ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit
| Autor: | Yang Ying, Mengning Yang, Qicheng Tang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Economics and Econometrics
Article Subject Computer science Strategy and Management Flow (psychology) Real-time computing 02 engineering and technology Plan (drawing) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Intelligent transportation system 050210 logistics & transportation business.industry Mechanical Engineering Deep learning 05 social sciences Rail transit Process (computing) lcsh:TA1001-1280 lcsh:HE1-9990 Computer Science Applications Term (time) Automotive Engineering 020201 artificial intelligence & image processing Artificial intelligence lcsh:Transportation engineering lcsh:Transportation and communications business |
| Zdroj: | Journal of Advanced Transportation, Vol 2019 (2019) |
| ISSN: | 0197-6729 |
| DOI: | 10.1155/2019/8392592 |
| Popis: | The short-term forecast of rail transit is one of the most essential issues in urban intelligent transportation system (ITS). Accurate forecast result can provide support for the forewarning of flow outburst and enables passengers to make an appropriate travel plan. Therefore, it is significant to develop a more accurate forecast model. Long short-term memory (LSTM) network has been proved to be effective on data with temporal features. However, it cannot process the correlation between time and space in rail transit. As a result, a novel forecast model combining spatio-temporal features based on LSTM network (ST-LSTM) is proposed. Different from other forecast methods, ST-LSTM network uses a new method to extract spatio-temporal features from the data and combines them together as the input. Compared with other conventional models, ST-LSTM network can achieve a better performance in experiments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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