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Traffic Prediction

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하위 태스크: 1
평가표: 33
보존된 평가 기록: 192

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.

<span style="color:grey; opacity: 0.6">( Image credit: BaiduTraffic )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/bojesomo/Traffic4Cast2020-DeepSedanionNetwork

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Traffic flow prediction using Deep Sedenion Networks

https://github.com/bojesomo/Traffic4Cast2021-SwinUNet3D

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SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers

https://github.com/cchao0116/EasyDGL

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EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning

https://github.com/chenyuqi990215/rntrajrec

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

https://github.com/chyazhang/bjtt

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BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

https://github.com/cy07gn/teltomob

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Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach

https://github.com/dandelionym/cdvgm

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Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction

https://github.com/david-tedjopurnomo/trafformer

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TrafFormer: A Transformer Model for Predicting Long-term Traffic

https://github.com/deepkashiwa20/Urban_Concept_Drift

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MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

https://github.com/deepkashiwa20/dl-traff-graph

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DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

https://github.com/deepkashiwa20/dl-traff-grid

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DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

https://github.com/deepkashiwa20/megacrn

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Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

https://github.com/drownfish19/corrstn

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A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting

https://github.com/echo-ji/st-ssl

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Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

https://github.com/echo-ji/stden

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

https://github.com/emory-melody/awesome-graph-ndes

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Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks

https://github.com/engyasin/GAM4Traffic

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Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines

https://github.com/eternityzy/cchmm

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Causal conditional hidden Markov model for multimodal traffic prediction

https://github.com/eternityzy/stnscm

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Spatio-temporal neural structural causal models for bike flow prediction

https://github.com/grand-lab/mtgode

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Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

https://github.com/guoxs/xtp-llm

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Towards Explainable Traffic Flow Prediction with Large Language Models

https://github.com/hanglearning/NeighborFL

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Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation

https://github.com/haounsw/pisa

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PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

https://github.com/himanshudce/owam

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Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes

https://github.com/hitplz/dstrformer

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Dynamic Trend Fusion Module for Traffic Flow Prediction

https://github.com/hkuds/flashst

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FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction

https://github.com/hkuds/opencity

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OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction

https://github.com/huaxiuyao/DMVST-Net

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Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

https://github.com/hyunwookl/pm-memnet

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Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting

https://github.com/iarai/neurips2022-traffic4cast

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Multi-task Learning for Sparse Traffic Forecasting

https://github.com/icitylab/traffnet

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TraffNet: Learning Causality of Traffic Generation for What-if Prediction

https://github.com/inon-peled/qtip_code_pub

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QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters

https://github.com/jeongwhanchoi/STG-NCDE

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Graph Neural Controlled Differential Equations for Traffic Forecasting

https://github.com/jeongwhanchoi/stg-nrde

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Graph Neural Rough Differential Equations for Traffic Forecasting

https://github.com/jiangnanx129/FairTP

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FairTP: A Prolonged Fairness Framework for Traffic Prediction

https://github.com/jillbetty001/ST-GDN

공식 코드

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

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