Central Station Based Demand Prediction in a Bike Sharing System

Huang, Jianbin; Wang, Xiangyu; Sun, Heli

doi:10.1109/mdm.2019.00-38

Cited by 7 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…This density-based clustering method can adaptively divide an appropriate number of clustered communities by setting different bandwidths [20]. Unlike the previous clustering communities built using bicycle-sharing travel points [13], we borrowed the concept of "central station" [19] and used the Mean-shift clustering algorithm to cluster the electronic fence parking points, and combined it with existing dispatching methods to build a dispatching station and delineate the dispatching range. In this way, we can enhance bicycle-sharing dispatching, improving dispatching efficiency and ensuring a balance between supply and demand, further enhancing bicycle-sharing systems' sustainability.…”

Section: Discussionmentioning

confidence: 99%

Section: Electronic Fence Parking Spot Clusteringmentioning

confidence: 99%

“…To ensure data validity, we choose to retain electronic fences that are used more than twice a day. We do not consider scheduling problems, as electronic fence parking points with low usage frequencies have a minimal impact on scheduling [19]. Mean-shift clustering is used to cluster the electronic fence parking points as the clustering feature, and the bandwidth is referenced to the distance of the bicycle-sharing DO points (Figure 1).…”

Section: Electronic Fence Parking Spot Clusteringmentioning

confidence: 99%

See 3 more Smart Citations

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

Lei,

Zhang,

Ren

2023

Sustainability

View full text Add to dashboard Cite

Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages of low cost and sustainability. Bicycle-sharing operators use electronic fence parking points to manage bicycle-sharing, but it is too time-consuming and impractical to manage them individually. Therefore, it is necessary to cluster the electronic fence parking points and implement regionalized management. This study uses a Mean-shift clustering algorithm to cluster the electronic fence parking points on Xiamen Island, construct a bicycle-sharing dispatching station, and divide the management area. Singular value decomposition is employed to analyze the travel demand patterns of bicycle-sharing and travel characteristics. In addition, we constructed regression models to explore the relationship between the urban built environment and bicycle-sharing trips during the morning and evening peak hours. The study results show that: (1) The 73 dispatching stations constructed cover 86.72% of the bicycle-sharing data, with a good clustering effect. (2) The travel demand for bicycle-sharing shows multiple patterns and different characteristics in different spatial and temporal distributions, which are influenced by land use. (3) There are spatial and temporal differences in the impact of the urban built environment on bicycle-sharing trips, especially residential and enterprise poi densities have opposite effects on shared bicycle-sharing during morning and evening peak hours. The research results of this paper can serve in the planning of bicycle-sharing dispatch stations and the differentiated management and dispatch of bicycle-sharing, which can reduce operating costs and promote the development of sustainable urban transport.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Electronic Fence Parking Spot Clusteringmentioning

confidence: 99%

Section: Electronic Fence Parking Spot Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

Lei,

Zhang,

Ren

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…For example, Chen et al [23] built a weighted correlation network to support the application of geographically-constrained clustering for overdemand cluster prediction. Similarly, Huang et al [24] further proposed a Two-Stage Station Clustering algorithm to cluster the central stations and common stations before predicting. In summary, the identification of the spatialtemporal zones is the basis of demand prediction.…”

Section: A Bicycle-sharing Demand Predictionmentioning

confidence: 99%

An Intelligent Rebalance System for Tidal Phenomenon of Dockless Bicycle-Sharing

Liao

Huang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the advantages of flexible parking locations and convenient cycling, Dockless Bicyclesharing (DBS) has become increasingly popular worldwide. However, with the massive increase of DBSs and electric fences, DBS systems face several challenges: (1) the hardness of identifying the DBS tidal zones; (2) the difficulty of accurately evaluating and identifying overload fences; (3) the issues of rebalancing DBS in time. To deal with these challenges, we propose a Dockless Bicycle-sharing Dynamic Rebalance (DBSDR) system to dynamically provide the optimal bicycle guidance for the DBS network. The DBSDR system contains three modules: DBS tidal zone identification, evaluation framework of overload fences, and DBS dynamic guidance. For DBS tidal zone identification, tidal zone identification and location from each fence with bicycle flows are provided with the HDBSCAN clustering method. The evaluation framework, covering DBS flows and the parking demand density, is proposed to assess the characteristics of overload fences. Finally, a DBS dynamic guidance method is provided to balance DBS for the tidal phenomenon with guiding users to the optimal target fence. Extensive experiments conducted on real-world DBS datasets show the effectiveness and accuracy of rebalancing the tidal phenomenon in the DBS system.INDEX TERMS Dockless Bicycle-sharing system, bicycle rebalancing, HDBSCAN, user-based guidance.

show abstract

Monte carlo tree search for dynamic bike repositioning in bike-sharing systems

Huang

Tan

et al. 2021

Appl Intell

View full text Add to dashboard Cite

Central Station Based Demand Prediction in a Bike Sharing System

Cited by 7 publications

References 4 publications

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

An Intelligent Rebalance System for Tidal Phenomenon of Dockless Bicycle-Sharing

Monte carlo tree search for dynamic bike repositioning in bike-sharing systems

Contact Info

Product

Resources

About