Passenger Flow Forecast of Rail Station Based on Multi-Source Data and Long Short Term Memory Network
The existing rail station passenger flow prediction models are inefficient, due to that most of them use single-source data to predict. In this paper, a novel method is proposed based on multi-layer LSTM, which integrates multi-source traffic data and multi-techniques (including feature selection ba...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.28475-28483 |
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| creator | Zhang, Zhe Wang, Cheng Gao, Yueer Chen, Yewang Chen, Jianwei |
| description | The existing rail station passenger flow prediction models are inefficient, due to that most of them use single-source data to predict. In this paper, a novel method is proposed based on multi-layer LSTM, which integrates multi-source traffic data and multi-techniques (including feature selection based on Spearman correlation and time feature clustering), to improve the performance of predicting passenger flow. The experimental results show that the multi-source data and the techniques integrated in the model are helpful, and the proposed method obtains a higher prediction accuracy which outperforms other methods (e.g. SARIMA, SVR and BP network) greatly. |
| doi_str_mv | 10.1109/ACCESS.2020.2971771 |
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In this paper, a novel method is proposed based on multi-layer LSTM, which integrates multi-source traffic data and multi-techniques (including feature selection based on Spearman correlation and time feature clustering), to improve the performance of predicting passenger flow. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-6cc8020f6d18d3c7acdd3b568b58932b0cb2ccf51b5bbeb4a86ec69ace7873753</citedby><cites>FETCH-LOGICAL-c408t-6cc8020f6d18d3c7acdd3b568b58932b0cb2ccf51b5bbeb4a86ec69ace7873753</cites><orcidid>0000-0003-2465-3914 ; 0000-0001-6075-2878</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8984375$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27612,27902,27903,27904,54910</link.rule.ids></links><search><creatorcontrib>Zhang, Zhe</creatorcontrib><creatorcontrib>Wang, Cheng</creatorcontrib><creatorcontrib>Gao, Yueer</creatorcontrib><creatorcontrib>Chen, Yewang</creatorcontrib><creatorcontrib>Chen, Jianwei</creatorcontrib><title>Passenger Flow Forecast of Rail Station Based on Multi-Source Data and Long Short Term Memory Network</title><title>IEEE access</title><addtitle>Access</addtitle><description>The existing rail station passenger flow prediction models are inefficient, due to that most of them use single-source data to predict. 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| subjects | Autoregressive processes Clustering Clustering algorithms Correlation Data models K-means long short term memory network multi-source data Multilayers Passengers Performance prediction Prediction algorithms prediction model Prediction models Predictive models Rail transit passenger flow Rails Railway engineering Railway stations spearman correlation Traffic information |
| title | Passenger Flow Forecast of Rail Station Based on Multi-Source Data and Long Short Term Memory Network |
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