{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00238000","sets":["581:11492:11501"]},"path":["11501"],"owner":"44499","recid":"238000","title":["K-neighboring on Multi-weighted Graphs for Passenger Count Prediction on Railway Networks"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-08-15"},"_buckets":{"deposit":"51fa7083-3dd4-4c2b-b525-cc5ee939a284"},"_deposit":{"id":"238000","pid":{"type":"depid","value":"238000","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"K-neighboring on Multi-weighted Graphs for Passenger Count Prediction on Railway Networks","author_link":["651658","651655","651657","651660","651659","651656"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"K-neighboring on Multi-weighted Graphs for Passenger Count Prediction on Railway Networks"},{"subitem_title":"K-neighboring on Multi-weighted Graphs for Passenger Count Prediction on Railway Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:“Applications and the Internet” in Conjunction with the Main Topics of COMPSAC 2023] traffic prediction, railway network, graph neural network, smart city","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-08-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo"},{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo"},{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Interfaculty Initiative in Information Studies, The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/238000/files/IPSJ-JNL6508003.pdf","label":"IPSJ-JNL6508003.pdf"},"date":[{"dateType":"Available","dateValue":"2026-08-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6508003.pdf","filesize":[{"value":"6.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"37b37d64-2173-4bf4-8d77-9b25ca233ab1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hangli, Ge"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Michikata"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Noboru, Koshizuka"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hangli, Ge","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Michikata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Noboru, Koshizuka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In this paper, a method of multi-weighted graphs learning for passenger count prediction in railway networks is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by the complexity of the railway networks as well as other factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task. To achieve high prediction performance of the models and discover the correlation between the models' performance and features, we propose six heterogenerous weight graphs, i.e., connection graph, distance graph, correlation graph and their fused graphs to fully construct the spatial and geometrical features of the railway network. Two representative graph neural networks (GNN), that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Moreover, to investigate the trade-off on the graph scale and the prediction performance, we improved our model to search the optimal K value of neighbor nodes for graph partition for optimization. The K-neighboring method shows small scale graph also has the opportunity to achieve promising prediction result. The effectiveness of the proposal including multiple weight graphs as well as the optimal K-value for neighboring also were investigated. It can provide the interpretability of the traffic prediction tasks on the railway network. \n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.575\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, a method of multi-weighted graphs learning for passenger count prediction in railway networks is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by the complexity of the railway networks as well as other factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task. To achieve high prediction performance of the models and discover the correlation between the models' performance and features, we propose six heterogenerous weight graphs, i.e., connection graph, distance graph, correlation graph and their fused graphs to fully construct the spatial and geometrical features of the railway network. Two representative graph neural networks (GNN), that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Moreover, to investigate the trade-off on the graph scale and the prediction performance, we improved our model to search the optimal K value of neighbor nodes for graph partition for optimization. The K-neighboring method shows small scale graph also has the opportunity to achieve promising prediction result. The effectiveness of the proposal including multiple weight graphs as well as the optimal K-value for neighboring also were investigated. It can provide the interpretability of the traffic prediction tasks on the railway network. \n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.575\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2024-08-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":238000,"updated":"2025-01-19T08:43:41.508644+00:00","links":{},"created":"2025-01-19T01:40:57.768388+00:00"}