Spatial big data analysis system for vehicle-driving GPS trajectory

Abstract

The data collection of vehicle-driving GPS trajectory becomes the basis of big data analysis and prediction for a variety of purposes, such as navigation and movement analysis. In order to properly analyze a large amount of GPS location information, it is necessary to determine the exact road map and location data by matching a digital map and space. 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 apply geohash indexing and long link vertex dividing preprocessing to spatial data for performance improvement of massive data map matching. Also speed filtering logic is applied together for qualified analysis. We established and implemented a distributed analysis environment for the better big data map-matching with HBase. Altogether we constructed a spatial analysis system using the MapReduce mechanism, which improved its performance. This paper shows that our analysis system provides the 44 times performance achievement compared to traditional mysql DB processing with mesh structure for 5,000,000 cases of GPS trajectory.