Journal
IEEE ACCESS
Volume 6, Issue -, Pages 75629-75638Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2879055
Keywords
Short-term traffic flow; forecasting; pattern matching; CNNs-LSTM
Categories
Funding
- National Natural Science Foundation of China [61773337, 61773338, 91746105]
- Zhejiang Provincial Natural Science Foundation [LR19F030002, LY17F030009]
- Fundamental Research Funds for the Central Universities [2018QNA050, 2018QNA4035]
- Zhejiang Province Key Research and Development Plan [2018C01007]
Ask authors/readers for more resources
Forecasting short-term traffic flow is one critical component in traffic management to improve operational efficiency. Data driven method, which trains the predictor with historical data across a given past period, have been proved to perform well. However, days which experience significantly different traffic flow patterns, negatively influence forecasting results. This paper proposes an advanced method, making use of appropriate prediction based on pattern matching. First, historical data is divided into several groups, according to their patterns, by clustering algorithms. Then the predictor is trained for each group based on a convolutional neural networks and long-short-term-memory model. For each time point, the degree of similarity between the target day and each group is measured, and the predictor trained by the group possessing the highest degree of similarity is selected to be appropriate. Based on a case study from Seattle, we show that selecting an appropriate predictor can significantly improve the accuracy of predictions. In addition, we demonstrate that the new method can, in general, outperform alternative methods in terms of prediction accuracy and stability.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available