{"month":"11","publisher":"IEEE","year":"2019","type":"conference","publication_identifier":{"isbn":["9781538670248"]},"day":"28","article_processing_charge":"No","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"None","language":[{"iso":"eng"}],"date_created":"2019-12-29T23:00:47Z","date_published":"2019-11-28T00:00:00Z","publication_status":"published","title":"LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale","status":"public","external_id":{"isi":["000521238102050"]},"author":[{"first_name":"Georg F","id":"464B40D6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8882-5116","full_name":"Osang, Georg F","last_name":"Osang"},{"first_name":"James","full_name":"Cook, James","last_name":"Cook"},{"first_name":"Alex","full_name":"Fabrikant, Alex","last_name":"Fabrikant"},{"full_name":"Gruteser, Marco","last_name":"Gruteser","first_name":"Marco"}],"citation":{"short":"G.F. Osang, J. Cook, A. Fabrikant, M. Gruteser, in:, 2019 IEEE Intelligent Transportation Systems Conference, IEEE, 2019.","ama":"Osang GF, Cook J, Fabrikant A, Gruteser M. LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. In: <i>2019 IEEE Intelligent Transportation Systems Conference</i>. IEEE; 2019. doi:<a href=\"https://doi.org/10.1109/ITSC.2019.8917514\">10.1109/ITSC.2019.8917514</a>","ista":"Osang GF, Cook J, Fabrikant A, Gruteser M. 2019. LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. 2019 IEEE Intelligent Transportation Systems Conference. ITSC: Intelligent Transportation Systems Conference, 8917514.","ieee":"G. F. Osang, J. Cook, A. Fabrikant, and M. Gruteser, “LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale,” in <i>2019 IEEE Intelligent Transportation Systems Conference</i>, Auckland, New Zealand, 2019.","apa":"Osang, G. F., Cook, J., Fabrikant, A., &#38; Gruteser, M. (2019). LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. In <i>2019 IEEE Intelligent Transportation Systems Conference</i>. Auckland, New Zealand: IEEE. <a href=\"https://doi.org/10.1109/ITSC.2019.8917514\">https://doi.org/10.1109/ITSC.2019.8917514</a>","mla":"Osang, Georg F., et al. “LiveTraVeL: Real-Time Matching of Transit Vehicle Trajectories to Transit Routes at Scale.” <i>2019 IEEE Intelligent Transportation Systems Conference</i>, 8917514, IEEE, 2019, doi:<a href=\"https://doi.org/10.1109/ITSC.2019.8917514\">10.1109/ITSC.2019.8917514</a>.","chicago":"Osang, Georg F, James Cook, Alex Fabrikant, and Marco Gruteser. “LiveTraVeL: Real-Time Matching of Transit Vehicle Trajectories to Transit Routes at Scale.” In <i>2019 IEEE Intelligent Transportation Systems Conference</i>. IEEE, 2019. <a href=\"https://doi.org/10.1109/ITSC.2019.8917514\">https://doi.org/10.1109/ITSC.2019.8917514</a>."},"isi":1,"doi":"10.1109/ITSC.2019.8917514","department":[{"_id":"HeEd"}],"conference":{"end_date":"2019-10-30","name":"ITSC: Intelligent Transportation Systems Conference","location":"Auckland, New Zealand","start_date":"2019-10-27"},"article_number":"8917514","quality_controlled":"1","scopus_import":"1","date_updated":"2023-09-06T14:50:28Z","_id":"7216","publication":"2019 IEEE Intelligent Transportation Systems Conference","abstract":[{"lang":"eng","text":"We present LiveTraVeL (Live Transit Vehicle Labeling), a real-time system to label a stream of noisy observations of transit vehicle trajectories with the transit routes they are serving (e.g., northbound bus #5). In order to scale efficiently to large transit networks, our system first retrieves a small set of candidate routes from a geometrically indexed data structure, then applies a fine-grained scoring step to choose the best match. Given that real-time data remains unavailable for the majority of the world’s transit agencies, these inferences can help feed a real-time map of a transit system’s trips, infer transit trip delays in real time, or measure and correct noisy transit tracking data. This system can run on vehicle observations from a variety of sources that don’t attach route information to vehicle observations, such as public imagery streams or user-contributed transit vehicle sightings.We abstract away the specifics of the sensing system and demonstrate the effectiveness of our system on a \"semisynthetic\" dataset of all New York City buses, where we simulate sensed trajectories by starting with fully labeled vehicle trajectories reported via the GTFS-Realtime protocol, removing the transit route IDs, and perturbing locations with synthetic noise. Using just the geometric shapes of the trajectories, we demonstrate that our system converges on the correct route ID within a few minutes, even after a vehicle switches from serving one trip to the next."}]}