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An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree
High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in complet...
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| Published in: | IEEE transactions on fuzzy systems 2023-02, Vol.31 (2), p.421-433 |
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| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c339t-5ca940b352a835c08e85035124e1adb1d8f8bacf137b737785b41abe12a1ab063 |
| container_end_page | 433 |
| container_issue | 2 |
| container_start_page | 421 |
| container_title | IEEE transactions on fuzzy systems |
| container_volume | 31 |
| creator | Zhang, Dalin Xu, Yi Peng, Yunjuan Du, Chenyue Wang, Nan Tang, Mincong Lu, Lingyun Liu, Jiqiang |
| description | High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable. |
| doi_str_mv | 10.1109/TFUZZ.2022.3181453 |
| format | article |
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Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.</description><identifier>ISSN: 1063-6706</identifier><identifier>EISSN: 1941-0034</identifier><identifier>DOI: 10.1109/TFUZZ.2022.3181453</identifier><identifier>CODEN: IEFSEV</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Data models ; Decision trees ; Delay ; Delays ; Error reduction ; Fuzzy decision tree ; Fuzzy logic ; graph community structure ; graph convolutional network ; High speed rail ; high-speed train station delay prediction ; interpretability ; Multiple objective analysis ; Neural networks ; Optimization ; Prediction algorithms ; Prediction models ; Predictive models ; Rail transportation ; Railway stations ; Spatiotemporal phenomena ; Task complexity ; Time series</subject><ispartof>IEEE transactions on fuzzy systems, 2023-02, Vol.31 (2), p.421-433</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-5ca940b352a835c08e85035124e1adb1d8f8bacf137b737785b41abe12a1ab063</citedby><cites>FETCH-LOGICAL-c339t-5ca940b352a835c08e85035124e1adb1d8f8bacf137b737785b41abe12a1ab063</cites><orcidid>0000-0003-1367-4242 ; 0000-0003-1147-4327 ; 0000-0001-9268-972X ; 0000-0003-0346-7020</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9792625$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>Zhang, Dalin</creatorcontrib><creatorcontrib>Xu, Yi</creatorcontrib><creatorcontrib>Peng, Yunjuan</creatorcontrib><creatorcontrib>Du, Chenyue</creatorcontrib><creatorcontrib>Wang, Nan</creatorcontrib><creatorcontrib>Tang, Mincong</creatorcontrib><creatorcontrib>Lu, Lingyun</creatorcontrib><creatorcontrib>Liu, Jiqiang</creatorcontrib><title>An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree</title><title>IEEE transactions on fuzzy systems</title><addtitle>TFUZZ</addtitle><description>High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.</description><subject>Artificial neural networks</subject><subject>Data models</subject><subject>Decision trees</subject><subject>Delay</subject><subject>Delays</subject><subject>Error reduction</subject><subject>Fuzzy decision tree</subject><subject>Fuzzy logic</subject><subject>graph community structure</subject><subject>graph convolutional network</subject><subject>High speed rail</subject><subject>high-speed train station delay prediction</subject><subject>interpretability</subject><subject>Multiple objective analysis</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Prediction algorithms</subject><subject>Prediction models</subject><subject>Predictive models</subject><subject>Rail transportation</subject><subject>Railway stations</subject><subject>Spatiotemporal phenomena</subject><subject>Task complexity</subject><subject>Time