Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction
Traffic flow prediction based on vehicle trajectories collected from the installed GPS devices
is critically important to Intelligent Transportation Systems (ITS). One limitation of existing …
is critically important to Intelligent Transportation Systems (ITS). One limitation of existing …
Traffic flow prediction with vehicle trajectories
This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph
Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle …
Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
Synchronous spatiotemporal graph transformer: A new framework for traffic data prediction
Modeling the spatiotemporal relationship (STR) of traffic data is important yet challenging for
existing graph networks. These methods usually capture features separately in temporal and …
existing graph networks. These methods usually capture features separately in temporal and …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
Smarter traffic prediction using big data, in-memory computing, deep learning and GPUs
Road transportation is the backbone of modern economies, albeit it annually costs 1.25
million deaths and trillions of dollars to the global economy, and damages public health and …
million deaths and trillions of dollars to the global economy, and damages public health and …
Spatiotemporal attention-based graph convolution network for segment-level traffic prediction
Traffic prediction, as a core component of intelligent transportation systems (ITS), has been
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Temporal multi-graph convolutional network for traffic flow prediction
M Lv, Z Hong, L Chen, T Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …