Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

Taxi Origin-Destination Demand Prediction

This is a Tensorflow implementation of Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction published in IEEE Transactions on Intelligent Transportation Systems. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all-region pairs in a future time interval. We address this problem with a novel contextualized spatial-temporal network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively.

If you use this code for your research, please cite our work:

@article{liu2019contextualized,
  title={Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction},
  author={Liu, Lingbo and Qiu, Zhilin and Li, Guanbin and Wang, Qing and Ouyang, Wanli and Lin, Liang},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2019},
  publisher={IEEE}
}

Requirements

Keras==2.1.4
pandas==0.17.1
tensorflow==1.14.0
numpy==1.16.2

Dataset Preprocessing

download NYC-TOD.tar.gz with following links and put it into folder NYC-TOD/.

Training and Testing

sh train_test.sh

About

Taxi Origin-Destination Demand Prediction

Resources

Releases

No releases published

Packages

No packages published