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Title	Traffic Flow Based Feature Engineering For Urban Management System
ID_Doc	58564
Authors	Mu H.; Aljeri N.; Boukerche A.
Year	2023
Published	Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
DOI	http://dx.doi.org/10.1109/NOMS56928.2023.10154284
Abstract	Accurate traffic flow forecasting is crucial for smart-cities and traffic management in modern society. With the rapid increase in traffic information's nonlinearity and complexity, Neural Networks-based models have been introduced to traffic flow forecasting for spatial and temporal dependencies extraction. In this paper, we present the feature engineering model of spatial, temporal, and spatial-temporal dependency in the traffic flow prediction problem. We consider the solution with Graph Convolutional Neural Network (GCN) for the spatial dependency modeling, Gated Recurrent Unit(GRU) for the temporal feature construction, and the sequential feature extraction for Spatial-temporal dependency. To explain the effectiveness of the proposed idea, we evaluate the models using real-world datasets. Experiments show that the models capture comprehensive Spatio-temporal correlations with sequential feature extraction outperforming the sole spatial and temporal models. © 2023 IEEE.
Author Keywords	Deep Learning; feature engineering; graph convolutional network; spatiotemporal dependency; traffic flow forecasting

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