Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating...

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Autores principales:	Wenjing Wang, Yanyan Chen, Haodong Sun, Yusen Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	trip chain travel segment data fusion travel mode mobile phone trip survey multiple binary classification model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/5f874f580275420faf2763f5d754ac93
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spelling	oai:doaj.org-article:5f874f580275420faf2763f5d754ac932021-11-11T19:50:43ZMultiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data10.3390/su1321122982071-1050https://doaj.org/article/5f874f580275420faf2763f5d754ac932021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12298https://doaj.org/toc/2071-1050Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.Wenjing WangYanyan ChenHaodong SunYusen ChenMDPI AGarticletrip chaintravel segmentdata fusiontravel modemobile phone trip surveymultiple binary classification modelEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12298, p 12298 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	trip chain travel segment data fusion travel mode mobile phone trip survey multiple binary classification model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	trip chain travel segment data fusion travel mode mobile phone trip survey multiple binary classification model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Wenjing Wang Yanyan Chen Haodong Sun Yusen Chen Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
description	Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.
format	article
author	Wenjing Wang Yanyan Chen Haodong Sun Yusen Chen
author_facet	Wenjing Wang Yanyan Chen Haodong Sun Yusen Chen
author_sort	Wenjing Wang
title	Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
title_short	Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
title_full	Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
title_fullStr	Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
title_full_unstemmed	Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data
title_sort	multiple binary classification model of trip chain based on the fusion of internet location data and transport data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5f874f580275420faf2763f5d754ac93
work_keys_str_mv	AT wenjingwang multiplebinaryclassificationmodeloftripchainbasedonthefusionofinternetlocationdataandtransportdata AT yanyanchen multiplebinaryclassificationmodeloftripchainbasedonthefusionofinternetlocationdataandtransportdata AT haodongsun multiplebinaryclassificationmodeloftripchainbasedonthefusionofinternetlocationdataandtransportdata AT yusenchen multiplebinaryclassificationmodeloftripchainbasedonthefusionofinternetlocationdataandtransportdata
_version_	1718431411111723008

Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

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