Year: 2025
Type: dissertation
Abstract: Bus travel time forecasting is a critical challenge in analysing and optimizing Public Transportation Networks. In this thesis, we propose a Spatio-Temporal Neural Network that captures both spatial and temporal dependencies by combining Graph Neural Networks (GNNs) with Recurrent Neural Networks (RNNs). The model is applied to a large and heterogeneous set of real-world bus routes from Belo Horizonte, Brazil, obtained from the integration of scheduled and real-time GTFS data. The proposed model significantly outperforms standard baselines, especially in detecting irregular and less frequent temporal patterns.
Author: GRAMAGLIA, GERLANDO
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7052697814
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