The paper presents a GeoAI framework that uses a variational autoencoder and a graph convolutional network to analyze high‑dimensional geospatial data for electric vehicle charging station location selection. By compressing EV usage, land‑use, population, and traffic attributes into a latent space, the model predicts suitable future station sites and identifies 27 new candidates in Bryan‑College Station, Texas. Two policy scenarios—maximizing geospatial similarity versus minimizing travel distance—demonstrate how different strategic objectives influence station placement outcomes.
As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many EV charging station location problem (CSLP) stu...
arXiv:2405. 00742v2 Announce Type: replace-cross Abstract: Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion.
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
arXiv:2603.05581v2 Announce Type: replace-cross Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...
The paper introduces a conditional variational autoencoder (CVAE) to generate synthetic electric vehicle (EV) charging sessions from real transaction-level data. It trains on engineered features such as plug‑in duration, charging duration, delivered energy, charging delay, and cyclical time‑of‑week, conditioning on day of week and managed charging status. Evaluation shows the synthetic data preserves key statistical properties and supports predictive modelling tasks via a Train‑on‑Synthetic‑Test‑on‑Real protocol.
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
EVTradeMatch is a mobility-aware, multi-objective matching framework that coordinates peer-to-peer energy trading between electric vehicles (EVs). It uses a prediction-guided score for charging-node suitability and formulates the matching problem as a mixed-integer linear program, solved via a tailored NSGA-II algorithm. Experiments show significant gains in transferred energy, charging-node suitability, and matching coverage compared to existing proximity- and auction-based methods.
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
TraveL is a Transformer-based framework that learns distributional representations of road network paths by incorporating traveler behaviors and regional road segment correlations. It encodes a path and its starting time into a distribution, enabling decoding of possible traveler behaviors. Experiments demonstrate that TraveL surpasses state‑of‑the‑art methods on synthetic and real datasets, improving travel time distribution estimation, path similarity prediction, and destination prediction metrics.