ST-Bikes: Predicting Travel-Behaviors of Sharing-Bikes Exploiting Urban Big Data

Chai, Jun; Song, Junfeng; Fan, Hongwei; Xu, Yinlong; Zhang, Le; Guo, Bing; Xu, Yuhong

doi:10.1109/tits.2022.3197778

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…Specifically, Bai et al use a cascade graph convolutional recurrent neural network to extract spatio-temporal correlations between data and two multilayer LSTM networks to represent external meteorological data and time meta separately [26]. Chai et al produce a multi-view spatio-temporal framework to combine characteristics into one prediction model framework of predicting the bike-sharing demand [27]. Alternatively, some scholars have integrated GCN and attention mechanisms in a natural way to tackle the issue of incorporating irrelevant stations' features in the prediction process because of inadequate or erroneous prior knowledge [28][29][30][31].…”

Section: Plos Onementioning

confidence: 99%

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Yang,

Wu,

Wang

et al. 2024

PLoS ONE

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Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness of user behavior, which makes the distribution of bikes unbalanced. These bring great challenges to bike-sharing demand prediction. In this study, a usage pattern similarity-based dual-network for bike-sharing demand prediction, called FF-STGCN, is proposed. Inter-station flow features and similar usage pattern features are fully considered. The model includes three modules: multi-scale spatio-temporal feature fusion module, bike usage pattern similarity learning module, and bike-sharing demand prediction module. In particular, we design a multi-scale spatio-temporal feature fusion module to address limitations in multi-scale spatio-temporal accuracy. Then, a bike usage pattern similarity learning module is constructed to capture the underlying correlated features among stations. Finally, we employ a dual network structure to integrate inter-station flow features and similar usage pattern features in the bike-sharing demand prediction module to realize the final prediction. Experiments on the Citi Bike dataset have demonstrated the effectiveness of our proposed model. The ablation experiments further confirm the indispensability of each module in the proposed model.

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Section: Plos Onementioning

confidence: 99%

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Yang,

Wu,

Wang

et al. 2024

PLoS ONE

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Sustainable development through digital innovation: Unveiling the impact of big data comprehensive experimental zones on energy utilization efficiency

Xue,

Cai,

Liu

et al. 2024

Sustainable Development

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Efficient energy utilization stands as a linchpin for national energy security and sustainable development, particularly in developing countries. The transformative potential of big data in driving regional economic growth is pivotal for advancing the goals of sustainability and carbon neutrality. This study employs advanced Generalized Difference‐in‐Differences methods to investigate the enduring influence of establishing national Big Data Comprehensive Experimental Areas (BDCEAs) on energy utilization efficiency within the experimental zones from 2008 to 2020. Our findings underscore the significant role of BDCEAs in fostering sustainable energy practices and contributing to the global pursuit of harmonious coexistence between humanity and nature. Through rigorous analysis, we reveal regional variations, emphasizing the substantial impact of big data on energy utilization efficiency in specific areas, while also exploring the underlying mechanisms of industrial structure upgrading, technological innovation, and resource allocation optimization. This research enriches the understanding of factors influencing energy utilization efficiency, providing valuable insights for leveraging the digital economy to promote a greener and more sustainable future.

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The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

Zhang,

Wang,

Zang

et al. 2024

Sustainability

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Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This paper conducts a systematic review of the intelligent transportation literature using a scientometric analysis, aiming to summarize what is already known, identify current research trends, evaluate academic impacts, and suggest future research directions. The study provides a detailed review by analyzing 113 journal articles from the Web of Science (WoS) database. It examines the growth of publications over time, explores the collaboration patterns of key contributors, such as researchers, countries, and organizations, and employs techniques such as co-authorship analysis and keyword co-occurrence analysis to delve into the publication clusters and identify emerging research topics. Nine emerging sub-topics are identified and qualitatively discussed. The outcomes include recognizing pioneering researchers in intelligent transportation for potential collaboration opportunities, identifying reliable sources of information for publishing new work, and aiding researchers in selecting the best solutions for specific problems. These findings help researchers better understand the application of ML and DL in the intelligent transportation literature and guide research policymakers and editorial boards in selecting promising research topics for further research and development.

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ST-Bikes: Predicting Travel-Behaviors of Sharing-Bikes Exploiting Urban Big Data

Cited by 3 publications

References 45 publications

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Sustainable development through digital innovation: Unveiling the impact of big data comprehensive experimental zones on energy utilization efficiency

The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

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