期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 121, 期 -, 页码 304-312出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2018.12.031
关键词
Daily long-term traffic flow; Forecasting; Deep neural network; Contextual factor; Batch training
类别
资金
- National Natural Science Foundation of China [61773337, 61773338]
- Zhejiang Provincial Natural Science Foundation [LY17F030009]
- Fundamental Research Funds for the Central Universities [2018QNA4050]
- Zhejiang Province Key Research and Development Plan [2018C01007]
- National key research and development program [2016YFE0108000]
Daily traffic flow forecasting is critical in advanced traffic management and can improve the efficiency of fixed-time signal control. This paper presents a traffic prediction method for one whole day using a deep neural network based on historical traffic flow data and contextual factor data. The main idea is that traffic flow within a short time period is strongly correlated with the starting and ending time points of the period together with a number of other contextual factors, such as day of week, weather, and season. Therefore, the relationship between the traffic flow values within a given time interval and a combination of contextual factors can be mined from historical data. First, a predictor was trained using a multi-layer supervised learning algorithm to mine the potential relationship between traffic flow data and a combination of key contextual factors. To reduce training times, a batch training method was proposed. Finally, a Seattle-based case study shows that, overall, the proposed method outperforms the conventional traffic prediction method in terms of prediction accuracy. (C) 2018 Elsevier Ltd. All rights reserved.
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