Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

Guo, Xiaojia; Grushka‐Cockayne, Yael; Reyck, Bert De

doi:10.1287/msom.2021.0975

Cited by 23 publications

(9 citation statements)

References 39 publications

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“…For instance, Rebollo and Balakrishnan (2014) apply random forests to predict air traffic delays of the National Airspace System using both temporal and network delay states With an increasing amount of available data that is associated with activities in the aviation industry, predictive analyses and forecasting methods face new challenges as well as opportunities, especially in regard to updating forecasts in real time. The predictive system developed by Guo et al (2020) is able to generate accurate forecasts using real-time flight and passenger information on a rolling basis. The parameters of their model, however, do not update over time.…”

Section: Forecasting For Airports 146mentioning

confidence: 99%

“…develop a multinomial logit regression model, designed to predict delays of US domestic passengers. Their study also uses data from the US Department of Transportation(Bureau of Transportation Statistics, 2020) Guo et al (2020). recently develop a predictive system that generates distributional forecasts of connection times for transfer passengers at an airport, as well as passenger flows at the immigration and security areas.…”

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confidence: 99%

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Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Forecasting For Airports 146mentioning

confidence: 99%

mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

Self Cite

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“…Elmachtoub, Liang, & McNellis, 2020;A. N. Elmachtoub & Grigas, 2021;Ferreira, Lee, & Simchi-Levi, 2016;Guo, Grushka-Cockayne, & De Reyck, 2021;J. Sun, Zhang, Hu, & Van Mieghem, 2021;K.…”

Section: Literature Reviewmentioning

confidence: 99%

Visualizing the Implicit Model Selection Tradeoff

Ze-zhen

Shaposhnik²

2023

jair

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The recent rise of machine learning (ML) has been leveraged by practitioners and researchers to provide new solutions to an ever growing number of business problems. As with other ML applications, these solutions rely on model selection, which is typically achieved by evaluating certain metrics on models separately and selecting the model whose evaluations (i.e., accuracy-related loss and/or certain interpretability measures) are optimal. However, empirical evidence suggests that, in practice, multiple models often attain competitive results. Therefore, while models’ overall performance could be similar, they could operate quite differently. This results in an implicit tradeoff in models’ performance throughout the feature space which resolving requires new model selection tools. This paper explores methods for comparing predictive models in an interpretable manner to uncover the tradeoff and help resolve it. To this end, we propose various methods that synthesize ideas from supervised learning, unsupervised learning, dimensionality reduction, and visualization to demonstrate how they can be used to inform model developers about the model selection process. Using various datasets and a simple Python interface, we demonstrate how practitioners and researchers could benefit from applying these approaches to better understand the broader impact of their model selection choices.

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“…Li et al (2021) adopted the seasonal ARIMA and SVM to predict the periodic flow of railway passenger. Guo et al (2021) proposed a regression tree combined with copula-based simulations employing passenger level data to generate real-time distributional estimates of travels in an airport. Rajendran et al (2021) developed a logistic regression (LR), artificial neural networks (ANN), RF, and gradient boosting (GB) for assessing air taxi demand considering various factors such as temperature, weather conditions and visibility.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Utku

Kaya

2022

Decis. Mak. Appl. Manag. Eng.

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Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.

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Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

Cited by 23 publications

References 39 publications

Forecasting: theory and practice

Forecasting: theory and practice

Visualizing the Implicit Model Selection Tradeoff

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

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