{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T13:44:09Z","timestamp":1775051049216,"version":"3.50.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1109\/tits.2023.3291737","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T17:53:10Z","timestamp":1689616390000},"page":"12821-12830","source":"Crossref","is-referenced-by-count":8,"title":["Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 Cities"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2076-8714","authenticated-orcid":false,"given":"Moritz","family":"Neun","sequence":"first","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4460-5194","authenticated-orcid":false,"given":"Christian","family":"Eichenberger","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3866-821X","authenticated-orcid":false,"given":"Yanan","family":"Xin","sequence":"additional","affiliation":[{"name":"Institute of Cartography and Geoinformation, ETH Z&#x00FC;rich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1562-7941","authenticated-orcid":false,"given":"Cheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Zurich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8160-7634","authenticated-orcid":false,"given":"Nina","family":"Wiedemann","sequence":"additional","affiliation":[{"name":"Institute of Cartography and Geoinformation, ETH Z&#x00FC;rich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0456-8539","authenticated-orcid":false,"given":"Henry","family":"Martin","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5736-4679","authenticated-orcid":false,"given":"Martin","family":"Tomko","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8835-0950","authenticated-orcid":false,"given":"Lukas","family":"Amb\u00fchl","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geomatic Engineering, Traffic Engineering Group, ETH Z&#x00FC;rich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7568-7981","authenticated-orcid":false,"given":"Luca","family":"Hermes","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Bielefeld University, Bielefeld, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1385-1109","authenticated-orcid":false,"given":"Michael","family":"Kopp","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/10630732.2017.1348880"},{"key":"ref2","volume-title":"Urban Spatial Traffic Patterns","author":"Vaughan","year":"1987"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/0041-1647(75)90063-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1287\/opre.7.1.79"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9399.001.0001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmse.2021.03.003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73280-6_6"},{"key":"ref8","article-title":"Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction","author":"Cui","year":"2018","journal-title":"arXiv:1801.02143"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-51539-5"},{"key":"ref10","first-page":"1","article-title":"Streets: A novel camera network dataset for traffic flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Snyder"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3009977.3010040"},{"key":"ref12","article-title":"New York city taxi trip data (2010\u20132013)","author":"Donovan","year":"2016"},{"key":"ref13","article-title":"VLUC: An empirical benchmark for video-like urban computing on citywide crowd and traffic prediction","author":"Jiang","year":"2019","journal-title":"arXiv:1911.06982"},{"key":"ref14","volume-title":"Uber Movement: Speeds Calculation Methodology","year":"2022"},{"key":"ref15","volume-title":"Faqs\u2014Uber Movement: Let\u2019s Find Smarter Ways Forward, Together","year":"2022"},{"key":"ref16","first-page":"4865","article-title":"Probe vehicle data: Data efficiency and privacy interest","volume-title":"Proc. 12th World Congr. Intell. Transp. SystemsITS AmericaITS JapanERTICO","author":"Boeckelt"},{"key":"ref17","volume-title":"Didi Chuxing Gaia Initiative","year":"2022"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219895"},{"key":"ref19","volume-title":"Sample Data|Here Developer","year":"2020"},{"key":"ref20","volume-title":"Caltrans Performance Measurement System (PEMS)","year":"2022"},{"key":"ref21","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.023"},{"key":"ref23","volume-title":"Portal","year":"2022"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103065"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3142255"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557151"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2038916.2038944"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020462"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869807"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2521482"},{"key":"ref31","first-page":"232","article-title":"The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task\u2014Insights from the IARAI Traffic4cast competition at NeurIPS 2019","volume-title":"Proc. NeurIPS Competition Demonstration Track","volume":"123","author":"Kreil"},{"key":"ref32","first-page":"325","article-title":"Traffic4cast at NeurIPS 2020\u2014Yet more on the unreasonable effectiveness of gridded geo-spatial processes","volume-title":"Proc. NeurIPS Competition Demonstration Track","volume":"133","author":"Kopp"},{"key":"ref33","first-page":"97","article-title":"Traffic4cast at NeurIPS 2021\u2014Temporal and spatial few-shot transfer learning in gridded geo-spatial processes","volume-title":"Proc. NeurIPS Competitions Demonstrations Track","volume":"176","author":"Eichenberger"},{"key":"ref34","volume-title":"Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 Cities\u2014Supplementary Material","author":"Neun","year":"2023"},{"key":"ref35","volume-title":"About\u2014Openstreetmap","year":"2022"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compenvurbsys.2017.05.004","article-title":"OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks","volume":"65","author":"Boeing","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref37","article-title":"Traffic4cast at NeurIPS 2022\u2014Predict dynamics along graph edges from sparse node data: Whole city traffic and ETA from stationary vehicle detectors","author":"Neun","year":"2023","journal-title":"arXiv:2303.07758"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/2743025"},{"key":"ref39","volume-title":"Mapping Traffic Congestion\u2014HERE Developer","author":"Parafina","year":"2022"},{"key":"ref40","volume-title":"Uber Movement","year":"2022"},{"key":"ref41","volume-title":"Here Launches Advanced Real-Time Traffic Service","year":"2021"},{"key":"ref42","volume-title":"Real Time Traffic\u2014Developer Guide\u2014Here Traffic API\u2014Here Developer","year":"2022"},{"key":"ref43","volume-title":"Terms and Conditions|Here Developer","year":"2020"},{"key":"ref44","volume-title":"FHWA: Traffic Analysis Tools","year":"2023"},{"issue":"1","key":"ref45","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3390\/safety7010003","article-title":"Observations on the relationship between crash frequency and traffic flow","volume":"7","author":"Wagner","year":"2021","journal-title":"Safety"},{"key":"ref46","volume-title":"Validation of Google Floating Car Data for Applications in Traffic Management","author":"van den Haak","year":"2016"},{"key":"ref47","volume-title":"Verkehrsdetektion Berlin","year":"2022"},{"key":"ref48","volume-title":"TFL Open Data, TIMS","year":"2022"},{"key":"ref49","volume-title":"Webtris","year":"2022"},{"key":"ref50","volume-title":"Madrid Open Data, Historical Traffic Data","year":"2022"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1080\/03081060.2022.2150858"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/10304348\/10184958.pdf?arnumber=10184958","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T14:39:22Z","timestamp":1709303962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10184958\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":51,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2023.3291737","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11]]}}}