Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network
Xinqiang Chen,
Jinquan Lu,
Jiansen Zhao,
Zhijian Qu,
Yongsheng Yang and
Jiangfeng Xian
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Xinqiang Chen: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Jinquan Lu: Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Jiansen Zhao: Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Zhijian Qu: Electrical and Automation Engineering College, East China Jiaotong University, Nanchang 330013, China
Yongsheng Yang: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Jiangfeng Xian: Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Sustainability, 2020, vol. 12, issue 9, 1-17
Abstract:
Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.
Keywords: traffic flow data; prediction; denoising; varied time scales (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (12)
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