An Improved Vehicle Data Format of Digital Tachograph

Kang, Joon-Gyu; Kim, Yoowon; 임웅택,; Jun, Moon Seog

doi:10.9708/jksci.2013.18.8.077

Cited by 2 publications

(1 citation statement)

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“…As an alternative, big data from digital tachographs (DTGs) can be used as it is now mandatory to mount DTG on all commercial vehicles to reduce traffic accidents in Korea [3]. Vast vehicle-driving data regarding speed, revolutions per minute (RPM), GPS coordinates, acceleration, and brake signal are collected through DTG devices [4]. However, because DTG data do not contain fuel consumption information, studies requiring fuel consumption calculations could not be performed.…”

Section: Introductionmentioning

confidence: 99%

DTG Big Data Analysis for Fuel Consumption Estimation

2017

J Inf Process Syst

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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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Section: Introductionmentioning

confidence: 99%