A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing
This work addresses the need for more accurate request prediction in ridesharing systems, which is crucial for optimizing vehicle schedules and efficiency, though it appears incremental as it builds on existing deep learning approaches.
The paper tackles the ridesharing origin-destination prediction problem by proposing the Baselined Gated Attention Recurrent Network (BGARN), which integrates spatial and temporal features to improve accuracy, and it outperforms existing models on the New York Taxi Demand Dataset.
Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals. As a result, recent years have witnessed an explosion of research interest in the RSODP (Origin-Destination Prediction for Ridesharing) problem with the goal of predicting the future ridesharing requests and providing schedules for vehicles ahead of time. Most of the existing prediction models utilise Deep Learning. However, they fail to effectively consider both spatial and temporal dynamics. 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 DGL (Deep Graph Library) and is experimentally evaluated using the New York Taxi Demand Dataset. The results show that BGARN outperforms all the other existing models in terms of prediction accuracy.