Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks

التفاصيل البيبلوغرافية
العنوان:	Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks
المؤلفون:	Iovino, Ludovico, Nguyen, Phuong T., Di Salle, Amleto, Gallo, Francesco, Flammini, Michele
سنة النشر:	2021
المجموعة:	ArXiv.org (Cornell University Library)
مصطلحات موضوعية:	Computer Science - Machine Learning, Computer Science - Artificial Intelligence
الوصف:	Studies on public transportation in Europe suggest that European inhabitants use buses in ca. 56% of all public transport travels. One of the critical factors affecting such a percentage and more, in general, the demand for public transport services, with an increasing reluctance to use them, is their quality. End-users can perceive quality from various perspectives, including the availability of information, i.e., the access to details about the transit and the provided services. The approach proposed in this paper, using innovative methodologies resorting on data mining and machine learning techniques, aims to make available the unavailable data about public transport. In particular, by mining GPS traces, we manage to reconstruct the complete transit graph of public transport. The approach has been successfully validated on a real dataset collected from the local bus system of the city of L'Aquila (Italy). The experimental results demonstrate that the proposed approach and implemented framework are both effective and efficient, thus being ready for deployment. ; Comment: 11 pages, 8 figures, accepted for publication by IEEE Transactions on Intelligent Transportation Systems (T-ITS)
نوع الوثيقة:	text
اللغة:	unknown
العلاقة:	http://arxiv.org/abs/2102.10323Test
DOI:	10.1109/TITS.2021.3053373
الإتاحة:	https://doi.org/10.1109/TITS.2021.3053373Test http://arxiv.org/abs/2102.10323Test
رقم الانضمام:	edsbas.C3B9AC41
قاعدة البيانات:	BASE

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الوصف
DOI:	10.1109/TITS.2021.3053373