RETRACTED ARTICLE: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

Ai, Yi; Li, Zongping; Gan, Mi; Zhang, Yunpeng; Yu, Daben; Chen, Wei; Ju, Yanni

doi:10.1007/s00521-018-3470-9

Cited by 145 publications

(93 citation statements)

References 17 publications

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“…This is the reason for introducing the IF THEN ELSE function. In order to make readers understand better, Equations (9) and (10) were not further simplified:…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Several authors have conducted research on DBSPs and have focused mainly on big data and machine learning applications (e.g., the scheduling efficiency of DBSPs [9][10][11], user travel forecast [12,13], electronic fence planning [14][15][16], and changes of travel mode [17,18]); user behavior [19,20]; environmental benefits [21,22]; and the overall development of the industry [23][24][25]. Note that these studies add complexity to this field.…”

Section: Introductionmentioning

confidence: 99%

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System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

Yang

Zhou

2019

Sustainability

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In China, dockless bike-sharing programs (DBSPs) have changed people’s travel modes, alleviated urban traffic congestion, and reduced carbon emissions. However, a number of DBSPs have experienced financial crises since 2018. This means that research on DBSPs must be considered not only in terms of the environment and technology, but also in the operation of the program. In this paper, we modeled the DBSP operations in a certain area using a system dynamics simulation approach. The main purpose was to explore the dynamics of the program’s operation process and evaluate possible improvement strategies for maximizing the revenue of the overall DBSP. Specifically, the analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. The research findings revealed that the dockless bike-sharing industry has great economic profits, but in the current environment, the market needs to be regulated by the local government. If a DBSP does not introduce new technologies or find new profit channels, it will be difficult to develop sustainably by only relying on riding profits. In addition, we provide a case study of Mobike’s operations in Beijing to support these findings and validate the developed model. Finally, we discuss Mobike’s possible improvement strategies.

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“…This is the reason for introducing the IF THEN ELSE function. In order to make readers understand better, Equations (9) and (10) were not further simplified:…”

Section: Mathematical Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

Yang

Zhou

2019

Sustainability

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“…The analysis of travel characteristics of FFBS users played an important role in FFBS introduction and redistribution. For travel distance and travel duration, the riding distance was concentrated in 0.8~2.8 km (mean was 2.3 km), the riding duration was concentrated in 10~25 min (mean was 20 min) [21]. With regard to travel purpose and the time period of travel, FFBS was mainly used for commuting to work, attending school, shopping, and entertainment [5,22], and the travel peaks appeared at 7:00~9:00, 12:00~14:00, and 17:00~19:00 [5,[21][22][23].…”

Section: Travel Characteristic Of Usersmentioning

confidence: 99%

“…For travel distance and travel duration, the riding distance was concentrated in 0.8~2.8 km (mean was 2.3 km), the riding duration was concentrated in 10~25 min (mean was 20 min) [21]. With regard to travel purpose and the time period of travel, FFBS was mainly used for commuting to work, attending school, shopping, and entertainment [5,22], and the travel peaks appeared at 7:00~9:00, 12:00~14:00, and 17:00~19:00 [5,[21][22][23]. Regarding the travel position, Shen et al [15] concluded that FFBS was primarily concentrated in the peripheral residential areas with high population density and access to the mass rapid transit by analyzing the GPS data of dockless bikes in Singapore.…”

Section: Travel Characteristic Of Usersmentioning

confidence: 99%

Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China

Cheng

et al. 2019

Sustainability

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In recent years, app-based third taxi service (ATTS) and free-floating bike sharing (FFBS) have become significant travel modes to satisfy travel demands of urban residents. In order to explore the mechanism of their modes selection, firstly, based on 595 valid samples, differences between ATTS and FFBS in original modes, travel distance, geographical position distribution, and travel emergency degree were compared. Then, a multinomial logistic model was established to investigate the factors influencing the choice among ATTS, FFBS, and traditional travel modes (TTM). The results show that: (1) FFBS attracts users whose original modes are walking, private bicycle and bus, while ATTS has a certain competition effect on cruising taxi and bus. (2) Residents are more likely to change from bus to FFBS on weekends, while this competitive relationship between ATTS and bus has no significant difference in different dates. (3) Compared with TTM, residents are more inclined to utilize shared modes to participate in flexible activities, especially in suburb. (4) Interestingly, ATTS is more likely to be utilized in emergency travel, and the residents without registered permanent residences tend to use FFBS and ATTS. Finally, some suggestions and policies were proposed for the government and enterprises to improve operation managements.

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“…Background. Deep learning has been proven to be a powerful tool for pattern classification problems and sensor studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A deep learning model usually has more than three layers, and by using multiple layers, the model extracts hierarchical features from the original data.…”

Section: Introductionmentioning

confidence: 99%

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Liu

Yan

et al. 2020

Computational Intelligence and Neuroscience

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Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

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RETRACTED ARTICLE: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

Cited by 145 publications

References 17 publications

System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

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