Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

Geng, Xu; Li, Yaguang; Wang, Leye; Zhang, Lingyu; Yang, Qiang; Ye, Jieping; Liu, Yan

doi:10.1609/aaai.v33i01.33013656

Cited by 689 publications

(404 citation statements)

References 19 publications

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“…Recent works include DCRNN [15], STDN [16], TGC-LSTM [17], ST-MGCN [18] and STGCN [8], et al They show appealing results on public datasets. But DCRNN is less efficient as it involves recurrent feedforward.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction

Chen

Lai

et al. 2020

IEEE Access

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Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatiotemporal structures and predict future traffic flows from surveillance video data. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flows. Then several such layers are stacked together to predict future flows over time. Meanwhile, the relations between traffic flows in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction

Chen

Lai

et al. 2020

IEEE Access

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“…Next, we used a deep learning method to predict the displacement of the FCMN. Current advances in the deep learning domain make it possible to model the complex spatiotemporal correlation in region-based spatiotemporal prediction [29]. For temporally correlated data, typical models include long-short term memory (LSTM) and gated recurrent units (GRU).…”

Section: Displacement Prediction Of Natural and Human-induced Slopesmentioning

confidence: 99%

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

Ma¹,

Mei²,

Zhang³

et al. 2020

Preprint

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Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of single monitoring points based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system, and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

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“…As a result, capturing such spatial-temporal correlations is essential for accurately predicting MSP flows. Although a series of studies have effectively accomplished other spatial-temporal prediction tasks [1][2][3][4][5][6][7][8][9][10], such as traffic volume and highway speed prediction, they invariably become suboptimal for MSP flow prediction due to the unique spatial-temporal characteristics of MSP flows. In what follows, we will elaborate upon the design of our novel deep learning framework optimized to better capture MSP flows' spatial-temporal correlations.…”

Section: Introductionmentioning

confidence: 99%

“…From the temporal perspective, RNN-based models [8][9][10][11][12] have been regarded as effective to capture the non-linear temporal correlation of short time series. However, the MSP flows at least in the past few hours have to be considered to predict those in the future.…”

Section: Introductionmentioning

confidence: 99%

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Sun

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Recent years have witnessed a drastic increase in the number of urban metro passengers, which inevitably causes the overcrowdedness in the metro systems of many cities. Clearly, an accurate prediction of passenger flows at metro stations is critical for a variety of metro system management operations, such as line scheduling and staff preallocation, that help alleviate such overcrowdedness. Thus, in this paper, we aim to address the problem of accurately predicting metro station passenger (MSP) flows. Similar to other traffic data, such as road traffic volume and highway speed, MSP flows are also spatial-temporal in nature. However, existing methods for other traffic prediction tasks are usually suboptimal to predict MSP flows due to MSP flows' unique spatial-temporal characteristics. As a result, we propose a novel deep learning framework STP-TrellisNets, which for the first time augments the newly-emerged temporal convolutional framework TrellisNet for spatial-temporal prediction. The temporal module of STP-TrellisNets (named CP-TrellisNets) employs two TrellisNets in serial to jointly capture the short-and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNets, its spatial module (named GC-TrellisNet) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements multiple diffusion graph convolutional networks (DGCNs) in time-series order with their outputs connected to a TrellisNet to capture the dynamics of such spatial correlation. Clearly, GC-TrellisNet essentially integrates TrellisNet with graph convolution, and empowers TrellisNet with the ability to capture dynamic graph-structured correlation. We conduct extensive experiments with two large-scale real-world automated fare collection datasets, which contain respectively about 1.5 billion records in Shenzhen, China and 70 million records in Hangzhou, China. The experimental results demonstrate that STP-TrellisNets outperforms the state-of-the-art baselines. CCS CONCEPTS • Information systems → Spatial-temporal systems; Data mining; • Computing methodologies → Neural networks.

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Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

Cited by 689 publications

References 19 publications

Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction

Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

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