Multi-attribute Graph Convolution Network for Regional Traffic Flow Prediction

Wang, Yue; Zhao, Aite; Li, Jianbo; Lv, Zhiqiang; Dong, Chuanhao; Li, Haoran

doi:10.1007/s11063-022-11036-9

Cited by 12 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We select several classic prediction models to compare with our model, including the Historical Average (HA), Auto-Regressive Moving Average (ARIMA), Graph Convolu-tional Network (GCN), Gated Recurrent Unit model (GRU), Temporal Graph Convolutional Network (T-GCN), Temporal Multi-Spatial Dependence Graph Convolutional Network (TmS-GCN) [48], and Multi-Attribute Graph Convolutional Network (MAGCN) [59].…”

Section: Baseline Methodsmentioning

confidence: 99%

Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

Yang,

2024

IJGI

View full text Add to dashboard Cite

The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. The application of Graph Convolutional Networks (GCNs) in traffic forecasting has inspired the development of a spatial–temporal model for grid-level prediction of the taxi demand–supply imbalance. However, spatial–temporal GCN prediction models conventionally capture only static inter-grid correlation features. This research aims to address the dynamic influences caused by taxi mobility and the variations of other transportation modes on the demand–supply dynamics between grids. To achieve this, we employ taxi trajectory data and develop a model that incorporates dynamic GCN and Gated Recurrent Units (GRUs) to predict grid-level imbalances. This model captures the dynamic inter-grid influences between neighboring grids in the spatial dimension. It also identifies trends and periodic changes in the temporal dimension. The validation of this model, using taxi trajectory data from Shenzhen city, indicates superior performance compared to classical time-series models and spatial–temporal GCN models. An ablation study is conducted to analyze the impact of various factors on the predictive accuracy. This study demonstrates the precision and applicability of the proposed model.

show abstract

Section: Baseline Methodsmentioning

confidence: 99%

Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

Yang,

2024

IJGI

View full text Add to dashboard Cite

show abstract

“…Yao et al proposed DMVST-Net [20], which uses both CNN and long short-term memory networks (LSTM) to capture complex spatio-temporal relationships. Wang [21] et al proposed MAGCN, MAGCN to divide the city into a grid of unequal size based on the attributes of the region. The regional traffic is then predicted using a matrix constructed using the traffic based on the Origin-Distribution of functional areas.…”

Section: Prediction Based On Convolutional Neural Networkmentioning

confidence: 99%

Multi-Spatial Scale Spatio-Temporal Transformer: A Refined Traffic Data Forecasting Method

张,

Zhang,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

Accurate traffic data forecasting is essential to improve the efficiency of intelligent transportation systems. Existing traffic prediction models only model spatial dependency based on the connectivity of roads, which overlooks the characteristic information of hidden spatial dependency and leads to a loss of prediction accuracy. In addition, there exists a strict relative positional relationship in the temporal dependency between traffic data, which is often overlooked by existing models, making it difficult to accurately model the temporal dependency. To solve these problems, this paper proposes a traffic data prediction method (MSS-STT) based on Multi-Spatial Scale Spatio-Temporal Transformer. MSS-STT first employs multiple specialized spatial Transformer networks to model different spatial scales in order to capture spatial dependencies and patterns at various levels. It also utilizes graph convolutional neural networks to extract static spatial structural features. Then, a gating mechanism is used to fuse the spatial dependencies from different spatial scales and the static spatial features. Finally, MSS-STT extracts different temporal dependencies by considering the order of time points and the varying contributions to the prediction from different relative positions between time points in historical traffic data. Experiments on three real-world datasets from the Caltrans Performance Measurement System (PeMS) show that the proposed MSS-STT model outperforms the state-of-the-art methods.

show abstract