Big Data Processing for Prediction of Traffic Time Based on Vertical Data Arrangement

To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.