Add to ORKGIntelligent Transportation Systems (ITS) are crucial for managing traffic, but accurate prediction is challenging. Deep learning, specifically Graph Convolutional Neural Networks (GCNs)[1], shows promise in handling complex traffic data. This project focus studies recent developments in traffic prediction using GCNs and proposes a novel GCN-based method with attention mechanisms and Kalman Filter. Experimental results demonstrate a 5% accuracy improvement compared to the original model
Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling a...
Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, ...
Traffic speed prediction is known as an important but challenging problem. In this paper, we propose...
Intelligent Transportation Systems (ITS) are becoming increasingly important as traditional traffi...
In this research, traffic data is formatted as a graph network problem and graph neural networks are...
Accurately predicting network-level traffic conditions has been identified as a critical need for sm...
This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic ...
Traffic speed prediction plays an important role in intelligent transportation systems, and many app...
City-scale traffic prediction is an important task for public safety, traffic management, and deploy...
Abstract Short‐term traffic flow prediction plays a crucial role in research and application of inte...
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions ...
Traffic Flow prediction is a very important part of managing traffic flows on the road network. It p...
Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator t...
Timely forecast of traffic is very much needed for smart cities, which allows travelers and governme...
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart citie...
Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling a...
Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, ...
Traffic speed prediction is known as an important but challenging problem. In this paper, we propose...
Intelligent Transportation Systems (ITS) are becoming increasingly important as traditional traffi...
In this research, traffic data is formatted as a graph network problem and graph neural networks are...
Accurately predicting network-level traffic conditions has been identified as a critical need for sm...
This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic ...
Traffic speed prediction plays an important role in intelligent transportation systems, and many app...
City-scale traffic prediction is an important task for public safety, traffic management, and deploy...
Abstract Short‐term traffic flow prediction plays a crucial role in research and application of inte...
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions ...
Traffic Flow prediction is a very important part of managing traffic flows on the road network. It p...
Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator t...
Timely forecast of traffic is very much needed for smart cities, which allows travelers and governme...
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart citie...
Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling a...
Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, ...
Traffic speed prediction is known as an important but challenging problem. In this paper, we propose...