http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
시계열 교통데이터 기반 고속도로 교통흐름 예측 모델 연구
강동묵(DongMug Kang),이명오(MyungOh Lee),김용현(YongHyun Kim),윤상훈(SangHun Yoon),신대교(DaeKyo Shin),장수현(SooHyun Jang) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
There are short sections where congestion often occurs on the highway, such as the exit route of the highway. If traffic flow can be predicted in the future point for these sections, drivers can drive actively and efficiently in consideration of road conditions. In this paper, we propose study on traffic flow prediction based on time series data of highway and average speed is used as traffic flow. In the case of time series data, it is extracted through speed estimation and object detection algorithms from 10 CCTV video installed on Yeongdong highway(Maseong IC ~ Singal JC, 3.4km) in Korea. For traffic flow prediction, we use Conv2D-LSTM model that consider spatio-temporal features. Also, in order to intuitively and efficiently represent the traffic congestion degree of a highway, traffic congestion parameter is proposed. As a result, the prediction model shows performance with an error of 7.08 based on the MAE(Mean Absolute Error), and the speed at the future point can be predicted.