BGARN: Baselined Attention Recurrent Network for Origin-Destination Prediction for Ridesharing

Home

Discover

Bookmarks

Keep Up With Top Trending Papers In CS / AI / ML ...

Source arXiv

Machine Learning

Neural and Evolutionary Computing

BGARN: Baselined Attention Recurrent Network for Origin-Destination Prediction for Ridesharing

A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing

In this paper the baselined gated attention recurrent network (bgarn) is proposed, which uses graph convolution with multi-head gated attention to extract spatial features, a recurrent module to extract temporal features, and a baselined transferring layer to calculate the final results.

The model is implemented with pytorch and is experimentally evaluated using the new york taxi demand dataset.

The results show that the model outperforms all the other existing models in terms of prediction accuracy.

Authors

Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos

◐ Latest Activity

◐ Recommended Members