DTG Big Data Analysis for Fuel Consumption Estimation


Wonhee Cho, Eunmi Choi, Journal of Information Processing Systems Vol. 13, No. 2, pp. 285-304, Apr. 2017  

https://doi.org/10.3745/JIPS.04.0031
Keywords: Big Data Analysis, DTG, Eco-Driving, Fuel Economy, Fuel Consumption Estimation, MapReduce
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Abstract

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.


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Cite this article
[APA Style]
Cho, W. & Choi, E. (2017). DTG Big Data Analysis for Fuel Consumption Estimation . Journal of Information Processing Systems, 13(2), 285-304. DOI: 10.3745/JIPS.04.0031.

[IEEE Style]
W. Cho and E. Choi, "DTG Big Data Analysis for Fuel Consumption Estimation ," Journal of Information Processing Systems, vol. 13, no. 2, pp. 285-304, 2017. DOI: 10.3745/JIPS.04.0031.

[ACM Style]
Wonhee Cho and Eunmi Choi. 2017. DTG Big Data Analysis for Fuel Consumption Estimation . Journal of Information Processing Systems, 13, 2, (2017), 285-304. DOI: 10.3745/JIPS.04.0031.