{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:35:17Z","timestamp":1743140117939,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031105241"},{"type":"electronic","value":"9783031105258"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10525-8_33","type":"book-chapter","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T19:45:27Z","timestamp":1658519127000},"page":"419-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for\u00a0Capacity Utilization Along the\u00a0Routes of\u00a0an\u00a0Urban Freight Service"],"prefix":"10.1007","author":[{"given":"Mandar V.","family":"Tabib","sequence":"first","affiliation":[]},{"given":"Jon K\u00e5re","family":"Stene","sequence":"additional","affiliation":[]},{"given":"Adil","family":"Rasheed","sequence":"additional","affiliation":[]},{"given":"Ove","family":"Langeland","sequence":"additional","affiliation":[]},{"given":"Frants","family":"Gundersen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"issue":"3","key":"33_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"JS Alex","year":"2004","unstructured":"Alex, J.S., Bernhard, S.: A tutorial on support vector regression. Stat. Comput. Arch. 14(3), 199\u2013222 (2004)","journal-title":"Stat. Comput. Arch."},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.procs.2015.05.004","volume":"52","author":"S Bakhtyar","year":"2015","unstructured":"Bakhtyar, S., Holmgren, J.: A data mining based method for route and freight estimation. Procedia Comput. Sci. 52, 396\u2013403 (2015)","journal-title":"Procedia Comput. Sci."},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Barua, L., Zou, B., Zhou, Y.: Machine learning for international freight transportation management: a comprehensive review. Res. Transp. Bus. Manag. 34, 100453 (2020)","DOI":"10.1016\/j.rtbm.2020.100453"},{"key":"33_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","volume-title":"Neural Networks: Tricks of the Trade","author":"Y Bengio","year":"2012","unstructured":"Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437\u2013478. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_26"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144\u2013152. Association for Computing Machinery, New York (1992)","DOI":"10.1145\/130385.130401"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Boukerche, A., Wang, J.: Machine learning-based traffic prediction models for intelligent transportation systems. Comput. Netw. 181, 107530 (2020)","DOI":"10.1016\/j.comnet.2020.107530"},{"issue":"1","key":"33_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Gao, Y.: Forecasting of freight volume based on support vector regression optimized by genetic algorithm. In: 2009 2nd IEEE International Conference on Computer Science and Information Technology, pp. 550\u2013553 (2009)","DOI":"10.1109\/ICCSIT.2009.5234798"},{"issue":"5","key":"33_CR9","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1515\/jisys-2016-0203","volume":"28","author":"Z Guo","year":"2019","unstructured":"Guo, Z., Fu, J.Y.: Prediction method of railway freight volume based on genetic algorithm improved general regression neural network. J. Intell. Syst. 28(5), 835\u2013848 (2019)","journal-title":"J. Intell. Syst."},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Hassan, L.A.H., Mahmassani, H.S., Chen, Y.: Reinforcement learning framework for freight demand forecasting to support operational planning decisions. Transp. Res. Part E: Logist. Transp. Rev. 137, 101926 (2020)","DOI":"10.1016\/j.tre.2020.101926"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, H., Chang, L., Li, Q., Chen, D.: Trajectory prediction of vehicles based on deep learning. In: 2019 4th International Conference on Intelligent Transportation Engineering (ICITE), pp. 190\u2013195 (2019)","DOI":"10.1109\/ICITE.2019.8880168"},{"issue":"7553","key":"33_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.trpro.2016.02.049","volume":"12","author":"M Ruesch","year":"2016","unstructured":"Ruesch, M., Schmid, T., Bohne, S., Haefeli, U., Walker, D.: Freight transport with VANs: developments and measures. Transp. Res. Procedia 12, 79\u201392 (2016)","journal-title":"Transp. Res. Procedia"},{"issue":"6088","key":"33_CR14","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"D Rumelhart","year":"1986","unstructured":"Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","journal-title":"Nature"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Taniguchi, E., Thompson, R., Yamada, T., Van Duin, J.: City logistics. In: Network Modelling and Intelligent Transport Systems, January 2001","DOI":"10.1108\/9780585473840"}],"container-title":["Communications in Computer and Information Science","Intelligent Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10525-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T18:38:08Z","timestamp":1727635088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10525-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031105241","9783031105258"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10525-8_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"INTAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grimstad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intap2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.uia.no\/en\/events\/4th-international-conference-on-intelligent-technologies-and-applications-intap-2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"243","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}