Forecasting investment and capacity addition in Indian airport infrastructure: Analysis from post-privatization and post-economic regulation era

Singh, D. B.; Dalei, Narendra N.; Raju, Totakura Bangar

doi:10.1016/j.jairtraman.2016.03.004

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…A reasonable forecasting method is an important prerequisite to improve the effectiveness and accuracy of future power grid investment forecasting. The exploration of prediction methods has always been the focus of scholars and many prediction methods have been put forward over the years 20,21 …”

Section: Introductionmentioning

confidence: 99%

“…The exploration of prediction methods has always been the focus of scholars and many prediction methods have been put forward over the years. 20,21 The traditional prediction models based on parameter estimation include regression analysis, time series prediction, and gray model. After decades of continuous development in the last century, these traditional methods have a mature theoretical basis, and a large number of scholars at home and abroad have conducted extensive research and application on them.…”

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confidence: 99%

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Power grid investment forecasting method based on PSO‐GRU neural network

Lou

Liu³

2023

Concurrency and Computation

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SummaryTraditional power grid investment forecasting models often ignore the cyclical characteristics of historical investment data, leading to one‐sided investment allocation results and insufficient model generalization ability. This article proposed a power network investment forecasting method based on particle swarm optimization‐gate recurrent unit (PSO‐GRU) neural network. First, the temporal attention mechanism is introduced into the traditional GRU network, which improves the ability of the network to extract temporal features. Then, in order to avoid the adverse effects of unreasonable parameter configuration on model training, an optimized particle swarm optimization algorithm was proposed to optimize the parameter set of GRU and improve the training accuracy of the model. The data provided by an electric power company in China is used for experimental analysis, and the predicted results are compared with other electric power investment models. The RMSE of the PSO‐GRU model proposed in this article is 0.1223, which is superior to other algorithms and has certain effectiveness.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Power grid investment forecasting method based on PSO‐GRU neural network

Lou

Liu³

2023

Concurrency and Computation

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“…Lunsford [9] proposed an error correction model based on the stock-flow relationship of housing unit start, construction, and completion to predict housing investment in the United States. Singh et al [10] collected the air traffic data, gross domestic product, and industrial production index of the India civil aviation department to study the corresponding elasticity coefficient, and predicted the capacity demand and investment demand of India airport infrastructure for the next 20 years. Chang and Linneman [11] used the time series model to forecast the growth rate of real estate investment in other countries on the basis of studying the substitution model of real estate investment growth rates in Japan, Korea, Taiwan, and the United States.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

2018

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Abstract:In recent years, the construction of China's power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China's power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.

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“…Most existing studies have predicted air travel demand using macro-socioeconomic explanatory variables such as the population and GDP of cities and countries. Singh et al estimated domestic and international passenger demand based on GDP variables, while Suryani et al also included fare and service level as explanatory variables ( 1, 2 ). Bonvino et al also tried to estimate an air travel demand model with the aggregated variables such as population, difference between travel time by air and train, per capita income, number of workers in industry, and number of hotels ( 3 ).…”

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confidence: 99%

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

Kim

Shim

et al. 2020

Transportation Research Record

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The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

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Forecasting investment and capacity addition in Indian airport infrastructure: Analysis from post-privatization and post-economic regulation era

Cited by 15 publications

References 10 publications

Power grid investment forecasting method based on PSO‐GRU neural network

Power grid investment forecasting method based on PSO‐GRU neural network

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

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