Resident travel mode prediction model in Beijing metropolitan area

With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing...

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Autores principales:	Xueyu Mi, Shengyou Wang, Chunfu Shao, Peng Zhang, Mingming Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6d2de6154c634a07a565c575a7d4d763
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id	oai:doaj.org-article:6d2de6154c634a07a565c575a7d4d763
record_format	dspace
spelling	oai:doaj.org-article:6d2de6154c634a07a565c575a7d4d7632021-11-18T08:14:32ZResident travel mode prediction model in Beijing metropolitan area1932-6203https://doaj.org/article/6d2de6154c634a07a565c575a7d4d7632021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588932/?tool=EBIhttps://doaj.org/toc/1932-6203With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.Xueyu MiShengyou WangChunfu ShaoPeng ZhangMingming ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xueyu Mi Shengyou Wang Chunfu Shao Peng Zhang Mingming Chen Resident travel mode prediction model in Beijing metropolitan area
description	With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.
format	article
author	Xueyu Mi Shengyou Wang Chunfu Shao Peng Zhang Mingming Chen
author_facet	Xueyu Mi Shengyou Wang Chunfu Shao Peng Zhang Mingming Chen
author_sort	Xueyu Mi
title	Resident travel mode prediction model in Beijing metropolitan area
title_short	Resident travel mode prediction model in Beijing metropolitan area
title_full	Resident travel mode prediction model in Beijing metropolitan area
title_fullStr	Resident travel mode prediction model in Beijing metropolitan area
title_full_unstemmed	Resident travel mode prediction model in Beijing metropolitan area
title_sort	resident travel mode prediction model in beijing metropolitan area
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6d2de6154c634a07a565c575a7d4d763
work_keys_str_mv	AT xueyumi residenttravelmodepredictionmodelinbeijingmetropolitanarea AT shengyouwang residenttravelmodepredictionmodelinbeijingmetropolitanarea AT chunfushao residenttravelmodepredictionmodelinbeijingmetropolitanarea AT pengzhang residenttravelmodepredictionmodelinbeijingmetropolitanarea AT mingmingchen residenttravelmodepredictionmodelinbeijingmetropolitanarea
_version_	1718422047651004416

Resident travel mode prediction model in Beijing metropolitan area

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