Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels

Maintenance & Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser: Katreddi, Sasanka, Thiruvengadam, Arvind, Thompson, Gregory J., Schmid, Natalia A
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1
container_issue
container_start_page 1
container_title IEEE access
container_volume 11
creator Katreddi, Sasanka
Thiruvengadam, Arvind
Thompson, Gregory J.
Schmid, Natalia A
description Maintenance & Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R 2 of 98.96% with a mean square error of 0.0089 /mile for training and an R 2 of 94.31% with a mean square error of 0.0312 /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.
doi_str_mv 10.1109/ACCESS.2023.3290994
format Article
fullrecord <record><control><sourceid>proquest_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_1987910</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10168912</ieee_id><doaj_id>oai_doaj_org_article_725cfe1cc7c144909c185574ce3ae8d9</doaj_id><sourcerecordid>2836054266</sourcerecordid><originalsourceid>FETCH-LOGICAL-c436t-3e75a8bead4a277564d4e64b8245adc90fb0ae5a7595af89656138ef65b1be343</originalsourceid><addsrcrecordid>eNpNUU1rGzEUXEoLDUl-QXsQ7Xldfa90NI7dBGIKTdOr0GrfJjIbKZXkENM_XzkbSnSReJoZ5s00zSeCF4Rg_W25Wq1vbhYUU7ZgVGOt-bvmhBKpWyaYfP_m_bE5z3mH61F1JLqT5u_WP8OA1uMIrmT004YhPqBNTJAL2sYBJjTGhLbWhwLBBgdoFevXOhf_YIuPAfmALsE-HdqLfTmg33Dv3QQZ3WYf7tCFh1w1qixaTgVSqJwnQJs9TPms-TDaKcP5633a3G7Wv1aX7fWP71er5XXrOJOlZdAJq3qwA7e064TkAwfJe0W5sIPTeOyxBWE7oYUdlZZCEqZglKInPTDOTpurWXeIdmceUzWeDiZab14GMd0Zm8rRtemocCMQ5zpHOK9ZOqKE6LgDZkENump9mbVqCN5k5wu4exdDqPEZolWnCa6grzPoMcU_-5qk2cV9XX3KhiomseBUyopiM8qlmHOC8b81gs2xWTM3a47NmtdmK-vzzPIA8IZBpNKEsn_7SZ8k</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2836054266</pqid></control><display><type>article</type><title>Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Katreddi, Sasanka ; Thiruvengadam, Arvind ; Thompson, Gregory J. ; Schmid, Natalia A</creator><creatorcontrib>Katreddi, Sasanka ; Thiruvengadam, Arvind ; Thompson, Gregory J. ; Schmid, Natalia A</creatorcontrib><description>Maintenance &amp; Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R 2 of 98.96% with a mean square error of 0.0089 /mile for training and an R 2 of 94.31% with a mean square error of 0.0312 /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3290994</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Alternative Fuel ; Alternative fuels ; Clusters ; Costs ; Data models ; Diesel ; Diesel engines ; Diesel fuels ; Diffusion rate ; Estimation ; Fuels ; Heavy duty trucks ; Heavy vehicles ; Heavy-Duty Vehicles ; Machine learning ; Maintenance and Repair Cost ; Maintenance costs ; Maintenance engineering ; Mean square errors ; Mixed Effect Model ; Mixed Effect Random Forest ; Natural gas ; Prediction models ; Predictive models ; Random forests ; Repair &amp; maintenance ; Vehicles ; Vocational Trucks</subject><ispartof>IEEE access, 2023-01, Vol.11, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c436t-3e75a8bead4a277564d4e64b8245adc90fb0ae5a7595af89656138ef65b1be343</citedby><cites>FETCH-LOGICAL-c436t-3e75a8bead4a277564d4e64b8245adc90fb0ae5a7595af89656138ef65b1be343</cites><orcidid>0000-0003-0531-7157 ; 0000-0002-9293-7161 ; 0000000292937161 ; 0000000305317157</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10168912$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,864,885,2102,27633,27924,27925,54933</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1987910$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Katreddi, Sasanka</creatorcontrib><creatorcontrib>Thiruvengadam, Arvind</creatorcontrib><creatorcontrib>Thompson, Gregory J.</creatorcontrib><creatorcontrib>Schmid, Natalia A</creatorcontrib><title>Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels</title><title>IEEE access</title><addtitle>Access</addtitle><description>Maintenance &amp; Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R 2 of 98.96% with a mean square error of 0.0089 /mile for training and an R 2 of 94.31% with a mean square error of 0.0312 /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.</description><subject>Alternative Fuel</subject><subject>Alternative fuels</subject><subject>Clusters</subject><subject>Costs</subject><subject>Data models</subject><subject>Diesel</subject><subject>Diesel engines</subject><subject>Diesel fuels</subject><subject>Diffusion rate</subject><subject>Estimation</subject><subject>Fuels</subject><subject>Heavy duty trucks</subject><subject>Heavy vehicles</subject><subject>Heavy-Duty Vehicles</subject><subject>Machine learning</subject><subject>Maintenance and Repair Cost</subject><subject>Maintenance costs</subject><subject>Maintenance engineering</subject><subject>Mean square errors</subject><subject>Mixed Effect Model</subject><subject>Mixed Effect Random Forest</subject><subject>Natural gas</subject><subject>Prediction models</subject><subject>Predictive