The Analysis of the Influencing Factors on the Problems of Bike-Sharing System in China

Wu, Shianghau; Lei, Xuxiunan

doi:10.1109/access.2019.2931988

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…These factors should also be prioritized when considering strategic action plans for the expansion of the BSS. In the study, the factors showed the highest factor of influence on BSSs, which is also confirmed by the research of Wu and Lei [82], Reddic et al [83], and Laa and Emberger [31].…”

Section: Discussionsupporting

confidence: 78%

External Environmental Analysis for Sustainable Bike-Sharing System Development

Macioszek

Cieśla

2022

Energies

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The paper introduces a discussion regarding the development of a public bike-sharing system, considering random factors, based on selected external environmental analysis methods. The global energy crisis is forcing scientists to continuously improve energy-efficient sustainable methods and scientific solutions. It is particularly important in transportation since transport activities and the constant increase in the number of vehicles have a large share in global energy consumption. The following study investigates the social, technological, economic, environmental, and political aspects of bike-sharing systems in cities. The research purpose of the article is to select the most important macro-environmental factors and their mutual interaction influencing the sustainable development of bike-sharing systems based on the Polish cities case study. The evaluation was carried out through expert methods with STEEP environmental analysis, evaluation of factors with the weighted score, and structural analysis method with MICMAC computer application. The classification of key factors influencing the development of a bike-sharing system has divided them into five groups. It can support public transport service providers and organizers. This can optimize the planning process with decision-making based on future environmental trends.

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Section: Discussionsupporting

confidence: 78%

External Environmental Analysis for Sustainable Bike-Sharing System Development

Macioszek

Cieśla

2022

Energies

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“…Many researchers have thoroughly reviewed BSS. Fishman et al 13 analyzed barriers to bike‐sharing in Melbourne and Brisbane, the same research has carried out by Wu et al 14 for the bike‐sharing system in China with the use of association rule mining. Matrai et al 3 had conducted a literature review on public bike‐sharing.…”

Section: Related Workmentioning

confidence: 99%

Mobility prediction for uneven distribution of bikes in bike sharing systems

Shir

Verma

Bhattacharya

2022

Concurrency and Computation

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User mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike-sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C-means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real-world scenarios.

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“…, n. The second one is a MVRPSDP subproblem to minimize the total travel cost of vehicles with the determined X t and Y t . Consequently, this section proposes an efficient two-stage heuristics to generate a near-optimal solution to the problem P. In the first stage of the algorithm, a relaxed model with (1)-(3) is established as a mutli-location newsvendor model with supply capacity constraint in Equations (2) and recall capacity constraints at hotspots in Equation (3). The relaxed model is resolved by a numerical approach combined with a heuristic rule to the newsvendor model.…”

Section: Heuristics For a Near-optimal Solution A Main Frameworkmentioning

confidence: 99%

“…. , n by reducing the model to n independent newsvendor problems [33], [34] without constraints (2) and (3). As addressed in literature [33], [34], [36] on the newsvendor problem, the optimal for a hotspot i, i = 1, 2, .…”

Section: B Subalgorithm In the First Stagementioning

confidence: 99%

“…A traveler can use a smartphone App to rent (i.e, unlock) a shared bike around his/her current position, ride it for a short period and return (i.e., lock) it near his/her destination [1]. Recently, the fourth-generation of dockless bikesharing systems (e.g., ''Mobike'' and ''OfO'' Apps) have become a common sight in many large cities in China, such as Beijing, Shanghai and Chengdu [2], [3]. Compared with the earlier generations of bike-sharing system, the dockless system allows travelers to rent and return bikes arbitrarily at any places within a region.…”

Section: Introductionmentioning

confidence: 99%

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Optimization Model and Algorithm for Dockless Bike-Sharing Systems Considering Unusable Bikes in China

Teng

Zhang

et al. 2020

IEEE Access

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The damaged dockless shared-bikes are increasingly common in large cities of China, which not only reduce users' satisfactions but also harm the public environment. It is a merging issue for dockless bike-sharing systems to timely recall unusable bikes and replenish new/usable bikes. This paper addresses a maintenance network for a dockless bike-sharing system within a specific region in which the random demands and returns of shared-bikes are considered and the returned bikes have a certain probability to be unusable. The problem is to jointly determine the numbers of unusable and usable bikes to be recalled and replenished, respectively, and the corresponding routes of maintenance vehicles via the maintenance network. The problem is formulated as a stochastic mixed-integer programming model with the objective to maximize the expected revenue of the maintenance network within a operational period. To resolve the such complicated integration-optimization problem efficiently, the formulated model is decomposed into a multilocation newsvendor subproblem and a multiple vehicle routing subproblem with simultaneous delivery and pickup. An efficient two-stage heuristics is then proposed, which resolves the two subproblems in the two stages of the algorithm, respectively. The computational results based on an real numerical case and a large number of numerical instances validate the performance of the proposed model and algorithm.

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The Analysis of the Influencing Factors on the Problems of Bike-Sharing System in China

Cited by 7 publications

References 27 publications

External Environmental Analysis for Sustainable Bike-Sharing System Development

External Environmental Analysis for Sustainable Bike-Sharing System Development

Mobility prediction for uneven distribution of bikes in bike sharing systems

Optimization Model and Algorithm for Dockless Bike-Sharing Systems Considering Unusable Bikes in China

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