Dockless Shared-Bike Demand Prediction with Temporal Convolutional Networks

Jin, Kun; Wang, Wei; Li, Shuang; Liu, Pei; Sun, Heyang

doi:10.1061/9780784483053.240

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…set of selected physical stations in a docked bike system or a region in a dockless bike system 2 . Based on historical usage patterns in different stations/locations, clusteringbased algorithms are often used for defining virtual stations, e.g., the fuzzy c-means clustering algorithm [66] and the distance-based clustering algorithm [32]. All trips with a start station/location belonging to the same virtual station are counted as a data sample.…”

Section: Data Aggregation Typesmentioning

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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Section: Data Aggregation Typesmentioning

confidence: 99%

Bike sharing usage prediction with deep learning: a survey

Jiang

2022

Neural Comput & Applic

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“…Xu et al [1] adopted a long-short term memory neural network (LSTM) with exogenous factors as additional input. Jin et al [2] applied temporal convolution networks (TCNs) to capture temporal dependencies for bike sharing demand prediction. To incorporate spatial information, previous research employed convolutional neural networks (CNNs) with recurrent networks [22], [10], which however, require aggregating demand data to a grid system.…”

Section: A Bike Sharing Demand Predictionmentioning

confidence: 99%

“…Early attempts use regression or machine learning models to solve this problem, which suffer from relatively low accuracy. Recently, more research interests have shifted to deep learning methods, due to their demonstrated effectiveness in extracting the complex knowledge hidden in large-scale mobility data [1], [2]. In particular, Graph Neural Networks (GNNs) have been employed in the demand prediction problem and achieved state-of-the-art performance [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks

Liang¹,

Guan²,

Zhan³

2022

Preprint

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For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transportation modes. This is particularly important for bike sharing because it is often used to complement travel through other modes (e.g., public transit). Despite some recent progress, no existing method is capable of leveraging spatiotemporal information from multiple modes and explicitly considers the distribution discrepancy between them, which can easily lead to negative transfer. To address these challenges, this study proposes a domain-adversarial multirelational graph neural network (DA-MRGNN) for bike sharing demand prediction with multimodal historical data as input. A temporal adversarial adaptation network is introduced to extract shareable features from demand patterns of different modes. To capture correlations between spatial units across modes, we adapt a multi-relational graph neural network (MRGNN) considering both cross-mode similarity and difference. In addition, an explainable GNN technique is developed to understand how our proposed model makes predictions. Extensive experiments are conducted using real-world bike sharing, subway and ride-hailing data from New York City. The results demonstrate the superior performance of our proposed approach compared to existing methods and the effectiveness of different model components.

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Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

Zhao,

Wu,

Zhang

et al. 2023

Urban Rail Transit

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The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.

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Dockless Shared-Bike Demand Prediction with Temporal Convolutional Networks

Cited by 4 publications

References 22 publications

Bike sharing usage prediction with deep learning: a survey

Bike sharing usage prediction with deep learning: a survey

Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks

Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

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