The codes and data of paper "Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks".

This is the overview of Curb-GAN. The implementation is realized by Pytorch.

Since the whole dataset is huge, here we only provide sample datasets.

The sample data is located in data folder, please unzip data/speed_data.zip and data/inflow_data.zip and put the corresponding data files into traffic_speed_estimation/ and taxi_inflow_estimation/ folders, respectively.

• Cuda 9.2
• pytorch 0.4.1
• Python 3.6.7
• Devices: NVIDIA GTX 1080 GPUs
• System: Ubuntu 16.04

Just take traffic speed estimation as an example:

1. cd traffic_speed_estimation/.
2. Running the codes: python train.py. (The parameters are defined in train.py)
3. The parameters of the trained model will be saved in to traffic_speed_estimation//CurbGAN_train.

The training process for taxi inflow estimation is similar.