Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks
This addresses the issue of limited transit information availability for public transport users, potentially improving service quality, but it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of unavailable transit data by reconstructing complete transit graphs from GPS traces using data mining and machine learning, validated on a real bus system dataset from L'Aquila, Italy, with results showing effectiveness and efficiency.
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.