence with the memory cell and gate mechanisms. Convolutional neural networks and graph neural networks learn spatial interactions of nearby stations with local convolutional operations defined for the grid-format and graph-format inputs respectively. In this survey, the latest studies about bike sharing usage prediction with deep learning are reviewed, with a classification for the prediction problems and models. Different applications based on bike usage prediction are discussed, both within and beyond bike share systems. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on bike sharing usage prediction with deep learning techniques.

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“…Mellou et al proposed a novel mixed-integer programming formulation to solve the dynamic rebalancing problem and provided a linear programming model to capture the bike flows from all trips [23,24]. A rebalancing framework for the dynamic bike sharing problem was presented in [25]. These methods are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach.…”

Section: Introductionmentioning

confidence: 99%

An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems

et al. 2022

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With a new generation of bike sharing services emerging, the development of dockless bike sharing services results in considerable socioeconomic and environmental benefits but also creates new issues, such as inappropriate parking behaviors and bike imbalances. To solve the inappropriate parking problem, electric fences have been introduced to guide users to park bikes in designated zones. Considering the role of electric fences in restricting user parking behaviors, an electric fence-based intelligent scheduling method for rebalancing dockless bike sharing systems is proposed in this paper. As a dynamic method that considers the real-time usage of bike sharing systems, an electric fence adjusts its capacity based on real-time information, which guides users to return bikes to electric fences with greater urgency. Because existing approaches require prespecified models and are unable to consider all the intricacies in the dynamic optimization problem, a model-free intelligent scheduling approach based on deep Q-learning that can adapt to the changing distributions of customer arrivals, available bikes, bike locations, and user travel times is used to solve the problem. Finally, a case study involving Beihang University is employed, which shows that the method performs well in rebalancing the bike sharing system and improving the mean utilization (MU) and customer satisfaction (CS).

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A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Cited by 11 publications

References 45 publications

A Systematic Literature Review on Machine Learning in Shared Mobility

A Systematic Literature Review on Machine Learning in Shared Mobility

Bike sharing usage prediction with deep learning: a survey

An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems

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