Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

• URL: http://arxiv.org/abs/2308.05601v1
• Date: Thu, 10 Aug 2023 14:20:43 GMT
• Title: Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction
• Authors: Weilong Ding, Tianpu Zhang, Jianwu Wang, Zhuofeng Zhao
• Abstract summary: Daily traffic flow prediction still faces challenges at network-wide toll stations. In this paper, a correlative prediction method is proposed for daily traffic flow highway domain through flow-temporal deep learning. Our method shows clear improvement in predictive accuracy than baselines and practical benefits in business.
• Score: 0.5551832942032954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inter-city highway transportation is significant for urban life. As one of the key functions in intelligent transportation system (ITS), traffic evaluation always plays significant role nowadays, and daily traffic flow prediction still faces challenges at network-wide toll stations. On the one hand, the data imbalance in practice among various locations deteriorates the performance of prediction. On the other hand, complex correlative spatio-temporal factors cannot be comprehensively employed in long-term duration. In this paper, a prediction method is proposed for daily traffic flow in highway domain through spatio-temporal deep learning. In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations. And then, based on graph convolutional network, we construct networks in distinct semantics to capture spatio-temporal features. Beside that, meteorology and calendar features are used by our model in the full connection stage to extra external characteristics of traffic flow. By extensive experiments and case studies in one Chinese provincial highway, our method shows clear improvement in predictive accuracy than baselines and practical benefits in business.

Related papers

• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis [6.8775337739726226]
  We propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns.
  arXiv  Detail & Related papers  (2023-08-20T14:31:55Z)
• Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction [3.8277254030074537]
  Deep-temporal learning method is proposed to predict daily traffic volume in three phases. In decision phase, traffic volumes on a coming day network-wide toll stations would be achieved effectively.
  arXiv  Detail & Related papers  (2023-08-11T14:33:20Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction [12.223433627287605]
  Traffic data are highly nonlinear and have complex spatial correlations between road nodes. Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations. We propose a new prediction model which captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data.
  arXiv  Detail & Related papers  (2022-03-21T06:38:34Z)
• Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks [4.318655493189584]
  We propose a model to predict traffic speed under the impact of construction work. The model is based on the powerful attention-based,temporal graph convolution architecture but utilizes various channels to integrate different sources of information. The model is evaluated on two benchmark datasets and a novel dataset we have collected over the bustling roadway's corner in Northern Virginia.
  arXiv  Detail & Related papers  (2021-10-04T16:07:37Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• 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)

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.