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
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
haounsw/pisa
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In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes.
Multi-task Learning for Sparse Traffic Forecasting
iarai/neurips2022-traffic4cast
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For this reason, we propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment.
Graph Neural Rough Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).
Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data
Lemma1/DPFE
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A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem.
Structural Recurrent Neural Network for Traffic Speed Prediction
rhymesg/SRNN
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We use a graph of a vehicular road network with recurrent neural networks (RNNs) to infer the interaction between adjacent road segments as well as the temporal dynamics.
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
LeiBAI/STG2Seq
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Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
