Spatio-Temporal Graph Convolutional Networks for EV Charging Demand Forecasting Using Real-World Multi-Modal Data Integration

• URL: http://arxiv.org/abs/2510.09048v3
• Date: Fri, 07 Nov 2025 00:33:42 GMT
• Title: Spatio-Temporal Graph Convolutional Networks for EV Charging Demand Forecasting Using Real-World Multi-Modal Data Integration
• Authors: Jose Tupayachi, Mustafa C. Camur, Kevin Heaslip, Xueping Li,
• Abstract summary: Transportation remains a major contributor to greenhouse gas emissions.<n>This study introduces TW-GCN, a-temporal forecasting framework that combines Graph Conal Networks with temporal architectures.<n>We utilize real-world traffic flows, weather conditions, and proprietary data to capture both spatial dependencies and temporal dynamics.
• Score: 1.83159883923531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transportation remains a major contributor to greenhouse gas emissions, highlighting the urgency of transitioning toward sustainable alternatives such as electric vehicles (EVs). Yet, uneven spatial distribution and irregular utilization of charging infrastructure create challenges for both power grid stability and investment planning. This study introduces TW-GCN, a spatio-temporal forecasting framework that combines Graph Convolutional Networks with temporal architectures to predict EV charging demand in Tennessee, United States (U.S.). We utilize real-world traffic flows, weather conditions, and proprietary data provided by one of the largest EV infrastructure company in the U.S. to capture both spatial dependencies and temporal dynamics. Extensive experiments across varying lag horizons, clustering strategies, and sequence lengths reveal that mid-horizon (3-hour) forecasts achieve the best balance between responsiveness and stability, with 1DCNN consistently outperforming other temporal models. Regional analysis shows disparities in predictive accuracy across East, Middle, and West Tennessee, reflecting how station density, population, and local demand variability shape model performance. The proposed TW-GCN framework advances the integration of data-driven intelligence into EV infrastructure planning, supporting both sustainable mobility transitions and resilient grid management.

Related papers

• Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks [5.876489372173655]
  We propose a unified Digital Twin (DT) and Graph Neural Network (GNN) framework for scalable, data-driven pavement health monitoring and predictive maintenance.<n>Our model achieves an R2 of 0.3798, outperforming baseline regressors and effectively capturing non-linear degradation.<n>This DT-GNN integration enhances forecasting precision and establishes a closed feedback loop for continuous improvement.
  arXiv  Detail & Related papers  (2025-11-04T19:59:17Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• DOVA-PATBM: An Intelligent, Adaptive, and Scalable Framework for Optimizing Large-Scale EV Charging Infrastructure [3.74242093516574]
  We present DOVA-PATBM (Deployment optimisation with Voronoi-oriented, Adaptive, POI-Aware Temporal Behaviour Model), a geo-computational framework that unifies contexts in a single pipeline.<n>The methodises heterogeneous data ( centrality, population, night lights, POIs, and feeder lines) onto a hierarchical H3 grid.<n>It infers intersection importance with a zone-normalised graph neural network model, and overlays a Voronoi tessellation that guarantees at least one five-port DC fast charger within every 30 km radius.
  arXiv  Detail & Related papers  (2025-06-18T09:15:18Z)
• Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery [6.061650526072546]
  We analyze charging data from Palo Alto and Boulder to uncover latent relationships between station characteristics and utilization.<n>Applying structural learning algorithms to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes.<n>We develop an optimization framework that translates these insights into actionable placement recommendations.
  arXiv  Detail & Related papers  (2025-03-21T11:15:02Z)
• DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership [12.6020349733674]
  This paper introduces DST-TransitNet, a hybrid Deep Learning model for system-wide ridership prediction. It is tested on Bogota's BRT system data, with three distinct social scenarios. It outperforms state-of-the-art models in precision, efficiency and robustness.
  arXiv  Detail & Related papers  (2024-10-19T06:59:39Z)
• 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 Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach [26.92971702938603]
  We present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows on roadways. We propose a two-stage neural network framework (STGNN) to process telecom data and integrate directional and geographic insights. We show how to incorporate the framework into real-world transportation systems enhancing sustainable urban mobility.
  arXiv  Detail & Related papers  (2024-05-26T17:14:50Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge [0.0]
  The Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020- 2021. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy.
  arXiv  Detail & Related papers  (2023-06-09T07:22:18Z)
• 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)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)

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.