models</subject><subject>Random forests</subject><subject>Repair &amp; maintenance</subject><subject>Vehicles</subject><subject>Vocational Trucks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1rGzEUXEoLDUl-QXsQ7Xldfa90NI7dBGIKTdOr0GrfJjIbKZXkENM_XzkbSnSReJoZ5s00zSeCF4Rg_W25Wq1vbhYUU7ZgVGOt-bvmhBKpWyaYfP_m_bE5z3mH61F1JLqT5u_WP8OA1uMIrmT004YhPqBNTJAL2sYBJjTGhLbWhwLBBgdoFevXOhf_YIuPAfmALsE-HdqLfTmg33Dv3QQZ3WYf7tCFh1w1qixaTgVSqJwnQJs9TPms-TDaKcP5633a3G7Wv1aX7fWP71er5XXrOJOlZdAJq3qwA7e064TkAwfJe0W5sIPTeOyxBWE7oYUdlZZCEqZglKInPTDOTpurWXeIdmceUzWeDiZab14GMd0Zm8rRtemocCMQ5zpHOK9ZOqKE6LgDZkENump9mbVqCN5k5wu4exdDqPEZolWnCa6grzPoMcU_-5qk2cV9XX3KhiomseBUyopiM8qlmHOC8b81gs2xWTM3a47NmtdmK-vzzPIA8IZBpNKEsn_7SZ8k</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Katreddi, Sasanka</creator><creator>Thiruvengadam, Arvind</creator><creator>Thompson, Gregory J.</creator><creator>Schmid, Natalia A</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>OTOTI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0531-7157</orcidid><orcidid>https://orcid.org/0000-0002-9293-7161</orcidid><orcidid>https://orcid.org/0000000292937161</orcidid><orcidid>https://orcid.org/0000000305317157</orcidid></search><sort><creationdate>20230101</creationdate><title>Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels</title><author>Katreddi, Sasanka ; Thiruvengadam, Arvind ; Thompson, Gregory J. ; Schmid, Natalia A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c436t-3e75a8bead4a277564d4e64b8245adc90fb0ae5a7595af89656138ef65b1be343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Alternative Fuel</topic><topic>Alternative fuels</topic><topic>Clusters</topic><topic>Costs</topic><topic>Data models</topic><topic>Diesel</topic><topic>Diesel engines</topic><topic>Diesel fuels</topic><topic>Diffusion rate</topic><topic>Estimation</topic><topic>Fuels</topic><topic>Heavy duty trucks</topic><topic>Heavy vehicles</topic><topic>Heavy-Duty Vehicles</topic><topic>Machine learning</topic><topic>Maintenance and Repair Cost</topic><topic>Maintenance costs</topic><topic>Maintenance engineering</topic><topic>Mean square errors</topic><topic>Mixed Effect Model</topic><topic>Mixed Effect Random Forest</topic><topic>Natural gas</topic><topic>Prediction models</topic><topic>Predictive models</topic><topic>Random forests</topic><topic>Repair &amp; maintenance</topic><topic>Vehicles</topic><topic>Vocational Trucks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Katreddi, Sasanka</creatorcontrib><creatorcontrib>Thiruvengadam, Arvind</creatorcontrib><creatorcontrib>Thompson, Gregory J.</creatorcontrib><creatorcontrib>Schmid, Natalia A</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>OSTI.GOV</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Katreddi, Sasanka</au><au>Thiruvengadam, Arvind</au><au>Thompson, Gregory J.</au><au>Schmid, Natalia A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023-01-01</date><risdate>2023</risdate><volume>11</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Maintenance &amp; Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R 2 of 98.96% with a mean square error of 0.0089 /mile for training and an R 2 of 94.31% with a mean square error of 0.0312 /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3290994</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-0531-7157</orcidid><orcidid>https://orcid.org/0000-0002-9293-7161</orcidid><orcidid>https://orcid.org/0000000292937161</orcidid><orcidid>https://orcid.org/0000000305317157</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2169-3536
ispartof IEEE access, 2023-01, Vol.11, p.1-1
issn 2169-3536
2169-3536
language eng
recordid cdi_osti_scitechconnect_1987910
source IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals
subjects Alternative Fuel
Alternative fuels
Clusters
Costs
Data models
Diesel
Diesel engines
Diesel fuels
Diffusion rate
Estimation
Fuels
Heavy duty trucks
Heavy vehicles
Heavy-Duty Vehicles
Machine learning
Maintenance and Repair Cost
Maintenance costs
Maintenance engineering
Mean square errors
Mixed Effect Model
Mixed Effect Random Forest
Natural gas
Prediction models
Predictive models
Random forests
Repair & maintenance
Vehicles
Vocational Trucks
title Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T02%3A10%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_osti_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mixed%20Effects%20Random%20Forest%20Model%20for%20Maintenance%20Cost%20Estimation%20in%20Heavy-Duty%20Vehicles%20Using%20Diesel%20and%20Alternative%20Fuels&rft.jtitle=IEEE%20access&rft.au=Katreddi,%20Sasanka&rft.date=2023-01-01&rft.volume=11&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2023.3290994&rft_dat=%3Cproquest_osti_%3E2836054266%3C/proquest_osti_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2836054266&rft_id=info:pmid/&rft_ieee_id=10168912&rft_doaj_id=oai_doaj_org_article_725cfe1cc7c144909c185574ce3ae8d9&rfr_iscdi=true