Probabilistic charging power forecast of EVCS: Reinforcement learning assisted deep learning approach

Y Li, S He, Y Li, L Ge, S Lou… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely
deployed with the development of large-scale transportation electrifications. However, since …

A Poisson-based distribution learning framework for short-term prediction of food delivery demand ranges

J Liang, J Ke, H Wang, H Ye… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The COVID-19 pandemic has caused a dramatic change in the demand composition of
restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such …

Spatiotemporal residual graph attention network for traffic flow forecasting

Q Zhang, C Li, F Su, Y Li - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic
management and control. In order to capture the spatiotemporal characteristics of the traffic …

Overview of Cooperative Fault-Tolerant Control Driven by the Full Information Chain of Intelligent Connected Vehicle Platoons Under the Zero-Trust Framework …

D Huang, Y Na, Y Liu, Z Zhang… - IEEE Intelligent …, 2023 - ieeexplore.ieee.org
The zero-trust framework is a potential solution to address complex dynamic behaviors,
information interactions, complex network topologies, and environmental security threats to …

Hmdrl: Hierarchical mixed deep reinforcement learning to balance vehicle supply and demand

J Xi, F Zhu, P Ye, Y Lv, H Tang… - IEEE Transactions On …, 2022 - ieeexplore.ieee.org
The imbalance of vehicle supply and demand is a common phenomenon that influences the
efficiency of online ride-hailing systems greatly. To address this problem, a three-level …

STHAN: Transportation demand forecasting with compound spatio-temporal relationships

S Ling, Z Yu, S Cao, H Zhang, S Hu - ACM Transactions on Knowledge …, 2023 - dl.acm.org
Transportation demand forecasting is a critical precondition of optimal online transportation
dispatch, which will greatly reduce drivers' wasted mileage and customers' waiting time …

A spatio-temporal approach with self-corrective causal inference for flight delay prediction

Q Zhu, S Chen, T Guo, Y Lv… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Accurate flight delay prediction is crucial for the secure and effective operation of the air
traffic system. Recent advances in modeling inter-airport relationships present a promising …

Multiagent deep reinforcement learning for automated truck platooning control

R Lian, Z Li, B Wen, J Wei, J Zhang… - IEEE Intelligent …, 2023 - ieeexplore.ieee.org
Human-leading automated truck platooning has been an effective technique to improve
traffic capacity and fuel economy and eliminate uncertainties of the traffic environment …

Data-driven distance metrics for kriging-short-term urban traffic state prediction

B Varga, M Pereira, B Kulcsár… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution
for many ITS applications. In this work, a geostatistical framework, kriging is extended in …

HMIAN: a hierarchical mapping and interactive attention data fusion network for traffic forecasting

J Sun, M Peng, H Jiang, Q Hong… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
With the development of intelligent transportation system (ITS), the vital technology of ITS,
short-term traffic forecasting, gains increasing attention. However, the existing prediction …