Public Transportation Operational Health Assessment Based on Multi-Source Data

In order to solve the problem of inefficient long-term operation of urban public transport vehicles and the difficulty of finding the cause of the disease, a new analysis idea was designed using machine learning methods. This study aimed to provide a rapid, accurate, and convenient solution model an...

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Autores principales:	Xuemei Zhou, Zhen Guan, Jiaojiao Xi, Guohui Wei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	transportation planning public transportation management machine learning operations research optimization random forest model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/169cd1529e6a4a00a6bb3b767b702d0d
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id	oai:doaj.org-article:169cd1529e6a4a00a6bb3b767b702d0d
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spelling	oai:doaj.org-article:169cd1529e6a4a00a6bb3b767b702d0d2021-11-25T16:33:10ZPublic Transportation Operational Health Assessment Based on Multi-Source Data10.3390/app1122106112076-3417https://doaj.org/article/169cd1529e6a4a00a6bb3b767b702d0d2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10611https://doaj.org/toc/2076-3417In order to solve the problem of inefficient long-term operation of urban public transport vehicles and the difficulty of finding the cause of the disease, a new analysis idea was designed using machine learning methods. This study aimed to provide a rapid, accurate, and convenient solution model and algorithm to address the drawbacks of traditional analysis tools that are incapable of handling multiple sources of public transport data. Based on a full process analysis of the bus operation status, the influencing factors and calculation methods were defined. Afterwards, the calculation results were used to construct a training set with a Random Forest regression model to obtain the weight ranking of different influencing factors. The results of the simulation validation proved that the model can use the basic data of bus operation to quickly find out the primary factors affecting the operation condition and pinpoint to the bottleneck interval. The method has high accuracy and feasibility. It can be universally applied to the analysis of regular bus scenarios to provide strong decision support for the operation level.Xuemei ZhouZhen GuanJiaojiao XiGuohui WeiMDPI AGarticletransportation planningpublic transportation managementmachine learningoperations research optimizationrandom forest modelTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10611, p 10611 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	transportation planning public transportation management machine learning operations research optimization random forest model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	transportation planning public transportation management machine learning operations research optimization random forest model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xuemei Zhou Zhen Guan Jiaojiao Xi Guohui Wei Public Transportation Operational Health Assessment Based on Multi-Source Data
description	In order to solve the problem of inefficient long-term operation of urban public transport vehicles and the difficulty of finding the cause of the disease, a new analysis idea was designed using machine learning methods. This study aimed to provide a rapid, accurate, and convenient solution model and algorithm to address the drawbacks of traditional analysis tools that are incapable of handling multiple sources of public transport data. Based on a full process analysis of the bus operation status, the influencing factors and calculation methods were defined. Afterwards, the calculation results were used to construct a training set with a Random Forest regression model to obtain the weight ranking of different influencing factors. The results of the simulation validation proved that the model can use the basic data of bus operation to quickly find out the primary factors affecting the operation condition and pinpoint to the bottleneck interval. The method has high accuracy and feasibility. It can be universally applied to the analysis of regular bus scenarios to provide strong decision support for the operation level.
format	article
author	Xuemei Zhou Zhen Guan Jiaojiao Xi Guohui Wei
author_facet	Xuemei Zhou Zhen Guan Jiaojiao Xi Guohui Wei
author_sort	Xuemei Zhou
title	Public Transportation Operational Health Assessment Based on Multi-Source Data
title_short	Public Transportation Operational Health Assessment Based on Multi-Source Data
title_full	Public Transportation Operational Health Assessment Based on Multi-Source Data
title_fullStr	Public Transportation Operational Health Assessment Based on Multi-Source Data
title_full_unstemmed	Public Transportation Operational Health Assessment Based on Multi-Source Data
title_sort	public transportation operational health assessment based on multi-source data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/169cd1529e6a4a00a6bb3b767b702d0d
work_keys_str_mv	AT xuemeizhou publictransportationoperationalhealthassessmentbasedonmultisourcedata AT zhenguan publictransportationoperationalhealthassessmentbasedonmultisourcedata AT jiaojiaoxi publictransportationoperationalhealthassessmentbasedonmultisourcedata AT guohuiwei publictransportationoperationalhealthassessmentbasedonmultisourcedata
_version_	1718413120562528256

Public Transportation Operational Health Assessment Based on Multi-Source Data

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