{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:15:16Z","timestamp":1743052516180,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030876715"},{"type":"electronic","value":"9783030876722"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87672-2_34","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T21:02:46Z","timestamp":1632258166000},"page":"518-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Location of Roadside Assistance Resources Through Incident Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3755-8465","authenticated-orcid":false,"given":"Roman","family":"Buil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-7066","authenticated-orcid":false,"given":"Santiago","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7619-7407","authenticated-orcid":false,"given":"Jesica","family":"de Armas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4718-7234","authenticated-orcid":false,"given":"Daniel","family":"Riera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1016\/j.jclepro.2018.08.207","volume":"203","author":"MW Ahmad","year":"2018","unstructured":"Ahmad, M.W., Reynolds, J., Rezgui, Y.: Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J. Cleaner Prod. 203, 810\u2013821 (2018). https:\/\/doi.org\/10.1016\/j.jclepro.2018.08.207","journal-title":"J. Cleaner Prod."},{"key":"34_CR2","doi-asserted-by":"publisher","unstructured":"Ban, T., Zhang, R., Pang, S., Sarrafzadeh, A., Inoue, D.: Referential knn regression for financial time series forecasting. In: Lee, M., Hirose, A., Hou, Z.G., Kil, R.M. (eds.) Neural Information Processing, pp. 601\u2013608. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-42054-2_75","DOI":"10.1007\/978-3-642-42054-2_75"},{"key":"34_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1007\/978-3-030-59747-4_32","volume-title":"Computational Logistics","author":"B Beirigo","year":"2020","unstructured":"Beirigo, B., Schulte, F., Negenborn, R.R.: Overcoming mobility poverty with shared autonomous vehicles: a learning-based optimization approach for rotterdam zuid. In: Lalla-Ruiz, E., Mes, M., Vo\u00df, S. (eds.) ICCL 2020. LNCS, vol. 12433, pp. 492\u2013506. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_32"},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.ijforecast.2013.07.005","volume":"30","author":"S Ben Taieb","year":"2014","unstructured":"Ben Taieb, S., Hyndman, R.J.: A gradient boosting approach to the kaggle load forecasting competition. Int. J. Forecast. 30, 382\u2013394 (2014). https:\/\/doi.org\/10.1016\/j.ijforecast.2013.07.005","journal-title":"Int. J. Forecast."},{"key":"34_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/978-3-642-38697-8_14","volume-title":"Application and Theory of Petri Nets and Concurrency","author":"M Clemente","year":"2013","unstructured":"Clemente, M., Fanti, M.P., Mangini, A.M., Ukovich, W.: The vehicle relocation problem in car sharing systems: modeling and simulation in a petri net framework. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 250\u2013269. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38697-8_14"},{"key":"34_CR6","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1016\/j.neucom.2015.08.051","volume":"173","author":"GF Fan","year":"2016","unstructured":"Fan, G.F., Peng, L.L., Hong, W.C., Sun, F.: Electric load forecasting by the svr model with differential empirical mode decomposition and auto regression. Neurocomputing 173, 958\u2013970 (2016). https:\/\/doi.org\/10.1016\/j.neucom.2015.08.051","journal-title":"Neurocomputing"},{"key":"34_CR7","unstructured":"Gregory, B.: Predicting customer churn: Extreme gradient boosting with temporal data (2018)"},{"key":"34_CR8","volume-title":"Forecasting: Principles and Practice","author":"R Hyndman","year":"2018","unstructured":"Hyndman, R., Athanasopoulos, G.: Forecasting: Principles and Practice, 2nd edn. OTexts, Melbourne (2018)","edition":"2"},{"key":"34_CR9","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.trb.2018.12.006","volume":"120","author":"S Illgen","year":"2019","unstructured":"Illgen, S., H\u00f6ck, M.: Literature review of the vehicle relocation problem in one-way car sharing networks. Transp. Res. Part B Methodol. 120, 193\u2013204 (2019). https:\/\/doi.org\/10.1016\/j.trb.2018.12.006","journal-title":"Transp. Res. Part B Methodol."},{"key":"34_CR10","doi-asserted-by":"publisher","unstructured":"Jin, X., Han, J.: K-means clustering, pp. 563\u2013564. Springer, Boston (2010). https:\/\/doi.org\/10.1007\/978-0-387-30164-8_425","DOI":"10.1007\/978-0-387-30164-8_425"},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","volume":"85","author":"J Ke","year":"2017","unstructured":"Ke, J., Zheng, H., Yang, H., Chen, X.M.