{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:01:47Z","timestamp":1770753707516,"version":"3.50.0"},"publisher-location":"New York, New York, USA","reference-count":14,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1145\/3041021.3051699","type":"proceedings-article","created":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T18:39:25Z","timestamp":1515695965000},"page":"1469-1474","source":"Crossref","is-referenced-by-count":18,"title":["Predicting Train Occupancies based on Query Logs and External Data Sources"],"prefix":"10.1145","author":[{"given":"Gilles","family":"Vandewiele","sequence":"first","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pieter","family":"Colpaert","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Janssens","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joachim","family":"Van Herwegen","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruben","family":"Verborgh","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"Mannens","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Femke","family":"Ongenae","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Filip","family":"De Turck","sequence":"additional","affiliation":[{"name":"Universiteit Gent, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3041021.3051699-1","doi-asserted-by":"crossref","unstructured":"M. Cantwell, B. Caulfield, and M. O'Mahony. Examining the factors that impact public transport commuting satisfaction. Journal of Public Transportation, 12(2):1, 2009.","DOI":"10.5038\/2375-0901.12.2.1"},{"key":"key-10.1145\/3041021.3051699-2","unstructured":"N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321--357, 2002."},{"key":"key-10.1145\/3041021.3051699-3","doi-asserted-by":"crossref","unstructured":"T. Chen and C. Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785--794. ACM, 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"key-10.1145\/3041021.3051699-4","doi-asserted-by":"crossref","unstructured":"P. Colpaert, A. Chua, R. Verborgh, E. Mannens, R. Van de Walle, and A. Vande Moere. What public transit api logs tell us about travel flows. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 873--878. International World Wide Web Conferences Steering Committee, 2016.","DOI":"10.1145\/2872518.2891069"},{"key":"key-10.1145\/3041021.3051699-5","doi-asserted-by":"crossref","unstructured":"Y. Kim and J. Kim. Gradient lasso for feature selection. In Proceedings of the twenty-first international conference on Machine learning, page 60. ACM, 2004.","DOI":"10.1145\/1015330.1015364"},{"key":"key-10.1145\/3041021.3051699-6","doi-asserted-by":"crossref","unstructured":"M. B. Kursa, A. Jankowski, and W. R. Rudnicki. Boruta--a system for feature selection. Fundamenta Informaticae, 101(4):271--285, 2010.","DOI":"10.3233\/FI-2010-288"},{"key":"key-10.1145\/3041021.3051699-7","doi-asserted-by":"crossref","unstructured":"U. Lundberg. Urban commuting: Crowdedness and catecholamine excretion. Journal of Human Stress, 2(3):26--32, 1976.","DOI":"10.1080\/0097840X.1976.9936067"},{"key":"key-10.1145\/3041021.3051699-8","unstructured":"R. Martinez-Cantin. Bayesopt: a bayesian optimization library for nonlinear optimization, experimental design and bandits. Journal of Machine Learning Research, 15(1):3735--3739, 2014."},{"key":"key-10.1145\/3041021.3051699-9","doi-asserted-by":"crossref","unstructured":"M. Milkovits. Modeling the factors affecting bus stop dwell time: use of automatic passenger counting, automatic fare counting, and automatic vehicle location data. Transportation Research Record: Journal of the Transportation Research Board, (2072):125--130, 2008.","DOI":"10.3141\/2072-13"},{"key":"key-10.1145\/3041021.3051699-10","doi-asserted-by":"crossref","unstructured":"A. Nuzzolo, U. Crisalli, L. Rosati, and A. Ibeas. Stop: a short term transit occupancy prediction tool for aptis and real time transit management systems. In Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on, pages 1894--1899. IEEE, 2013.","DOI":"10.1109\/ITSC.2013.6728505"},{"key":"key-10.1145\/3041021.3051699-11","unstructured":"A. Puong. Dwell time model and analysis for the mbta red line. Massachusetts Institute of Technology Research Memo, 2000."},{"key":"key-10.1145\/3041021.3051699-12","doi-asserted-by":"crossref","unstructured":"R. Silva, S. M. Kang, and E. M. Airoldi. Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proceedings of the National Academy of Sciences, 112(18):5643--5648, 2015.","DOI":"10.1073\/pnas.1412908112"},{"key":"key-10.1145\/3041021.3051699-13","doi-asserted-by":"crossref","unstructured":"A. Tirachini, D. A. Hensher, and J. M. Rose. Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transportation research part A: policy and practice, 53:36--52, 2013.","DOI":"10.1016\/j.tra.2013.06.005"},{"key":"key-10.1145\/3041021.3051699-14","doi-asserted-by":"crossref","unstructured":"N. Zhang, H. Chen, X. Chen, and J. Chen. Forecasting public transit use by crowdsensing and semantic trajectory mining: Case studies. ISPRS International Journal of Geo-Information, 5(10):180, 2016.","DOI":"10.3390\/ijgi5100180"}],"event":{"name":"the 26th International Conference","location":"Perth, Australia","acronym":"WWW '17 Companion","number":"26","sponsor":["SIGWEB, ACM Special Interest Group on Hypertext, Hypermedia, and Web","IW3C2, International World Wide Web Conference Committee"],"start":{"date-parts":[[2017,4,3]]},"end":{"date-parts":[[2017,4,7]]}},"container-title":["Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3041021.3051699","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3051699&ftid=1865273&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:04:58Z","timestamp":1750273498000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3041021.3051699"}},"subtitle":[],"proceedings-subject":"World Wide Web Companion","short-title":[],"issued":{"date-parts":[[2017]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1145\/3041021.3051699","relation":{},"subject":[],"published":{"date-parts":[[2017]]}}}