Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction

Abstract

The prediction of pick-up regions for online ride-hailing can reduce the number of vacant vehicles on the streets, which will optimize the transportation efficiency of cities, reduce energy consumption and carbon emissions, and increase the income of online ride-hailing drivers. However, traditional studies have ignored the temporal and spatial dependencies among pick-up regions and the effects of similarity of POI attributes in different regions in modelling, making the features of the model incomplete. To address the above problems, we propose a new multigraph aggregation spatiotemporal graph convolutional network (MAST-GCN) model to predict pick-up regions for online ride-hailing. In this paper, we propose a graph aggregation method to extract the spatiotemporal aspects and preference features of spatial graphs, order graphs, and POI graphs. GCN is used on the aggregated graphs to extract spatial dimensional features from graph-structured data. The historical data are sequentially divided into temporal granularity according to the period, and convolution operations are performed on the time axis to obtain the features in the temporal dimension. The attention mechanism is used to assign different weights to features with strong periodicity and strong correlation, which effectively solves the pick-up region prediction problem. We implemented the MAST-GCN model based on the PyTorch framework, stacked with a two-layer spatiotemporal graph convolution module, where the dimension of the graph convolution is 64. We evaluate the proposed model on two real-world large scale ride-hailing datasets. The results show that our method provides significant improvements over state-of-the-art baselines.