Peak Hour Identification for Traffic Congestion Based on IoT Environments
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초록
This study deals with the analysis of traffic congestion and the peak hour identification by using Kalman Filter and Ensemble Model. There are different types of traffic congestion, Roadway Traffic congestion, Airways Traffic congestion, Network Traffic congestion, and so on. This study focuses on Roadway Traffic congestion. The peak hour identification is essential to prevent roadway traffic congestion. In roadway traffic congestion, there are two categories in traffic data, namely Roadside Equipment (RSE) data and Video Detection System (VDS) data. Both data were collected from RSE devices and VDS devices, which are located in roadways signals, toll plaza, private sectors, and etc. In traffic data, it may contain error values. So, this paper applies the Kalman Filter for the purpose of removing the error values or inaccurate values and providing the cleaned Traffic data. The suggested study also uses the Ensemble Model to average the traffic data at corresponding hours easily to analyse the traffic data. To identify peak hour, it defines four different models by considering numbers, average times, and average speeds of vehicles. With the suggested method, the perfect peak hour in the traffic data can be easily and exactly obtained. In the tests and results, this paper showed the detailed process of peak hour identification in traffic congestion.
Abstract
This study deals with the analysis of traffic congestion and the peak hour identification by using Kalman Filter and Ensemble Model. There are different types of traffic congestion, Roadway Traffic congestion, Airways Traffic congestion, Network Traffic congestion, and so on. This study focuses on Roadway Traffic congestion. The peak hour identification is essential to prevent roadway traffic congestion. In roadway traffic congestion, there are two categories in traffic data, namely Roadside Equipment (RSE) data and Video Detection System (VDS) data. Both data were collected from RSE devices and VDS devices, which are located in roadways signals, toll plaza, private sectors, and etc. In traffic data, it may contain error values. So, this paper applies the Kalman Filter for the purpose of removing the error values or inaccurate values and providing the cleaned Traffic data. The suggested study also uses the Ensemble Model to average the traffic data at corresponding hours easily to analyse the traffic data. To identify peak hour, it defines four different models by considering numbers, average times, and average speeds of vehicles. With the suggested method, the perfect peak hour in the traffic data can be easily and exactly obtained. In the tests and results, this paper showed the detailed process of peak hour identification in traffic congestion.
참고문헌 (20)
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