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Forecasting air passenger traffic flow based on the two-phase learning model

https://doi.org/10.1007/s11227-020-03428-2

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1099-1689(199909)9:3<191::aid-stvr184>3.0.co;2-eopenalex-snapshot

Machine learning. Tom M. Mitchell. Published by McGraw-Hill, Maidenhead, U.K., International Student Edition, 1997. ISBN: 0-07-115467-1, 414 pages. Price: U.K. £22.99, soft cover.

inferredcites →https://doi.org/10.1007/3-540-45014-9_1openalex-snapshot

Ensemble Methods in Machine Learning

inferredcites →https://doi.org/10.1007/978-981-10-6385-5_61openalex-snapshot

Prediction of Passenger Flow at Sanya Airport Based on Combined Methods

inferredcites →https://doi.org/10.1007/s11227-018-2452-0openalex-snapshot

Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study

inferredcites →https://doi.org/10.1007/s11227-019-03064-5openalex-snapshot

An optimized time series combined forecasting method based on neural networks

inferredcites →https://doi.org/10.1016/j.atmosenv.2008.07.020openalex-snapshot

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

inferredcites →https://doi.org/10.1016/j.ijforecast.2009.06.002openalex-snapshot

Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures

inferredcites →https://doi.org/10.1016/j.ijforecast.2015.07.002openalex-snapshot

Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation

inferredcites →https://doi.org/10.1016/j.jairtraman.2018.05.007openalex-snapshot

ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry

inferredcites →https://doi.org/10.1016/j.trc.2014.03.016openalex-snapshot

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

inferredcites →https://doi.org/10.1016/j.trc.2018.04.025openalex-snapshot

Predictor fusion for short-term traffic forecasting

inferredcites →https://doi.org/10.1016/j.tre.2014.03.009openalex-snapshot

Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

inferredcites →https://doi.org/10.1023/a:1008640732416openalex-snapshot

On the Accuracy of Meta-learning for Scalable Data Mining

inferredcites →https://doi.org/10.1057/9781137291264_6openalex-snapshot

Box and Jenkins: Time Series Analysis, Forecasting and Control

inferredcites →https://doi.org/10.1109/access.2019.2897028openalex-snapshot

A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration

inferredcites →https://doi.org/10.1109/access.2019.2907739openalex-snapshot

Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM

inferredcites →https://doi.org/10.1109/access.2019.2919566openalex-snapshot

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

inferredcites →https://doi.org/10.1109/bigdata.2018.8622235openalex-snapshot

Research on User Consumption Behavior Prediction Based on Improved XGBoost Algorithm

inferredcites →https://doi.org/10.1109/cbi.2014.39openalex-snapshot

Predicting Airline Passenger Load: A Case Study

inferredcites →https://doi.org/10.1109/dbta.2009.33openalex-snapshot

Study on the Forecast of Air Passenger Flow Based on SVM Regression Algorithm

inferredcites →https://doi.org/10.1109/icoiact.2018.8350816openalex-snapshot

Hybrid forecasting model to predict air passenger and cargo in Indonesia

inferredcites →https://doi.org/10.1109/icsssm.2019.8887752openalex-snapshot

Military Escape Parachute Price Prediction for Small Sample Based on XGBoost

inferredcites →https://doi.org/10.1109/tie.2017.2787586openalex-snapshot

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot

XGBoost

inferredcites →https://doi.org/10.1166/jctn.2019.8242openalex-snapshot

Air Passenger Demand Forecasting Using Particle Swarm Optimization and Firefly Algorithm

inferredcites →https://doi.org/10.2166/hydro.2020.017openalex-snapshot

Daily runoff forecasting based on data-augmented neural network model

inferredcites →https://doi.org/10.2307/2347162openalex-snapshot

The Holt-Winters Forecasting Procedure

inferredcites →https://doi.org/10.3141/1644-14openalex-snapshot

Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models

inferredcites →https://doi.org/10.3390/a10040139openalex-snapshot

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4824822openalex-extracted

Arrival Information-Guided Spatiotemporal Prediction of Transportation Hub Passenger Distribution

inferred← citeshttps://doi.org/10.1007/978-3-031-60549-9_40openalex-snapshot

Engineering Patterns of Changes in the Parameters of Functioning of Intercity Passenger Transportation System

inferred← citeshttps://doi.org/10.1007/978-3-032-18477-1_52openalex-snapshot

WISE-Blend: A Hybrid Ensemble Framework for Interpretable Fuzzy Time Series Forecasting

inferred← citeshttps://doi.org/10.1007/978-981-96-6948-6_31openalex-snapshot

XGBoost-Based Prediction Model for Train Passenger Numbers: Evaluating the Effect of the COVID-19 Pandemic

inferred← citeshttps://doi.org/10.1007/s11227-022-04468-6openalex-snapshot

Feature selection with a deep learning based high-performance computing model for traffic flow analysis of Twitter data

inferred← citeshttps://doi.org/10.1007/s13198-021-01176-xopenalex-snapshot

Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model

inferred← citeshttps://doi.org/10.1016/j.cie.2023.109707openalex-snapshot

Flight short-term booking demand forecasting based on a long short-term memory network

inferred← citeshttps://doi.org/10.1016/j.tre.2025.104011openalex-snapshot

Arrival information-guided spatiotemporal prediction of transportation hub passenger distribution

inferred← citeshttps://doi.org/10.1088/1742-6596/2735/1/012018openalex-snapshot

Research on the Prediction of Flight Passenger Flow in Holidays based on Machine Learning

inferred← citeshttps://doi.org/10.1117/12.3032987openalex-snapshot

A model for forecasting flight advance booking demand with competition effect

inferred← citeshttps://doi.org/10.1177/03611981261441276openalex-snapshot

Short-Term Forecasting of Checked Baggage Flow in Airports Using a Hybrid Improved Particle Swarm Optimization-Back Propagation Neural Network Model

inferred← citeshttps://doi.org/10.30518/jav.1351229openalex-snapshot

Comparison of Artificial Intelligence Techniques for The UK Air Passenger Short-Term Demand Forecasting: A Destination Insight Study

inferred← citeshttps://doi.org/10.3390/su16104110openalex-snapshot

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

inferred← citeshttps://doi.org/10.46299/j.isjea.20220103.20openalex-snapshot

Аналіз сучасних наукових підходів до розрахунку кількості пасажирів на авіаційному транспорті

inferred← citeshttps://doi.org/10.46299/j.isjea.20220105.14openalex-snapshot

Закономірності зміни параметрів функціонування системи міжміських пасажирських перевезень

inferred← citeshttps://doi.org/10.48130/dts-0024-0005openalex-snapshot

A panel data model to predict airline passenger volume

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