series</subject><issn>1063-6706</issn><issn>1941-0034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><recordid>eNo9kEtPwzAQhC0EEqXwB-BiiXOKH3GcHEuhpVJ5SE0vvUROshEpeWEnQil_HqetOM2uNTMrfwjdUjKhlAQP4Xyz3U4YYWzCqU9dwc_QiAYudQjh7rmdiccdTxLvEl0ZsyPEeqg_Qr_TCi-rFnSjoVVxAXjdqjavK_wEherxh4Y0Tw4Pr3UKBX5UBlJs14VWzSee1WXZVXnb4zfotCqstD-1_sKqSnGYl-CsQedg8Lzb73tbmuRmKAs1wDW6yFRh4OakY7SZP4ezF2f1vljOpisn4TxoHZGowCUxF0z5XCTEB18QLihzgao0pqmf-bFKMsplLLmUvohdqmKgTFmx_x6j-2Nvo-vvDkwb7epOV_ZkxKTkrvBYMLjY0ZXo2hgNWdTovFS6jyiJBsjRAXI0QI5OkG3o7hjKAeA_EMiAeUzwP6r3eU0</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Zhang, Dalin</creator><creator>Xu, Yi</creator><creator>Peng, Yunjuan</creator><creator>Du, Chenyue</creator><creator>Wang, Nan</creator><creator>Tang, Mincong</creator><creator>Lu, Lingyun</creator><creator>Liu, Jiqiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-1367-4242</orcidid><orcidid>https://orcid.org/0000-0003-1147-4327</orcidid><orcidid>https://orcid.org/0000-0001-9268-972X</orcidid><orcidid>https://orcid.org/0000-0003-0346-7020</orcidid></search><sort><creationdate>20230201</creationdate><title>An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree</title><author>Zhang, Dalin ; Xu, Yi ; Peng, Yunjuan ; Du, Chenyue ; Wang, Nan ; Tang, Mincong ; Lu, Lingyun ; Liu, Jiqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c339t-5ca940b352a835c08e85035124e1adb1d8f8bacf137b737785b41abe12a1ab063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial neural networks</topic><topic>Data models</topic><topic>Decision trees</topic><topic>Delay</topic><topic>Delays</topic><topic>Error reduction</topic><topic>Fuzzy decision tree</topic><topic>Fuzzy logic</topic><topic>graph community structure</topic><topic>graph convolutional network</topic><topic>High speed rail</topic><topic>high-speed train station delay prediction</topic><topic>interpretability</topic><topic>Multiple objective analysis</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Prediction algorithms</topic><topic>Prediction models</topic><topic>Predictive models</topic><topic>Rail transportation</topic><topic>Railway stations</topic><topic>Spatiotemporal phenomena</topic><topic>Task complexity</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Dalin</creatorcontrib><creatorcontrib>Xu, Yi</creatorcontrib><creatorcontrib>Peng, Yunjuan</creatorcontrib><creatorcontrib>Du, Chenyue</creatorcontrib><creatorcontrib>Wang, Nan</creatorcontrib><creatorcontrib>Tang, Mincong</creatorcontrib><creatorcontrib>Lu, Lingyun</creatorcontrib><creatorcontrib>Liu, Jiqiang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on fuzzy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Dalin</au><au>Xu, Yi</au><au>Peng, Yunjuan</au><au>Du, Chenyue</au><au>Wang, Nan</au><au>Tang, Mincong</au><au>Lu, Lingyun</au><au>Liu, Jiqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree</atitle><jtitle>IEEE transactions on fuzzy systems</jtitle><stitle>TFUZZ</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>31</volume><issue>2</issue><spage>421</spage><epage>433</epage><pages>421-433</pages><issn>1063-6706</issn><eissn>1941-0034</eissn><coden>IEFSEV</coden><abstract>High-speed train delay prediction has always been one of the important research issues in the railway dispatching. 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| ispartof | IEEE transactions on fuzzy systems, 2023-02, Vol.31 (2), p.421-433 |
| issn | 1063-6706 1941-0034 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Journals |
| subjects | Artificial neural networks Data models Decision trees Delay Delays Error reduction Fuzzy decision tree Fuzzy logic graph community structure graph convolutional network High speed rail high-speed train station delay prediction interpretability Multiple objective analysis Neural networks Optimization Prediction algorithms Prediction models Predictive models Rail transportation Railway stations Spatiotemporal phenomena Task complexity Time series |
| title | An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree |
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