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This paper proposes a Joint Traffic Flow Estimation and Prediction (JT-FEP) approach, which considers the missing data as additional unknown network parameters.
Abstract—Classical methods of traffic flow prediction with missing data are generally implemented in two sequential stages:.
Estimating Accidents in a Road Network ... This paper reviews model relating accidents to traffic flows, with particular emphasis on the appropriateness of the ...
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In this paper, a novel approach for modeling traffic flow in urban networks that is especially suitable for state estimation is proposed.
7 days ago · The model transform the traditional single-intersection prediction into a road network prediction. Subsequently, the accuracy of BF-SAGE-GRU is ...
In this paper, we review the studies that focus on the missing traffic state estimation problem, especially for the traffic state estimation on the segments ...
Jan 25, 2022 · A novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks ( ...
May 18, 2024 · In this paper, we proposed a novel model named 3D spatial–temporal-based adaptive modeling graph convolutional network (3D(STAMGCN)) that ...
This paper addresses an issue of short-term traffic flow prediction in urban traffic networks with traffic signals in intersections.
The Intelligent transport system is helpful in estimating the road network capacity, alleviating the traffic congestion and guiding the traffic participants, ...