[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

Peng, Yali; Liang, Ting; Hao, Xiao‐Jiang; Chen, Yu; Li, Shicheng; Yi, Yugen

doi:10.1155/2021/5486328

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…Linear Model [185,186,187] Tree-based Ensembles [184,186,188,189] Neural Networks [190,191,192] Scooter Dockless OD + Neural Networks [193] For example, demand in one area can be affected by traffic in other areas, and external factors such as weather, events, and holidays can have an impact on demand throughout all regions. Despite significant research in traffic forecasting, spatio-temporal forecasting remains an area of ongoing study.…”

Section: Generalizedmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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Section: Generalizedmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…When applied to cyclist mobility, deep learning and CNN have been used in the bike-sharing prediction modeling, because the use of shared bicycles is susceptible to time dependence and external factors [ 44 ], such as weather [ 4 , 47 ], bike rebalancing and land use characteristics. In [ 14 ], authors applied the Self Organizing Map artificial neural network to identify atypical trajectories from video sequences at fixed locations.…”

Section: Related Backgroundmentioning

confidence: 99%

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Yaqoob

Cafiso

Morabito

et al. 2023

Eur. Transp. Res. Rev.

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Background Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach “BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user’s trained models. Conclusion The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

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“…RNN with LSTM is found to perform well when compared with conventional artificial neural networks and RNN. [39] have developed a model for predicting sales at three different levels, Item, day, and store levels. It employs RNN and Transformers and also introduced the concept that using dynamic time for learning to avoid overfitting.…”

Section: Comparative Study Of Deep Learning Modelsmentioning

confidence: 99%

A Comprehensive Study of Data Analytic Techniques for Sales Forecasting

2023

Recent Trends in Data Science and Its Applications

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Predicting the sales of a product or demand for a service is an utmost need in any industry. With the changes and increase in the channels and modes through which the products are sold, the prediction of sales in the future becomes tedious. The number of features that would help in predicting sales differs and increases. This leads to the usage of Machine Learning and Deep Learning Models in sales forecasting. A deep survey of various works that employ machine learning and Deep learning approaches for sales prediction is done and inferences are made. Every work studied has a list of models which are used as baseline models for comparison and the same is listed for better inferences. Works that address the problem of predicting the sales of new products are also discussed. A list of publicly available sales forecasting datasets is also given.

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[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

Cited by 11 publications

References 34 publications

A Systematic Literature Review on Machine Learning in Shared Mobility

A Systematic Literature Review on Machine Learning in Shared Mobility

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

A Comprehensive Study of Data Analytic Techniques for Sales Forecasting

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