과적화물차량 단속을 위한 화물 DTG 데이터 기반의 빅데이터 분석 프레임워크 설계 및 구현

국문 초록 (Abstract)

본 논문에서는 과적 화물차량 단속을 위한 대용량 화물 DTG 데이터 분석 플랫폼을 설계 및 구현한다. DTG(digital tachograph)는 차량운행기록을 실시간으로 저장하는 장치로서, 차량의 GPS, 속도, RPM, 제동유무, 이동거리 등 차량운행 관련 데이터가 1초 단위로 기록된다. 차량 운행 패턴 및 분석을 하기 위해서는 DTG 데이터의 빠른 처리가 필수적이며, 특히 대용량 DTG 데이터를 가공 및 변환하기 위해서는 빅데이터 분석 플랫폼이 필요하다. 본 논문에서는 오픈소스 기반의 빅데이터 프레임워크인 스파크(Spark)를 이용하여 과적차량 단속을 위한 대용량 화물 DTG 데이터의 분석 플랫폼을 구현하였다. 구현 결과, 실제 대용량 화물 DTG 데이터를 GIS 데이터로 변환하여 지도상에 표현하고 단속 추천 지점을 보여준다.

다국어 초록 (Multilingual Abstract)

In this paper, we design and implement an analytics platform based on bulk cargo DTG data for crackdown on overloaded trucks. DTG(digital tachograph) is a device that stores the driving record in real time; that is, it is a device that records the vehicle driving related data such as GPS, speed, RPM, braking, and moving distance of the vehicle in one second unit. The fast processing of DTG data is essential for finding vehicle driving patterns and analytics. In particular, a big data analytics platform is required for preprocessing and converting large amounts of DTG data. In this paper, we implement a big data analytics framework based on cargo DTG data using Spark, which is an open source-based big data framework for crackdown on overloaded trucks. As the result of implementation, our proposed platform converts real large cargo DTG data sets into GIS data, and these are visualized by a map. It also recommends crackdown points.

목차 (Table of Contents)

• Abstract
• 요약
• Ⅰ. Introduction
• Ⅱ. Related works
• Ⅲ. The proposed big data analytics framework
• Ⅳ. Implementation of an index-based system for partial denoising boundary matching
• Ⅴ. Conclusions
• REFERENCES

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