Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks

Abstract

Accurate and reliable taxi demand prediction is of great importance for intelligent planning and management in the transportation system. To collectively forecast the taxi demand in all regions of a city, many existing studies focus on the capturing of spatial and temporal correlations among regions but ignore the local statistical differences throughout the geographical layout of a city. This limits the further improvement of prediction accuracy. In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. We evaluate the proposed model on a real dataset from a ride-hailing service platform (DiDi Chuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we further explore the working mechanism of the proposed model by visualizing its feature extraction processes. The visualization results showed that our approach can better localize and capture useful features from spatial-related regions.