A basis of spatial big data analysis with map-matching system

Abstract

The data collection of vehicle trajectories becomes the basis of big data analysis and prediction for a variety of purposes, such as vehicle navigation and movement analysis. A digital tachograph (DTG) is pre-installed on most commercial vehicles in South Korea and is highly valuable for analyzing eco-driving metrics such as safe driving and fuel consumption estimates. In order to properly analyze a large amount of GPS location information, it is necessary to find the exact match of the location data in space to the link in the digital road network data. We previously discovered the road information of the GPS coordinates using the commonly utilized map-matching technique. However, such a navigation map-matching technique requires a lot of supplementary corrections in order to rapidly and accurately navigate a large amount of data. In this study, we applied enhanced map-matching logics with Geohash as spatial index, long link vertex division, speed filtering, azimuth filtering, and map-matching weight logics. Also, we established and implemented a distributed analysis environment for the big data map-matching with HBase (a Hadoop-based NoSQL DB). This paper shows a spatial analysis system using the map-matching logics on the Hadoop MapReduce mechanism, which improved its performance.