Alternatives and similar repositories for DeepCrowd:

Users that are interested in DeepCrowd are comparing it to the libraries listed below

• Essaim / CGCDemandPrediction
  ☆49Updated 2 years ago
• goaheand / AdapGL-pytorch
  ☆64Updated 3 years ago
• deepkashiwa20 / ODCRN
  [ECMLPKDD21] Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19
  ☆31Updated last year
• KL4805 / CrossTReS
  Repo for CrossTReS: Cross-city Transfer Learning for Traffic Prediction via Source Region Selection
  ☆17Updated 2 years ago
• deepkashiwa20 / MegaCRN
  [AAAI23] This it the official github for AAAI23 paper "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting"
  ☆112Updated last year
• skzhangPKU / GTA
  Multi-sensor traffic flow forecasting.
  ☆13Updated 2 years ago
• j1o2h3n / DDSTGCN
  Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction
  ☆43Updated last year
• trainingl / STG4Traffic
  Code of “STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction”.
  ☆66Updated 8 months ago
• deepkashiwa20 / DL-Traff-Grid
  [CIKM 2021 Resource Paper] DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (Grid Part)
  ☆54Updated 2 years ago
• wumingyao / MADGCN
  ☆10Updated 3 years ago
• JinleiZhangBJTU / Conv-GCN
  Code for Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
  ☆30Updated 4 years ago
• MiaoHaoSunny / ST-DAAN
  Code for paper "Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks"
  ☆13Updated 3 years ago
• underdoc-wang / EAST-Net
  [AAAI'22] Event-Aware Multimodal Mobility Nowcasting
  ☆14Updated 2 years ago
• R0oup1iao / Traffic-Transformer
  This is a Pytorch implementation of Traffic Transformer. Now the corresponding paper is available online at
  ☆25Updated 2 years ago
• ZhuangDingyi / STZINB
  Source code of implementing spatial-temporal zero-inflated negative binomial network for trip demand prediction
  ☆25Updated 3 years ago
• Echohhhhhh / GSNet
  AAAI 2021. GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting
  ☆32Updated last year
• LMissher / STWave
  [ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
  ☆84Updated 2 weeks ago
• mingqian000 / TrGNN
  Traffic Flow Prediction with Vehicle Trajectories
  ☆68Updated 4 years ago
• underdoc-wang / ST-MGCN
  [AAAI'19] Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (Pytorch Replication)
  ☆90Updated 3 years ago
• Echo-Ji / STDEN
  Pytorch implementation of Spatoi-temporal Differential Equation Network (STDEN).
  ☆38Updated 2 years ago
• NickHan-cs / Spatio-Temporal-Data-Mining-Survey
  Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.
  ☆120Updated 8 months ago
• panzheyi / MF-STN
  The codes and data of paper "Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction"
  ☆26Updated last year
• Echo-Ji / ST-SSL_Dataset
  The data of paper "Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction".
  ☆31Updated last year
• HCPLab-SYSU / HIAM
  ☆15Updated last year
• liangzhehan / DMSTGCN
  ☆72Updated 2 years ago
• Echo-Ji / ST-SSL
  ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction
  ☆173Updated 3 months ago
• zouguojian / Travel-time-prediction
  When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
  ☆37Updated last year
• SYLan2019 / DSTAGNN
  DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting, which is accepted at ICML2022.
  ☆157Updated 2 years ago
• WoodSugar / GSTNet
  ☆14Updated 3 years ago
• ivechan / PVCGN
  code for "Physical-Virtual Collaboration Graph Network for Station-Level Metro Ridership Prediction"
  ☆41Updated 4 years ago