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.

中文翻译：

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不可用的公交提要规范：使其可与递归神经网络一起使用

欧洲的公共交通研究表明，欧洲居民在约公元前使用公共汽车。所有公共交通旅行的56％。影响这一比例的关键因素之一，其质量通常是对公共交通服务的需求（通常是越来越不愿意使用它们）的一个因素。最终用户可以从各种角度感知质量，包括信息的可用性，即对有关过境和所提供服务的详细信息的访问。本文提出的方法，采用了依靠数据挖掘和机器学习技术的创新方法，旨在提供有关公共交通的不可用数据。特别是，通过挖掘GPS轨迹，我们设法重建了完整的公共交通运输图。该方法已在从拉奎拉市（意大利）的本地公交系统收集的真实数据集中成功验证。实验结果表明，所提出的方法和已实现的框架既有效又高效，因此可以进行部署。