{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/artificial-neural-networks-applied-to-taxi","title":"Artificial Neural Networks Applied to Taxi Destination Prediction","arxiv_id":"1508.00021","date":"2015-07-31","proceeding":null,"authors":["Alexandre de Brébisson","Étienne Simon","Alex Auvolat","Pascal Vincent","Yoshua Bengio"],"abstract":"We describe our first-place solution to the ECML/PKDD discovery challenge on\ntaxi destination prediction. The task consisted in predicting the destination\nof a taxi based on the beginning of its trajectory, represented as a\nvariable-length sequence of GPS points, and diverse associated\nmeta-information, such as the departure time, the driver id and client\ninformation. Contrary to most published competitor approaches, we used an\nalmost fully automated approach based on neural networks and we ranked first\nout of 381 teams. The architectures we tried use multi-layer perceptrons,\nbidirectional recurrent neural networks and models inspired from recently\nintroduced memory networks. Our approach could easily be adapted to other\napplications in which the goal is to predict a fixed-length output from a\nvariable-length sequence.","url_abs":"http://arxiv.org/abs/1508.00021v2","url_pdf":"http://arxiv.org/pdf/1508.00021v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"artificial-neural-networks-applied-to-taxi","repo_url":"https://github.com/adbrebs/taxi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1508.00021","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}