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Weather factors such as temperature and rainfall in residential areas and tourist destinations affect traffic flow on the surrounding roads. In this study, we attempt to find new knowledge between traffic congestion and weather by using big data processing technology. Changes in traffic congestion due to the weather are evaluated by using multiple linear regression analysis to create a prediction...
Weather factors in travel spots and residential areas, such as temperature or precipitation, may cause a change in the floating population in travel spots during vacation seasons. This study aims to predict the daily floating population by creating a prediction model through a multiple linear regression analysis of the changes in the floating population based on weather factors. The regression analysis...
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...
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