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C Emerg. Technol. 85, 591\u2013608 (2017). https:\/\/doi.org\/10.1016\/j.trc.2017.10.016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"34_CR12","unstructured":"Kodinariya, T., Makwana, P.: Review on determining of cluster in k-means clustering, vol. 1, pp. 90\u201395 (2013)"},{"key":"34_CR13","unstructured":"Koskela, T., Lehtokangas, M., Saarinen, J., Kaski, K.: Time series prediction with multilayer perception, fir and elman neural networks (1996)"},{"key":"34_CR14","doi-asserted-by":"publisher","unstructured":"Lei, Z., Qian, X., Ukkusuri, S.V.: Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks, vol. 117 (2020). https:\/\/doi.org\/10.1016\/j.trc.2020.102678","DOI":"10.1016\/j.trc.2020.102678"},{"key":"34_CR15","doi-asserted-by":"publisher","unstructured":"Li, X., Wang, C., Huang, X.: Reducing car-sharing relocation cost through non-parametric density estimation and stochastic programming. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ITSC45102.2020.9294599","DOI":"10.1109\/ITSC45102.2020.9294599"},{"key":"34_CR16","doi-asserted-by":"publisher","unstructured":"Liao, S., Zhou, L., Di, X., Yuan, B., Xiong, J.: Large-scale short-term urban taxi demand forecasting using deep learning. In: 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 428\u2013433 (2018). https:\/\/doi.org\/10.1109\/ASPDAC.2018.8297361","DOI":"10.1109\/ASPDAC.2018.8297361"},{"key":"34_CR17","doi-asserted-by":"publisher","unstructured":"Meek, C., Chickering, D., Heckerman, D.: Autoregressive tree models for time-series analysis, pp. 229\u2013244. https:\/\/doi.org\/10.1137\/1.9781611972726.14","DOI":"10.1137\/1.9781611972726.14"},{"key":"34_CR18","doi-asserted-by":"publisher","unstructured":"Mei, J., He, D., Harley, R., Habetler, T., Qu, G.: A random forest method for real-time price forecasting in new york electricity market, vol. 2014, pp. 1\u20135 (2014). https:\/\/doi.org\/10.1109\/PESGM.2014.6939932","DOI":"10.1109\/PESGM.2014.6939932"},{"key":"34_CR19","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","volume":"32","author":"JD Rodriguez","year":"2010","unstructured":"Rodriguez, J.D., Perez, A., Lozano, J.A.: Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 569\u2013575 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2009.187","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR20","unstructured":"Thakur, A., Krohn-Grimberghe, A.: Autocompete: a framework for machine learning competition (2015)"},{"key":"34_CR21","doi-asserted-by":"publisher","unstructured":"Vateekul, P., Sri-iesaranusorn, P., Aiemvaravutigul, P., Chanakitkarnchok, A., Rojviboonchai, K.: Recurrent neural-based vehicle demand forecasting and relocation optimization for car-sharing system: a real use case in Thailand, vol. 2021 (2021). https:\/\/doi.org\/10.1155\/2021\/8885671","DOI":"10.1155\/2021\/8885671"},{"key":"34_CR22","doi-asserted-by":"publisher","unstructured":"Wang, N., Guo, J., Liu, X., Fang, T.: A service demand forecasting model for one-way electric car-sharing systems combining long short-term memory networks with granger causality test, vol. 244 (2020). https:\/\/doi.org\/10.1016\/j.jclepro.2019.118812","DOI":"10.1016\/j.jclepro.2019.118812"},{"key":"34_CR23","doi-asserted-by":"publisher","unstructured":"Wang, N., Jia, S., Liu, Q.: A user-based relocation model for one-way electric carsharing system based on micro demand prediction and multi-objective optimization, vol. 296 (2021). https:\/\/doi.org\/10.1016\/j.jclepro.2021.126485","DOI":"10.1016\/j.jclepro.2021.126485"}],"container-title":["Lecture Notes in Computer Science","Computational Logistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87672-2_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T21:11:27Z","timestamp":1632258687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87672-2_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030876715","9783030876722"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87672-2_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Logistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccl22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccl2021.nl\/","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":"111","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":"42","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":"38% - 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":"2.5","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":"2","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)"}}]}}