An Explainable Spatio-Temporal Framework for Traffic Forecasting Using Graph-Based Features

Urban traffic prediction plays a critical role in improving transportation efficiency and supporting sustainability in smart cities. This study proposes an explainable spatio-temporal machine learning framework that integrates periodic temporal features with graph-based spatial representations. Temp...

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Hlavní autor:	Deniz Balta
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2026-01-01
Edice:	IEEE Access
Témata:	Traffic forecasting spatio-temporal modeling graph-based analysis explainable AI feature importance intelligent transportation systems
On-line přístup:	https://ieeexplore.ieee.org/document/11601043/
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