{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:37:57Z","timestamp":1775662677533,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403376","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T19:04:00Z","timestamp":1597950240000},"page":"3243-3251","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["BusTr"],"prefix":"10.1145","author":[{"given":"Richard","family":"Barnes","sequence":"first","affiliation":[{"name":"University of California, Berkeley, Berkeley, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senaka","family":"Buthpitiya","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Cook","sequence":"additional","affiliation":[{"name":"Google Research, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex","family":"Fabrikant","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Tomkins","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangzhou","family":"Xu","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJATM.2014.065290"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.104.9.2763"},{"issue":"0","key":"e_1_3_2_1_3_1","first-page":"1","article-title":"Optimal orientations of discrete global grids and the poles of inaccessibility","volume":"0","author":"Barnes Richard","year":"2019","unstructured":"Richard Barnes. Optimal orientations of discrete global grids and the poles of inaccessibility. International Journal of Digital Earth, 0 (0): 1--14, 2019.","journal-title":"International Journal of Digital Earth"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2014.09.003"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2015.04.006"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/atr.5670410304"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/qje\/qjy007"},{"key":"e_1_3_2_1_8_1","volume-title":"Bus travel time prediction: A lognormal auto-regressive (AR) modeling approach. arXiv","author":"Dhivyabharathi B.","year":"1904","unstructured":"B. Dhivyabharathi, B. Anil Kumar, Avinash Achar, and Lelitha Vanajakshi. Bus travel time prediction: A lognormal auto-regressive (AR) modeling approach. arXiv: 1904.03444, 2019."},{"key":"e_1_3_2_1_9_1","volume-title":"Predicting bus delays with machine learning. Google AI Blog","author":"Fabrikant Alex","year":"2019","unstructured":"Alex Fabrikant. Predicting bus delays with machine learning. Google AI Blog, 2019. URL https:\/\/ai.googleblog.com\/2019\/06\/predicting-bus-delays-with-machine.html."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1753326.1753597"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_1_12_1","volume-title":"GTFS Realtime Specification. https:\/\/developers.google.com\/transit\/gtfsrealtime\/reference\/","author":"GTFS.","year":"2020","unstructured":"GTFS. GTFS Realtime Specification. https:\/\/developers.google.com\/transit\/gtfsrealtime\/reference\/, 2020."},{"key":"e_1_3_2_1_13_1","volume-title":"GTFS static overview. https:\/\/developers.google.com\/transit\/gtfs","author":"GTFS.","year":"2020","unstructured":"GTFS. GTFS static overview. https:\/\/developers.google.com\/transit\/gtfs, 2020b."},{"key":"e_1_3_2_1_14_1","volume-title":"A novel segment-based approach for improving classification performance of transport mode detection. Sensors, 18 (1)","author":"Guvensan M. Amac","year":"2018","unstructured":"M. Amac Guvensan, Burak Dusun, Baris Can, and H. Irem Turkmen. A novel segment-based approach for improving classification performance of transport mode detection. Sensors, 18 (1), 2018."},{"key":"e_1_3_2_1_15_1","first-page":"1 6517","volume-title":"Heghedus. PhD Forum: Forecasting Public Transit Using Neural Network Models. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP)","author":"Cristina","year":"2017","unstructured":"Cristina Heghedus. PhD Forum: Forecasting Public Transit Using Neural Network Models. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pages 1--2, Hong Kong, China, May 2017. IEEE. ISBN 978-1-5090-6517-2."},{"key":"e_1_3_2_1_16_1","first-page":"842 510","volume-title":"Chunming Rong. Neural Network Frameworks. Comparison on Public Transportation Prediction. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","author":"Heghedus Cristina","year":"2019","unstructured":"Cristina Heghedus, Antorweep Chakravorty, and Chunming Rong. Neural Network Frameworks. Comparison on Public Transportation Prediction. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 842--849, Rio de Janeiro, Brazil, May 2019. IEEE. ISBN 978-1-72813-510-6."},{"key":"e_1_3_2_1_17_1","volume-title":"Mitigation of Climate Change","author":"Climate IPCC.","year":"2014","unstructured":"IPCC. Climate Change 2014: Mitigation of Climate Change. Cambridge University Press, 2014. ISBN 978-1-107-05821-7."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2004.1399041"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.retrec.2016.07.019"},{"key":"e_1_3_2_1_20_1","volume-title":"Kingma and Jimmy Ba. Adam: A method for stochastic optimization","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1366-5545(00)00016-8"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2011.570109"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3141\/1884-04"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7620\/ab3ca7"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917514"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.regsciurbeco.2017.07.002"},{"key":"e_1_3_2_1_27_1","volume-title":"Survey of ETA prediction methods in public transport networks. arXiv","author":"Reich Thilo","year":"1904","unstructured":"Thilo Reich, Marcin Budka, Derek Robbins, and David Hulbert. Survey of ETA prediction methods in public transport networks. arXiv: 1904.05037, 2019."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1559\/152304003100011090"},{"key":"e_1_3_2_1_29_1","first-page":"93","article-title":"Bus speed estimation by neural networks to improve the automatic fleet management","volume":"37","author":"Salvo G.","year":"2007","unstructured":"G. Salvo, G. Amato, and Pietro Zito. Bus speed estimation by neural networks to improve the automatic fleet management. European Transport, 37: 93--104, 2007.","journal-title":"European Transport"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, December 9, 2017, Long Beach, USA, 2017. The workshop is BayesOpt 2017 NIPS Workshop on Bayesian Optimization","author":"Solnik Benjamin","year":"2017","unstructured":"Benjamin Solnik, Daniel Golovin, Greg Kochanski, John Elliot Karro, Subhodeep Moitra, and D. Sculley. Bayesian optimization for a better dessert. In Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, December 9, 2017, Long Beach, USA, 2017. The workshop is BayesOpt 2017 NIPS Workshop on Bayesian Optimization December 9, 2017, Long Beach, USA."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP.2016.7501714"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.9"},{"key":"e_1_3_2_1_33_1","unstructured":"Transit App. \"how we mapped the world's weirdest streets\" 2015. URL \"https:\/\/medium.com\/transit-app\/hello-nairobi-cc27bb5a73b7\"."},{"key":"e_1_3_2_1_34_1","volume-title":"Who's on board. Technical report","author":"Transit Center","year":"2016","unstructured":"Transit Center. Who's on board. Technical report, Transit Center, 2016. URL http:\/\/transitcenter.org\/wp-content\/uploads\/2016\/07\/Whos-On-Board-2016-7_12_2016.pdf."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317891"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5038\/2375-0901.9.3.12"},{"key":"e_1_3_2_1_37_1","volume-title":"Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors (Basel), 16 (1)","author":"Wan Jiafu","year":"2016","unstructured":"Jiafu Wan, Jianqi Liu, Zehui Shao, Athanasios V. Vasilakos, Muhammad Imran, and Keliang Zhou. Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors (Basel), 16 (1), 2016."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11877"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2011.06.010"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2017.04.012"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-1006-1"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.3141\/2082-13"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.3846\/16484142.2012.692710"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403376","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:47:28Z","timestamp":1755802048000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403376"}},"subtitle":["Predicting Bus Travel Times from Real-Time Traffic"],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":43,"alternative-id":["10.1145\/3394486.3403376","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403376","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}