Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph AttentionGraph Neural NetworkPredictionSchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction
Predicting traffic accidents is the key to sustainable city management, which requires effective address of the dynamic and complex spatiotemporal characteristics of cities. Current data-driven models often struggle with…
Contrastive LearningGraph LearningManagementA Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting
Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neur…
A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Tr…
Demand ForecastingManagementHSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
Spatiotemporal forecasting has emerged as an indispensable building block of diverse smart city applications, such as intelligent transportation and smart energy management. Recent advancements have uncovered that the pe…
energy managementFederated LearningRepresentation LearningTime Series+1Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction
The continuous expansion of the urban construction scale has recently contributed to the demand for the dynamics of traffic intersections that are managed, making adaptive modellings become a hot topic. Existing deep lea…
Time SeriesTime Series AnalysisTime Series ForecastingTraffic Prediction