{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:14:25Z","timestamp":1774466065138,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781711","type":"print"},{"value":"9783031781728","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78172-8_30","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T09:47:55Z","timestamp":1733132875000},"page":"461-477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GCompletor: A Graph-Based Deep Learning Method for Traffic State Imputation on Urban Road Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1697-3323","authenticated-orcid":false,"given":"Kaijie","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1002-9272","authenticated-orcid":false,"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-1345","authenticated-orcid":false,"given":"Li","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2063-2051","authenticated-orcid":false,"given":"Xitong","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8929-7032","authenticated-orcid":false,"given":"Ye","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6133-407X","authenticated-orcid":false,"given":"Kejiang","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.arcontrol.2017.03.005","volume":"43","author":"T Seo","year":"2017","unstructured":"Seo, T., Bayen, A.M., Kusakabe, T., Asakura, Y.: Traffic state estimation on highway: a comprehensive survey. Annu. Rev. Control. 43, 128\u2013151 (2017)","journal-title":"Annu. Rev. Control."},{"issue":"2","key":"30_CR2","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1109\/TNET.2019.2899777","volume":"27","author":"P Nayak","year":"2019","unstructured":"Nayak, P., Garetto, M., Knightly, E.W.: Modeling multi-user wlans under closed-loop traffic. IEEE\/ACM Trans. Networking 27(2), 763\u2013776 (2019)","journal-title":"IEEE\/ACM Trans. Networking"},{"issue":"45","key":"30_CR3","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1177\/0361198118777631","volume":"2672","author":"P Chakraborty","year":"2018","unstructured":"Chakraborty, P., Adu-Gyamfi, Y.O., Poddar, S., Ahsani, V., Sharma, A., Sarkar, S.: Traffic congestion detection from camera images using deep convolution neural networks. Transp. Res. Rec. 2672(45), 222\u2013231 (2018)","journal-title":"Transp. Res. Rec."},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.trpro.2017.03.057","volume":"22","author":"O Altintasi","year":"2017","unstructured":"Altintasi, O., Tuydes-Yaman, H., Tuncay, K.: Detection of urban traffic patterns from floating car data (FCD). Transp. Res. Procedia 22, 382\u2013391 (2017)","journal-title":"Transp. Res. Procedia"},{"issue":"3","key":"30_CR5","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1109\/TITS.2009.2026312","volume":"10","author":"L Qu","year":"2009","unstructured":"Qu, L., Li, L., Zhang, Y., Hu, J.: PPCA-based missing data imputation for traffic flow volume: a systematical approach. IEEE Trans. Intell. Transp. Syst. 10(3), 512\u2013522 (2009)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Goulart, J.D.M., Kibangou, A., Favier, G.: Traffic data imputation via tensor completion based on soft thresholding of tucker core. Transp. Res. Part C Emerg. Technol. 85, 348\u2013362 (2017)","DOI":"10.1016\/j.trc.2017.09.011"},{"issue":"2","key":"30_CR7","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1080\/15472450.2015.1015721","volume":"20","author":"B Ran","year":"2016","unstructured":"Ran, B., Tan, H., Feng, J., Wang, W., Cheng, Y., Jin, P.: Estimating missing traffic volume using low multilinear rank tensor completion. J. Intell. Transp. Syst. 20(2), 152\u2013161 (2016)","journal-title":"J. Intell. Transp. Syst."},{"issue":"8","key":"30_CR8","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1002\/atr.1443","volume":"50","author":"L Li","year":"2016","unstructured":"Li, L., He, S., Zhang, J., Ran, B.: Short-term highway traffic flow prediction based on a hybrid strategy considering temporal-spatial information. J. Adv. Transp. 50(8), 2029\u20132040 (2016)","journal-title":"J. Adv. Transp."},{"issue":"3","key":"30_CR9","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1080\/15472450.2012.694788","volume":"16","author":"W Yin","year":"2012","unstructured":"Yin, W., Murray-Tuite, P., Rakha, H.: Imputing erroneous data of single-station loop detectors for nonincident conditions: comparison between temporal and spatial methods. J. Intell. Transp. Syst. 16(3), 159\u2013176 (2012)","journal-title":"J. Intell. Transp. Syst."},{"issue":"12","key":"30_CR10","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1061\/(ASCE)0733-947X(2005)131:12(931)","volume":"131","author":"D Ni","year":"2005","unstructured":"Ni, D., Leonard, J.D., Guin, A., Feng, C.: Multiple imputation scheme for overcoming the missing values and variability issues in its data. J. Transp. Eng. 131(12), 931\u2013938 (2005)","journal-title":"J. Transp. Eng."},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Xu, J.R., Li, X.Y., Shi, H.J.: Short-term traffic flow forecasting model under missing data. J. Comput. Appl.30(4), 1117\u20131120 (2010)","DOI":"10.3724\/SP.J.1087.2010.01117"},{"issue":"1","key":"30_CR12","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1177\/0361198105193500107","volume":"1935","author":"D Ni","year":"2005","unstructured":"Ni, D., Leonard, J.D.: Markov chain Monte Carlo multiple imputation using Bayesian networks for incomplete intelligent transportation systems data. Transp. Res. Rec. 1935(1), 57\u201367 (2005)","journal-title":"Transp. Res. Rec."},{"issue":"3","key":"30_CR13","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.trc.2010.10.004","volume":"19","author":"MG Karlaftis","year":"2011","unstructured":"Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp. Res. Part C Emerg. Technol. 19(3), 387\u2013399 (2011)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Wang, J., Jiang, J., Jiang, W., Li, C., Zhao, W.X. (eds.): LibCity: an open library for traffic prediction. In: ACM (2021)","DOI":"10.1145\/3474717.3483923"},{"issue":"5","key":"30_CR15","doi-asserted-by":"publisher","first-page":"3904","DOI":"10.1109\/TITS.2020.3043250","volume":"23","author":"J Ye","year":"2020","unstructured":"Ye, J., Zhao, J., Ye, K., Xu, C.: How to build a graph-based deep learning architecture in traffic domain: a survey. IEEE Trans. Intell. Transp. Syst. 23(5), 3904\u20133924 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Ye, J., Zhao, J., Ye, K., Xu, C.: Multi-STGCnet: a graph convolution based spatial-temporal framework for subway passenger flow forecasting. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207049"},{"key":"30_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.trc.2017.10.023","volume":"86","author":"X Chen","year":"2018","unstructured":"Chen, X., He, Z., Wang, J.: Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transp. Res. part C Emerg. Technol. 86, 59\u201377 (2018)","journal-title":"Transp. Res. part C Emerg. Technol."},{"issue":"10","key":"30_CR18","doi-asserted-by":"publisher","first-page":"11950","DOI":"10.1109\/TVT.2020.3007025","volume":"69","author":"L Han","year":"2020","unstructured":"Han, L., Zheng, K., Zhao, L., Wang, X., Wen, H.: Content-aware traffic data completion in its based on generative adversarial nets. IEEE Trans. Veh. Technol. 69(10), 11950\u201311962 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"30_CR19","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.trc.2016.09.015","volume":"72","author":"Y Duan","year":"2016","unstructured":"Duan, Y., Lv, Y., Liu, Y.L., Wang, F.Y.: An efficient realization of deep learning for traffic data imputation. Transp. Res. part C Emerg. Technol. 72, 168\u2013181 (2016)","journal-title":"Transp. Res. part C Emerg. Technol."},{"key":"30_CR20","unstructured":"BiGRU: Bigru. https:\/\/github.com\/topics\/bigru"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Xu, D.W., Wang, Y.D., Jia, L.M., Qin, Y., Dong, H.H.: Real-time road traffic state prediction based on ARIMA and kalman filter. Front. Inf. Technol. Electron. Eng. 18(2), 287\u2013302 (2017)","DOI":"10.1631\/FITEE.1500381"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, pp. 3634\u20133640 (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"issue":"9","key":"30_CR24","first-page":"4659","volume":"44","author":"X Chen","year":"2021","unstructured":"Chen, X., Sun, L.: Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4659\u20134673 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR25","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.trc.2018.11.003","volume":"98","author":"X Chen","year":"2018","unstructured":"Chen, X., He, Z., Sun, L.: A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transp. Res. Part C Emerg. Technol. 98, 73\u201384 (2018)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"4","key":"30_CR26","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TITS.2019.2910295","volume":"21","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Lv, Y., Wang, F.Y.: Traffic flow imputation using parallel data and generative adversarial networks. IEEE Trans. Intell. Transp. Syst. 21(4), 1624\u20131630 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78172-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T10:08:41Z","timestamp":1733134121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78172-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031781711","9783031781728"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78172-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}