Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

• URL: http://arxiv.org/abs/2006.05905v4
• Date: Sun, 17 Apr 2022 01:41:27 GMT
• Title: Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction
• Authors: Weiguo Pian, Yingbo Wu, Xiangmou Qu, Junpeng Cai, Ziyi Kou
• Abstract summary: Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Existing ride-hailing demand prediction methods only assign the same importance to different neighbor regions. We propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method.
• Score: 3.084885761077852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines.

Related papers

• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting [6.867331860819595]
  Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately. We propose to directly model the cross-spatial-temporal correlations on the spatial-temporal graph using local multi-head self-attentions.
  arXiv  Detail & Related papers  (2022-07-09T19:21:00Z)
• A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset [9.897431292540393]
  Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a fundamental challenge. In this paper, we tackle this problem from both aspects of network structure and data-set formulation.
  arXiv  Detail & Related papers  (2022-03-23T16:03:55Z)
• CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting [7.169972421976212]
  We propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for traffic forecasting. We design a gating mechanism to sparse the cross-time dynamic graph, which conforms to the sparse spatial correlations in the real world.
  arXiv  Detail & Related papers  (2021-12-06T01:56:07Z)
• STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting [7.232141271583618]
  We propose a novel deep learning model for traffic forecasting named inefficient-Context Spatio-Temporal Joint Linear Attention (SSTLA) SSTLA applies linear attention to a joint graph to capture global dependence between alltemporal- nodes efficiently. Experiments on two real-world traffic datasets, England and Temporal7, demonstrate that our STJLA can achieve 9.83% and 3.08% 3.08% accuracy in MAE measure over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-12-04T06:39:18Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph [31.062303389341317]
  We propose a noveltemporal graph attention network namely Gallat (Graph prediction with all attention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of DDW graphs, we build three attention layers to fully capture the dependencies among different regions across all historical time slots. We evaluate proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-01-04T03:32:01Z)
• On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition [97.14064057840089]
  Graphal networks (GCNs) promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a graph. Most of the recently proposed G-temporal-based methods improve the performance by learning the graph structure at each layer of the network.
  arXiv  Detail & Related papers  (2020-11-07T19:03:04Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.