Name
Title | ||
|---|---|---|
Big Data Processing for Prediction of Traffic Time Based on Vertical Data Arrangement |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CloudCom.2014.54 | CloudCom |
Keywords | Field | DocType |
rhive,parallel processing,predictive graphs,spatiotemporal prediction map,big traffic data,forecasting theory,statistics analysis,vertical data arrangement,statistical analysis,two-dimensional map,r clusters,time-series forecasting methods,traffic condition prediction,time-series forecasting method,spatiotemporal phenomena,hadoop clusters,traffic times,big data processing tools,r-squared value,traffic information systems,graph theory,big data,road traffic,big data processing,predicted data,time series,traffic time prediction,spatiotemporal pattern,statistical model,time-series variables,market research,forecasting,predictive models,accuracy | Big data processing,Data mining,Graph,Time series,Airfield traffic pattern,Computer science,Statistical model,Artificial intelligence,Market research,Traffic conditions,Machine learning,Spatiotemporal pattern | Conference |
ISSN | Citations | PageRank |
2330-2194 | 2 | 0.39 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seungwoo Jeon | 1 | 7 | 1.18 |
| Bonghee Hong | 2 | 140 | 68.51 |
| Byungsoo Kim | 3 | 23 | 4.50 |