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
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
panzheyi/ST-MetaNet
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KDD '19 2019
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e. g., points of interests and road networks.
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
tumeteor/neurips2019challenge
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This extended abstract describes our solution for the Traffic4Cast Challenge 2019.
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting.
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
pbecker93/ExpectedInformationMaximization
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ICLR 2020
Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes.
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly.
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Practicing-Federated-Learning-for-IoT/FedGRU
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Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90. 96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction
Spatio-temporal forecasting is an open research field whose interest is growing exponentially.
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Cameras
The lack of an open-source traffic estimation method using open data platforms is a bottleneck for building sophisticated navigation services on top of OpenStreetMap (OSM).
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
uctb/uctb
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The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.
Multi-agent Trajectory Prediction with Fuzzy Query Attention
nitinkamra1992/FQA
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NeurIPS 2020
Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning.
