[PDF][PDF] Dynamic demand prediction for expanding electric vehicle sharing systems: A graph sequence learning approach

M Luo, H Wen, Y Luo, B Du, K Klemmer… - arXiv preprint arXiv …, 2019 - researchgate.net
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth
across the globe. Many car sharing service providers as well as automobile manufacturers …

D3P: Data-driven demand prediction for fast expanding electric vehicle sharing systems

M Luo, B Du, K Klemmer, H Zhu… - Proceedings of the …, 2020 - dl.acm.org
The future of urban mobility is expected to be shared and electric. It is not only a more
sustainable paradigm that can reduce emissions, but can also bring societal benefits by …

Spatial community-informed evolving graphs for demand prediction

Q Wang, B Guo, Y Ouyang, K Shu, Z Yu… - Joint European Conference …, 2020 - Springer
The rapidly increasing number of sharing bikes has facilitated people's daily commuting
significantly. However, the number of available bikes in different stations may be imbalanced …

A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction

H Qu, H Kuang, Q Wang, J Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging
space can significantly alleviate the growing load on intelligent transportation systems. As …

Learning heterogeneous spatial-temporal representation for bike-sharing demand prediction

Y Li, Z Zhu, D Kong, M Xu, Y Zhao - Proceedings of the AAAI conference on …, 2019 - aaai.org
Bike-sharing systems, aiming at meeting the public's need for” last mile” transportation, are
becoming popular in recent years. With an accurate demand prediction model, shared bikes …

Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network

S Wang, A Chen, P Wang, C Zhuge - Transportation Research Part C …, 2023 - Elsevier
Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …

Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting

L Bai, L Yao, S Kanhere, X Wang, Q Sheng - arXiv preprint arXiv …, 2019 - arxiv.org
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing
services. However, predicting passenger demand over multiple time horizons is generally …

A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing

H Kuang, H Qu, K Deng, J Li - Applied Energy, 2024 - Elsevier
A growing number of electric vehicles (EVs) is putting pressure on smart charging services.
As a foundation of informing drivers of vacant charging facilities and rationalizing pricing, an …

Deep spatio-temporal forecasting of electrical vehicle charging demand

FB Hüttel, I Peled, F Rodrigues, FC Pereira - arXiv preprint arXiv …, 2021 - arxiv.org
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends.
However, this requires that the energy used to meet the demand is green. To meet this …

Deep trip generation with graph neural networks for bike sharing system expansion

Y Liang, F Ding, G Huang, Z Zhao - Transportation Research Part C …, 2023 - Elsevier
Bike sharing is emerging globally as an active, convenient, and sustainable mode of
transportation. To plan successful bike-sharing systems (BSSs), many cities start from a …