Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carshari...
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Autor*in:	Chunxia Wang [verfasserIn] Jun Bi [verfasserIn] Qiuyue Sai [verfasserIn] Zun Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	carsharing travel characteristics demand predict gradient boosting decision tree

Übergeordnetes Werk:	In: Algorithms - MDPI AG, 2008, 14(2021), 6, p 179
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:6, p 179

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/a14060179

Katalog-ID:	DOAJ070286485

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spelling	10.3390/a14060179 doi (DE-627)DOAJ070286485 (DE-599)DOAJ26c8cf8028904b349f1b50343d24663e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Chunxia Wang verfasserin aut Analysis and Prediction of Carsharing Demand Based on Data Mining Methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application. carsharing travel characteristics demand predict gradient boosting decision tree Industrial engineering. Management engineering Electronic computers. Computer science Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Zun Yuan verfasserin aut In Algorithms MDPI AG, 2008 14(2021), 6, p 179 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:14 year:2021 number:6, p 179 https://doi.org/10.3390/a14060179 kostenfrei https://doaj.org/article/26c8cf8028904b349f1b50343d24663e kostenfrei https://www.mdpi.com/1999-4893/14/6/179 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 6, p 179
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allfieldsSound	10.3390/a14060179 doi (DE-627)DOAJ070286485 (DE-599)DOAJ26c8cf8028904b349f1b50343d24663e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Chunxia Wang verfasserin aut Analysis and Prediction of Carsharing Demand Based on Data Mining Methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application. carsharing travel characteristics demand predict gradient boosting decision tree Industrial engineering. Management engineering Electronic computers. Computer science Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Zun Yuan verfasserin aut In Algorithms MDPI AG, 2008 14(2021), 6, p 179 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:14 year:2021 number:6, p 179 https://doi.org/10.3390/a14060179 kostenfrei https://doaj.org/article/26c8cf8028904b349f1b50343d24663e kostenfrei https://www.mdpi.com/1999-4893/14/6/179 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 6, p 179
language	English
source	In Algorithms 14(2021), 6, p 179 volume:14 year:2021 number:6, p 179
sourceStr	In Algorithms 14(2021), 6, p 179 volume:14 year:2021 number:6, p 179
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institution	findex.gbv.de
topic_facet	carsharing travel characteristics demand predict gradient boosting decision tree Industrial engineering. Management engineering Electronic computers. Computer science
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authorswithroles_txt_mv	Chunxia Wang @@aut@@ Jun Bi @@aut@@ Qiuyue Sai @@aut@@ Zun Yuan @@aut@@
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callnumber-first	T - Technology
author	Chunxia Wang
spellingShingle	Chunxia Wang misc T55.4-60.8 misc QA75.5-76.95 misc carsharing misc travel characteristics misc demand predict misc gradient boosting decision tree misc Industrial engineering. Management engineering misc Electronic computers. Computer science Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
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topic	misc T55.4-60.8 misc QA75.5-76.95 misc carsharing misc travel characteristics misc demand predict misc gradient boosting decision tree misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_unstemmed	misc T55.4-60.8 misc QA75.5-76.95 misc carsharing misc travel characteristics misc demand predict misc gradient boosting decision tree misc Industrial engineering. Management engineering misc Electronic computers. Computer science
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title	Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
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title_full	Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
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title_sort	analysis and prediction of carsharing demand based on data mining methods
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title_auth	Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
abstract	With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.
abstractGer	With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.
abstract_unstemmed	With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.
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title_short	Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
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