Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a nov...
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Autor*in:	Bin Chen [verfasserIn] Jin Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 14(2022), 24, p 16694
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:24, p 16694

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su142416694

Katalog-ID:	DOAJ082970777

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allfields	10.3390/su142416694 doi (DE-627)DOAJ082970777 (DE-599)DOAJ94a460a2ed0a4ed6b035ff152e9d5995 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Bin Chen verfasserin aut Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator. predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences Jin Wu verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 24, p 16694 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:24, p 16694 https://doi.org/10.3390/su142416694 kostenfrei https://doaj.org/article/94a460a2ed0a4ed6b035ff152e9d5995 kostenfrei https://www.mdpi.com/2071-1050/14/24/16694 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 24, p 16694
spelling	10.3390/su142416694 doi (DE-627)DOAJ082970777 (DE-599)DOAJ94a460a2ed0a4ed6b035ff152e9d5995 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Bin Chen verfasserin aut Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator. predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences Jin Wu verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 24, p 16694 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:24, p 16694 https://doi.org/10.3390/su142416694 kostenfrei https://doaj.org/article/94a460a2ed0a4ed6b035ff152e9d5995 kostenfrei https://www.mdpi.com/2071-1050/14/24/16694 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 24, p 16694
allfields_unstemmed	10.3390/su142416694 doi (DE-627)DOAJ082970777 (DE-599)DOAJ94a460a2ed0a4ed6b035ff152e9d5995 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Bin Chen verfasserin aut Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator. predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences Jin Wu verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 24, p 16694 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:24, p 16694 https://doi.org/10.3390/su142416694 kostenfrei https://doaj.org/article/94a460a2ed0a4ed6b035ff152e9d5995 kostenfrei https://www.mdpi.com/2071-1050/14/24/16694 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 24, p 16694
allfieldsGer	10.3390/su142416694 doi (DE-627)DOAJ082970777 (DE-599)DOAJ94a460a2ed0a4ed6b035ff152e9d5995 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Bin Chen verfasserin aut Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator. predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences Jin Wu verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 24, p 16694 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:24, p 16694 https://doi.org/10.3390/su142416694 kostenfrei https://doaj.org/article/94a460a2ed0a4ed6b035ff152e9d5995 kostenfrei https://www.mdpi.com/2071-1050/14/24/16694 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 24, p 16694
allfieldsSound	10.3390/su142416694 doi (DE-627)DOAJ082970777 (DE-599)DOAJ94a460a2ed0a4ed6b035ff152e9d5995 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Bin Chen verfasserin aut Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator. predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences Jin Wu verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 24, p 16694 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:24, p 16694 https://doi.org/10.3390/su142416694 kostenfrei https://doaj.org/article/94a460a2ed0a4ed6b035ff152e9d5995 kostenfrei https://www.mdpi.com/2071-1050/14/24/16694 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 24, p 16694
language	English
source	In Sustainability 14(2022), 24, p 16694 volume:14 year:2022 number:24, p 16694
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topic_facet	predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model Environmental effects of industries and plants Renewable energy sources Environmental sciences
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container_title	Sustainability
authorswithroles_txt_mv	Bin Chen @@aut@@ Jin Wu @@aut@@
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callnumber-first	T - Technology
author	Bin Chen
spellingShingle	Bin Chen misc TD194-195 misc TJ807-830 misc GE1-350 misc predicting model misc air-transport-demand forecasting misc seasonal autoregressive integrated moving average (sARIMA) model misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter
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topic_title	TD194-195 TJ807-830 GE1-350 Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter predicting model air-transport-demand forecasting seasonal autoregressive integrated moving average (sARIMA) model
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc predicting model misc air-transport-demand forecasting misc seasonal autoregressive integrated moving average (sARIMA) model misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc predicting model misc air-transport-demand forecasting misc seasonal autoregressive integrated moving average (sARIMA) model misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_browse	misc TD194-195 misc TJ807-830 misc GE1-350 misc predicting model misc air-transport-demand forecasting misc seasonal autoregressive integrated moving average (sARIMA) model misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter
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title_full	Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter
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title_sort	predicting model for air transport demand under uncertainties based on particle filter
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title_auth	Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter
abstract	The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.
abstractGer	The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.
abstract_unstemmed	The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.
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title_short	Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter
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Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

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