Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system
The research aims to develop a cloud-based service framework for reducing carbon dioxide emission and fuel consumption in intelligent transportation system. It collects traffic condition, driving behavior, and video through telematics and digital tachygraphy and road-side cameras to facilitate advan...
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| Vydáno v: | International journal of production economics Ročník 164; s. 454 - 461 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
01.06.2015
Elsevier Sequoia S.A |
| Témata: | |
| ISSN: | 0925-5273, 1873-7579 |
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| Abstract | The research aims to develop a cloud-based service framework for reducing carbon dioxide emission and fuel consumption in intelligent transportation system. It collects traffic condition, driving behavior, and video through telematics and digital tachygraphy and road-side cameras to facilitate advanced data analytics for the reduction of fuel consumption. There are three specific features regarding this framework. First, a transportation cloud is built for the storage of massive data and video. This cloud-based system not only avoids the use of hard disks at client-site for energy conservation and reliability improvement, but also allows the back-end data analytics at both server and client sites. Second, a real-time traffic condition analytic was developed by mobile machine vision techniques based on video and data collected from road-side cameras to analyze and recognize traffic conditions, such as traffic flow, braking events, traffic lights, and count-down timers. Then, a fuel-efficient route navigation technology is also developed for eco-driving based on real time traffic information and a dynamic shortest path algorithm for saving time and fuel consumption. Third, a sequential pattern mining model was proposed to diagnose misguided driving behavior for eco-driving based on the real-time data collected from digital tachygraphy and on-board diagnostics system. Furthermore, an e-Learning visualization system was developed to provide advice and instruction for correction of misguided driving behavior. Indeed, the fuel consumption and power consumption can be reduced simultaneously based on the proposed framework regarding cloud-based system and eco-driving. |
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| AbstractList | The research aims to develop a cloud-based service framework for reducing carbon dioxide emission and fuel consumption in intelligent transportation system. It collects traffic condition, driving behavior, and video through telematics and digital tachygraphy and road-side cameras to facilitate advanced data analytics for the reduction of fuel consumption. There are three specific features regarding this framework. First, a transportation cloud is built for the storage of massive data and video. This cloud-based system not only avoids the use of hard disks at client-site for energy conservation and reliability improvement, but also allows the back-end data analytics at both server and client sites. Second, a real-time traffic condition analytic was developed by mobile machine vision techniques based on video and data collected from road-side cameras to analyze and recognize traffic conditions, such as traffic flow, braking events, traffic lights, and count-down timers. Then, a fuel-efficient route navigation technology is also developed for eco-driving based on real time traffic information and a dynamic shortest path algorithm for saving time and fuel consumption. Third, a sequential pattern mining model was proposed to diagnose misguided driving behavior for eco-driving based on the real-time data collected from digital tachygraphy and on-board diagnostics system. Furthermore, an e-Learning visualization system was developed to provide advice and instruction for correction of misguided driving behavior. Indeed, the fuel consumption and power consumption can be reduced simultaneously based on the proposed framework regarding cloud-based system and eco-driving. |
| Author | Lin, Chi-Fang Yu, Liang-Chih Yao, Hsiu-Hsen Robert Lai, K. Hsu, Chia-Yu Chang, Pei-Chann Chen, Duan-Yu Yang, Chin-Sheng |
| Author_xml | – sequence: 1 givenname: Chia-Yu surname: Hsu fullname: Hsu, Chia-Yu organization: Department of Information Management, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 2 givenname: Chin-Sheng surname: Yang fullname: Yang, Chin-Sheng organization: Department of Information Management, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 3 givenname: Liang-Chih orcidid: 0000-0003-1443-4347 surname: Yu fullname: Yu, Liang-Chih organization: Department of Information Management, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 4 givenname: Chi-Fang surname: Lin fullname: Lin, Chi-Fang organization: Department of Computer Science & Engineering, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 5 givenname: Hsiu-Hsen surname: Yao fullname: Yao, Hsiu-Hsen organization: Department of Computer Science & Engineering, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 6 givenname: Duan-Yu surname: Chen fullname: Chen, Duan-Yu organization: Department of Electrical Engineering, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 7 givenname: K. surname: Robert Lai fullname: Robert Lai, K. organization: Department of Computer Science & Engineering, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan – sequence: 8 givenname: Pei-Chann surname: Chang fullname: Chang, Pei-Chann email: iepchang@saturn.yzu.edu.tw organization: Department of Information Management, Yuan Ze University, No. 135, Yuan-Tung Road Chungli, Taoyuan 32003, Taiwan |
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| SubjectTerms | Cloud Cloud computing Data analysis Eco-driving Emissions control Energy conservation Energy consumption Intelligent systems Intelligent transportation systems Studies Sustainable development Telematics Traffic |
| Title | Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system |
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