ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit
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 d...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/8392592 |
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| author | Qicheng Tang Mengning Yang Ying Yang |
| author_facet | Qicheng Tang Mengning Yang Ying Yang |
| author_sort | Qicheng Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8fc74314178142cab6dc55367469403e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8fc74314178142cab6dc55367469403e2025-02-03T01:26:13ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/83925928392592ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail TransitQicheng Tang0Mengning Yang1Ying Yang2School of Big Data & Software Engineering, Chongqing University, Chongqing, ChinaSchool of Big Data & Software Engineering, Chongqing University, Chongqing, ChinaSchool of Big Data & Software Engineering, Chongqing University, Chongqing, ChinaThe 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.http://dx.doi.org/10.1155/2019/8392592 |
| spellingShingle | Qicheng Tang Mengning Yang Ying Yang ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit Journal of Advanced Transportation |
| title | ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit |
| title_full | ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit |
| title_fullStr | ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit |
| title_full_unstemmed | ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit |
| title_short | ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit |
| title_sort | st lstm a deep learning approach combined spatio temporal features for short term forecast in rail transit |
| url | http://dx.doi.org/10.1155/2019/8392592 |
| work_keys_str_mv | AT qichengtang stlstmadeeplearningapproachcombinedspatiotemporalfeaturesforshorttermforecastinrailtransit AT mengningyang stlstmadeeplearningapproachcombinedspatiotemporalfeaturesforshorttermforecastinrailtransit AT yingyang stlstmadeeplearningapproachcombinedspatiotemporalfeaturesforshorttermforecastinrailtransit |