Air transportation demand forecast through Bagging Holt Winters methods

Dantas, Tiago Mendes; Oliveira, Fernando Luiz Cyrino; Repolho, Hugo M.

doi:10.1016/j.jairtraman.2016.12.006

Cited by 101 publications

(46 citation statements)

References 31 publications

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“…The results presented in [13] are quite important because they show an impressive reduction on the forecasting error for Exponential Smoothing methods, indicating an enourmous potential to improve forecasts in many fields. In this sense, in a result also verified in this thesis, Dantas and Cyrino Oliveira in [14] expanded the fields of application to the air transportation industry by showing the method proposed in [13] was able to outperform the benchmarks (SARIMA, Holt Winters, ETS, and Seasonal Naive). The paper poses itself as an important proof of the method's capacity due to its use on a real problem.…”

Section: Literature Review and Contributionssupporting

confidence: 71%

Combining to Succeed: A Novel Strategy to Improve Forecasts From Exponential Smoothing Models

DANTAS¹

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Section: Literature Review and Contributionssupporting

confidence: 71%

Combining to Succeed: A Novel Strategy to Improve Forecasts From Exponential Smoothing Models

DANTAS¹

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“…According to Table 8, we obtain the choice intention model of passengers for EMU trains with sleeping cars, which is shown as follows. H 1 = −1.691 − 1.446 f 1 − 0.641 f 3 − 0.202x 61 − 0.323x 62 − 0.353x 63 − 0.369x 64 − 0.313x 65 − 0.040x 66 − 0.216x 67 (11) H 2 = 0.282 − 1.446 f 1 − 0.641 f 3 − 0.202x 61 − 0.323x 62 − 0.353x 63 − 0.369x 64 − 0.313x 65 − 0.040x 66 − 0.216x 67 (12) In formula (11) and 12 To analyze the relationship between passengers' choice intention for EMU trains with sleeping cars and the characteristics of passengers' personal attributes more clearly, Equations (13) and (14) are obtained according to Equations (8), (9), (10), (11), and (12). H 1 = −1.691 + 0.092x 2 − 0.377x 3 − 0.740x 4 − 0.949x 5 − 0.202x 61 − 0.323x 62 − 0.353x 63 − 0.369x 64 − 0.313x 65 − 0.040x 66 − 0.216x 67 (13) H 2 = 0.282 + 0.092x 2 − 0.377x 3 − 0.740x 4 − 0.949x 5 − 0.202x 61 − 0.323x 62 − 0.353x 63 − 0.369x 64 − 0.313x 65 − 0.040x 66 − 0.216x 67 (14)…”

Section: Results Of Ordinal Logistic Regressionmentioning

confidence: 99%

“…This is the representative achievement of the early studies of passenger travel demand, and it provides a good reference for the following research on travel behavior of passengers. Subsequently, passenger demand characteristics of various transport modes were studied, such as railways [1,8], highways [9], public transport [10,11], and air transport [12]. For instance, Owen and Phillips analyzed the travel demand characteristics of railway passengers based on British Rail's monthly ticket data and propose a demand function considering responses to changes in economic variables [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Passengers’ Demand Characteristics Experimental Analysis of EMU Trains with Sleeping Cars in Northwest China

2019

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Passenger demand characteristics for electrical multiple unit (EMU) trains with sleeping cars will directly affect the train operation scheme in a long transportation corridor. Descriptive statistics of individual attributes and passenger choice intentions for EMU trains with sleeping cars are calculated based on the revealed preference (RP) and stated preference (SP) survey data in Northwest China to illustrate the overall conditions of passengers’ demands. Considering the higher dimensionality and multi-collinearity in the dataset of influencing factors, the factor analysis method was first adopted to reduce the number of dimensions of the raw dataset and obtain orthogonal common factors. Then, the ordinal logistic regression model was adopted to test and perform a regression analysis based on multinomial logit theory. The analysis shows that these influencing factors, such as income, profession, educational background and residence, would have a greater impact on the choice of an EMU train with sleeping cars. It is significant that passengers’ choice intentions are positively correlated with income and educational background. The result can provide some reference for the decision-making regarding operating an EMU train with sleeping cars in Northwest China. In addition, the proposed method can be applied to the analysis of passengers’ demand characteristics in similar situations.

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“…During this time a variety of models have been developed to predict the demand of passengers. The most used prediction methods can be classified into two large groups: economic models and time-series models (Dantas, Oliveira, & Repolho, 2017). The economic methods focus on the correlation between the demand of passengers and multiple variables, which are considered to be influential in the change of the economic environment and traffic system.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Traffic Forecast in Post-Liberalization Context: A Dynamic Linear Models Approach

2020

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The process of air transport liberalization in Colombia began in 1991. Liberalization entailed the entry of private capital into the airport sector which subsequently led, in several temporary phases, to the privatization of the country’s main airports. Simultaneously, new air operators entered the market. This new market situation, supported by the complete deregulation of airfares, generated a dynamic and sustained growth of air transport in Colombia for two decades. Within the context of post-liberalization, this article presents a forecast (medium-term – 5 years period) of air traffic in the country’s main airport using DLMs (Dynamic Linear Models). It has the following advantages vs. the usual forecast calculation methodologies: it detects stochastic tendencies that are hidden in the time series. It also detects structural changes that allow estimating the variable effect of exogenous shocks over time without increasing the number of parameters. From the results obtained, it should be noted that the application of DLMs presents MAPE (Mean Absolute Percentage Error) values below 1%, which guarantees predictions of higher accuracy and thus introduces a new alternative model to develop reliable forecasts in air transport, at least in the medium-term.

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Air transportation demand forecast through Bagging Holt Winters methods

Cited by 101 publications

References 31 publications

Combining to Succeed: A Novel Strategy to Improve Forecasts From Exponential Smoothing Models

Combining to Succeed: A Novel Strategy to Improve Forecasts From Exponential Smoothing Models

Passengers’ Demand Characteristics Experimental Analysis of EMU Trains with Sleeping Cars in Northwest China

Air Traffic Forecast in Post-Liberalization Context: A Dynamic Linear Models Approach

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