Traffic Prediction
114 papers with code • 32 benchmarks • 18 datasets
Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.
( Image credit: BaiduTraffic )
Benchmarks
These leaderboards are used to track progress in Traffic Prediction
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Libraries
Use these libraries to find Traffic Prediction models and implementations
PeMSD8
METR-LA
PeMSD7
PeMSD4
Q-Traffic
Most implemented papers
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
deepkashiwa20/dl-traff-graph
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Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
guoshnbjtu/astgcn-r-pytorch
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IJCAI-19 2019
The output of the three components are weighted fused to generate the final prediction results.
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Davidham3/STSGCN
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Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
nnzhan/MTGNN
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Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method.
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals.
A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr).
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
zezhishao/step
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However, the patterns of time series and the dependencies between them (i. e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data.
