Time-Series Data and Analysis Software of Connected Vehicles

Abstract

In this study, we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles. We designed two software modules: The first to derive the Pearson correlation coefficients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data. In particular, we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority. We also analyzed seasonal fuel efficiency (four seasons) and mileage of vehicles, and identified rapid acceleration, rapid deceleration, sudden stopping (harsh braking), quick starting, sudden left turn, sudden right turn and sudden U-turn driving patterns of vehicles. We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS (Global Positioning System) data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis. In this paper, we mainly describe the development environment of the analysis software, the structure and data flow of the overall analysis platform, the configuration of the collected vehicle data, and the various algorithms used in the analysis. Finally, we present illustrative results of our analysis, such as dangerous driving patterns that were detected.