In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.

Fiorini, S., Ciavotta, M., Maurino, A. (2022). Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem. ALGORITHMS, 15(10) [10.3390/a15100376].

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

Fiorini S.
;
Ciavotta M.;Maurino A.
2022

Abstract

In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.
Articolo in rivista - Articolo scientifico
attention mechanism; autoencoder architecture; spatial correlation; temporal correlation; time-distributed convolutions; vehicle flow prediction;
English
14-ott-2022
2022
15
10
376
none
Fiorini, S., Ciavotta, M., Maurino, A. (2022). Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem. ALGORITHMS, 15(10) [10.3390/a15100376].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/395678
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