Traffic map prediction using UNet based deep convolutional neural network

Abstract

This paper describes our UNet based deep convolutional neural network approach on the Traffic4cast challenge 2019. Challenges task is to predict future traffic flow volume, heading and speed on high resolution whole city map. We used UNet based deep convolutional neural network to train predictive model for the short term traffic forecast. On each convolution block, layers are densely connected with subsequent layers like a DenseNet. Trained and evaluated on the real world data set collected from three distinct cities in the world, our method achieved best performance in this challenge.

NeuralIPS 2019 Traffic4cast Workshop

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Cited by in corpus (5)

• Utilizing UNet for the future traffic map prediction task Traffic4cast challenge 2020
• Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
• Temporal Autoencoder with U-Net Style Skip-Connections for Frame Prediction
• Towards Good Practices of U-Net for Traffic Forecasting
• Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation