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A Transfer Learning-Based LSTM for Traffic Flow Prediction with Missing Data
| 题名： | A Transfer Learning-Based LSTM for Traffic Flow Prediction with Missing Data |
| 正文语种： | eng |
| 作者： | Zhao Zhang;Hao Yang;Xianfeng Yang |
| 作者单位： | Dept. of Civil and Environmental Engineering Univ. of Utah 110 S. Central Campus Dr. Suite 2000 Salt Lake City UT 84112;Dept. of Civil Engineering McMaster Univ. 1280 Main St. West John Hodgins Engineering Bldg. Hamilton ON Canada L8S 4L7;Dept. of Civil and Environmental Engineering Univ. of Maryland 1173 Glenn L. Martin Hall College Park MD 20742 |
| 关键词： | Traffic flow prediction; Transfer learning; Long short-term memory (LSTM) network; Missing data |
| 摘要： | Traffic flow prediction plays an important role in intelligent transportation systems (ITS) on freeways. However, incomplete traffic information tends to be collected by traffic detectors, which is a major constraint for existing methods to get precise traffic predictions. To overcome this limitation, this study aims to propose and evaluate a new advanced model, named transfer learning-based long short-term memory (LSTM) model for traffic flow forecasting with incomplete traffic information, that adopts traffic information from similar locations for the target location to increase the data quality. More specifically, dynamic time warping (DTW) is used to evaluate the similarity between the source and target domains and then transfer the most similar data to the target domain to generate a hybrid complete training sample for LSTM to improve the prediction performance. To evaluate the effectiveness of the transfer learning-based LSTM, this study implements empirical studies with a real-world data set collected from a stretch of I-15 freeway in Utah. Experimental study results indicate that the transfer learning-based LSTM network could effectively predict the traffic flow conditions with a training sample with missing values. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 10 |
| 页码： | 04023095.1-04023095.9 |
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