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

TL;DR

This paper presents a machine learning approach to reconstruct complete transit graphs from GPS data, enhancing public transport information availability and quality, validated on a real Italian bus dataset.

Contribution

It introduces an innovative data mining methodology using recurrent neural networks to recover unavailable transit data from GPS traces.

Findings

Effective reconstruction of transit graphs from GPS data

Validated approach on real-world bus dataset

Framework ready for deployment in public transport systems

Abstract

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.