Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones
Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and...
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| Veröffentlicht in: | Journal of advanced transportation 2020, Vol.2020 (2020), p.1-11 |
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| creator | Su, Yuelong Zhang, Yunlong Rong, Jian Liu, Chang Zhao, Xiaohua Yao, Ying Dong, Zhenning |
| description | Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption. |
| doi_str_mv | 10.1155/2020/9263605 |
| format | Article |
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The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.</description><identifier>ISSN: 0197-6729</identifier><identifier>EISSN: 2042-3195</identifier><identifier>DOI: 10.1155/2020/9263605</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Acceleration ; Cellular telephones ; Data base management systems ; Deceleration ; Diagnostic systems ; Driving ability ; Energy consumption ; Energy use ; Fuel consumption ; Idling ; Local transit ; Methods ; Monitoring ; Neural networks ; Prediction models ; Predictions ; Sensors ; Smart phones ; Smartphones ; Speed limits ; Support vector machines ; Taxicabs ; Transport buildings, stations and terminals ; Transportation ; Urban transportation</subject><ispartof>Journal of advanced transportation, 2020, Vol.2020 (2020), p.1-11</ispartof><rights>Copyright © 2020 Ying Yao et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Ying Yao et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-2edcf2828e566a2f2d153ed15f94ef20ba73cc841bde9f74d47641903a606c1d3</citedby><cites>FETCH-LOGICAL-c540t-2edcf2828e566a2f2d153ed15f94ef20ba73cc841bde9f74d47641903a606c1d3</cites><orcidid>0000-0003-3122-7972 ; 0000-0001-5730-6199 ; 0000-0003-3626-0480 ; 0000-0002-9322-9386</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,873,2096,4010,27900,27901,27902</link.rule.ids></links><search><contributor>Chen, Feng</contributor><contributor>Feng Chen</contributor><creatorcontrib>Su, Yuelong</creatorcontrib><creatorcontrib>Zhang, Yunlong</creatorcontrib><creatorcontrib>Rong, Jian</creatorcontrib><creatorcontrib>Liu, Chang</creatorcontrib><creatorcontrib>Zhao, Xiaohua</creatorcontrib><creatorcontrib>Yao, Ying</creatorcontrib><creatorcontrib>Dong, Zhenning</creatorcontrib><title>Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones</title><title>Journal of advanced transportation</title><description>Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.</description><subject>Acceleration</subject><subject>Cellular telephones</subject><subject>Data base management systems</subject><subject>Deceleration</subject><subject>Diagnostic systems</subject><subject>Driving ability</subject><subject>Energy consumption</subject><subject>Energy use</subject><subject>Fuel consumption</subject><subject>Idling</subject><subject>Local transit</subject><subject>Methods</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Predictions</subject><subject>Sensors</subject><subject>Smart phones</subject><subject>Smartphones</subject><subject>Speed limits</subject><subject>Support vector machines</subject><subject>Taxicabs</subject><subject>Transport buildings, stations and terminals</subject><subject>Transportation</subject><subject>Urban 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Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones</title><author>Su, Yuelong ; Zhang, Yunlong ; Rong, Jian ; Liu, Chang ; Zhao, Xiaohua ; Yao, Ying ; Dong, Zhenning</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-2edcf2828e566a2f2d153ed15f94ef20ba73cc841bde9f74d47641903a606c1d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Acceleration</topic><topic>Cellular telephones</topic><topic>Data base management systems</topic><topic>Deceleration</topic><topic>Diagnostic systems</topic><topic>Driving ability</topic><topic>Energy consumption</topic><topic>Energy use</topic><topic>Fuel consumption</topic><topic>Idling</topic><topic>Local transit</topic><topic>Methods</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Predictions</topic><topic>Sensors</topic><topic>Smart 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Chang</au><au>Zhao, Xiaohua</au><au>Yao, Ying</au><au>Dong, Zhenning</au><au>Chen, Feng</au><au>Feng Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones</atitle><jtitle>Journal of advanced transportation</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>0197-6729</issn><eissn>2042-3195</eissn><abstract>Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. 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| subjects | Acceleration Cellular telephones Data base management systems Deceleration Diagnostic systems Driving ability Energy consumption Energy use Fuel consumption Idling Local transit Methods Monitoring Neural networks Prediction models Predictions Sensors Smart phones Smartphones Speed limits Support vector machines Taxicabs Transport buildings, stations and terminals Transportation Urban transportation |
| title | Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones |
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