TLab: Traffic Map Movie Forecasting Based on HR-NET

11/13/2020

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The problem of the effective prediction for large-scale spatio-temporal traffic data has long haunted researchers in the field of intelligent transportation. Limited by the quantity of data, citywide traffic state prediction was seldom achieved. Hence the complex urban transportation system of an entire city cannot be truly understood. Thanks to the efforts of organizations like IARAI, the massive open data provided by them has made the research possible. In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet. Through feature engineering, the hand-crafted features are input into the model in a form of channels. It is worth noting that, to learn the inherent attributes of geographical locations, we proposed a novel method called geo-embedding, which contributes to significant improvement in the accuracy of the model. In addition, we explored the influence of the selection of activation functions and optimizers, as well as tricks during model training on the model performance. In terms of prediction accuracy, our solution has won 2nd place in NeurIPS 2020, Traffic4cast Challenge.

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TLab: Traffic Map Movie Forecasting Based on HR-NET

Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Towards Good Practices of U-Net for Traffic Forecasting

Traffic4cast-Traffic Map Movie Forecasting – Team MIE-Lab

TLab: Traffic Map Movie Forecasting Based on HR-NET

Related Research

Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Towards Good Practices of U-Net for Traffic Forecasting

Traffic4cast-Traffic Map Movie Forecasting – Team MIE-Lab