This study presents a model for predicting traffic flow founded on heterogeneous data fusion. For the primary traffic-related data, a blend of graph ...
Download Citation | On May 10, 2024, Gang Yuan and others published Research on Urban Traffic Flow Prediction Model Utilizing Heterogeneous Data Fusion ...
Jul 1, 2023 · Deep learning methods were used to uniformly model heterogeneous data and predict traffic flow in the future.Experiments were conducted on three ...
This research work aims at developing a data fusion-based traffic congestion control system in smart cities using a deep learning model.
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In [28], the authors introduce a Convolutional Neural Net- work (CNN) model to predict traffic accidents using a state matrix with influencing traffic features.
We propose a self-supervised learning-based traffic prediction framework called Traffic Prediction with Self-Supervised Learning (TPSSL) to address this issue.
In this review study, (i) the smart techniques used for the analysis of mobility data in the prediction of traffic flow in urban areas are grouped.
machine-based approach in data fusion and smart city traffic prediction. In the academic aspect, the data fusion method of the collector has been widely ...
Jul 23, 2024 · Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flow dataset to deliver better performance traffic ...
Aug 18, 2024 · Using a diversity of big data and deep learning techniques to predict citywide crowd/traffic flows throughout a city.
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