Analysis of bike sharing system by clustering: the Vélib’ case

Feng, Yun‐Long; Affonso, Roberta Costa; Zolghadri, Marc

doi:10.1016/j.ifacol.2017.08.2430

Cited by 22 publications

(18 citation statements)

References 13 publications

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“…The silhouette coefficient tests the quality of groupings based on dispersion in groups and the distance between them, but also provides the best information, from estimates between samples from different groups that are not taken into account. Thus, the Silhouette coefficient is also used to measure how close each point is in a cluster to the points in a neighboring group and measure the results of cluster group validation [21], [22] = distance between data i and j Then look for differences in the average of objects with equation 7…”

Section: Silhouette Coefficientmentioning

confidence: 99%

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Ghufron

Surarso

Gernowo

2020

kursor

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The need for data analysis in tertiary education every semester is needed, this is due to the increasingly large and uncontrolled data, on the other hand generally higher education does not yet have a data warehouse and big data analysis to maintain data quality at tertiary institutions is not easy, especially to estimate the results of university accreditation high, because the data continues to grow and is not controlled, the purpose of this study is to apply k-medoids clustering by applying the calculation of the weighting matrix of higher education accreditation with the data of the last 3 years namely length of study, average GPA, student and lecturer ratio and the number of lecturers according to the study program, so that it can predict accurate cluster results, the results of this study indicate that k-medoid clustering produces good cluster data results with an evaluation value of the Bouldin index davies cluster index of 0.407029478 and is said to be a good cluster result.

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Section: Silhouette Coefficientmentioning

confidence: 99%

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Ghufron

Surarso

Gernowo

2020

kursor

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“…Also, most studies conduct experiments on relatively small networks and the literature mostly simplifies and surveys the network as a vector graph. Few studies use clustering in order to reduce the complexity of large-scale optimization problem [8,10,13,15,23], while what is not considered in the most rebalancing studies is the users' behavior in bike sharing systems. Users' behavior is the result of the inherent interactions between demand and supply.…”

Section: Dynamic Rebalancing Problemmentioning

confidence: 99%

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

et al. 2019

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.Bike sharing systems are one of the effective and environmentally friendly solutions to the first/last mile problem; they notably reduce motorized trips, air pollution, fuel consumption and needs for parking spaces [1][2][3][4][5][6][7]. Since the last decade, these systems have become one of the main complementary modes of public transit systems in many urban areas. Different factors such as accessibility to stations, availability of bikes and vacant docks, and fares influence the effectiveness of these systems, among which the availability of bikes during the day is one of the most challenging issues in the designing, operating, and management of these services.To a large extent, bike sharing systems are used for one-way trips, and such a trend leads to inappropriate bike distribution in time and space [7,8]. As a result, unmet demand and consequently user dissatisfaction occur in the system. To minimize the demand dissatisfaction (i.e., maximize usage and revenue), the redistribution of bikes over stations is required. This redistribution, also called rebalancing, is implemented by employing vehicles (usually trucks) to collect surplus bikes from overloaded stations and deliver to bike deficient stations [9]. The efficiency of rebalancing operations is important to the authorities to ensure that the number of bikes and vacant docks at each station are periodically restored to predefined target values [8].Rebalancing...

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“…The other 17 stations are the most visited destination stations from these three stations. These stations are located in touristic zones and have a stable usage rate [46]. The data is collected for a period of 30 days, between the 5 of September and 15 of October 2016, excluding the weekends.…”

Section: Data Analysis and Hypothesismentioning

confidence: 99%

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

et al. 2018

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The Bike Sharing System is a sustainable urban transport solution that consists of a fleet of bikes placed in various stations. Users will be satisfied if they find available bikes at their departure station and free docks at the destination. Despite the regulation operations of the system provider (i.e., redistribution of bikes by truck) deeper modifications (bike fleet size or station capacity) are often necessary to ensure a satisfactory service rate. In this paper, we model a sub-graph of a Bike Sharing System using the closed queuing network with a Repetitive-Service-Random-Destination blocking mechanism. This model is solved using the Maximum Entropy Method. This model faithfully reproduces the system dynamics considering the limited capacity of stations. We analyze the performance, particularly, via an overall performance indicator of the system. The various control and monitoring decisions (fleet-size, capacity of stations, incoming and outgoing flow of bikes) are applied to find out the best performance levels. The results demonstrate that the overall performance is robust enough regarding the fleet size changes but it degrades with the increase of the stations’ capacity. Finally, the arrival and departure flows control is an efficient and powerful operational leverage.

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Analysis of bike sharing system by clustering: the Vélib’ case

Cited by 22 publications

References 13 publications

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

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