AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

• URL: http://arxiv.org/abs/2209.03356v1
• Date: Wed, 7 Sep 2022 13:51:45 GMT
• Title: AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
• Authors: Ruikang Luo, Yaofeng Song, Liping Huang, Yicheng Zhang and Rong Su
• Abstract summary: Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study. Our model is tested on the data collected in Dundee City.
• Score: 8.596556653895028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considering comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data. And the external factors are modeled as dynamic attributes by the attribute-augmented encoder for training. AST-GIN model is tested on the data collected in Dundee City and experimental results show the effectiveness of our model considering external factors influence over various horizon settings compared with other baselines.

Related papers

• Attention-based Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences [5.687001127686438]
  We propose an attention-based heterogeneous data fusion approach (ADF) for electric vehicle charging demand prediction. To learn non-pairwise relationships, we cluster service areas by the types and numbers of points of interest in the areas. We demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our clustering method.
  arXiv  Detail & Related papers  (2024-10-24T14:19:38Z)
• 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)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Exploring Attention GAN for Vehicle Motion Prediction [2.887073662645855]
  We study the influence of attention in generative models for motion prediction, considering both physical and social context. We validate our method using the Argoverse Motion Forecasting Benchmark 1.1, achieving competitive unimodal results.
  arXiv  Detail & Related papers  (2022-09-26T13:18:32Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Random vector functional link neural network based ensemble deep learning for short-term load forecasting [14.184042046855884]
  This paper proposes a novel ensemble deep Random Functional Link (edRVFL) network for electricity load forecasting. The hidden layers are stacked to enforce deep representation learning. The model generates the forecasts by ensembling the outputs of each layer.
  arXiv  Detail & Related papers  (2021-07-30T01:20:48Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting [8.490904938246347]
  This study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network.
  arXiv  Detail & Related papers  (2020-11-26T14:15:52Z)
• AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting [12.284512000306314]
  We propose attribute-augmentedtemporal graph convolutional network (AST-GCN) to integrate external factors into traffic forecasting schemes. Experiments show the effectiveness of considering external information on traffic forecasting tasks when compared to traditional traffic prediction methods.
  arXiv  Detail & Related papers  (2020-11-22T12:49:55Z)

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.