Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction

Abstract

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of CNNs and RNNs by meshing plentiful pixel-level labeled data, but spatial data sparsity and limited learning capabilities on temporal long-term dependencies are still two striking bottlenecks. To address these limitations, we propose a new virtual graph modeling method to focus on significant demand regions and a novel Deep Multi-View Spatiotemporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen learning capabilities of spatial dynamics and temporal long-term dependencies. Specifically, DMVST-VGNN integrates the structures of 1D Convolutional Neural Network, Multi Graph Attention Neural Network and Transformer layer, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, experiments are conducted on two large-scale New York City datasets in fine-grained prediction scenes. And the experimental results demonstrate effectiveness and superiority of DMVST-VGNN framework in significant citywide ride-hailing demand prediction.

References in corpus (8)

• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
• Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
• Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
• Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction
• Spatial-Temporal Self-Attention Network for Flow Prediction

Cited by in corpus (4)

• Graph Neural Network for Traffic Forecasting: A Survey
• A Comprehensive Survey on Deep Graph Representation Learning
• Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
• A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing