Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges …
P Dong, J Zhao, X Liu, J Wu, X Xu, Y Liu… - … and Sustainable Energy …, 2022 - Elsevier
The rapid development of intelligent and connected technologies is conducive to the
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …
[PDF][PDF] A comprehensive study of speed prediction in transportation system: From vehicle to traffic
In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
Efficient training of physics‐informed neural networks via importance sampling
MA Nabian, RJ Gladstone… - Computer‐Aided Civil and …, 2021 - Wiley Online Library
Physics‐informed neural networks (PINNs) are a class of deep neural networks that are
trained, using automatic differentiation, to compute the response of systems governed by …
trained, using automatic differentiation, to compute the response of systems governed by …
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
A short-term traffic flow prediction model based on an improved gate recurrent unit neural network
W Shu, K Cai, NN Xiong - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
With the increasing demand for intelligent transportation systems, short-term traffic flow
prediction has become an important research direction. The memory unit of a Long Short …
prediction has become an important research direction. The memory unit of a Long Short …
Congestion prediction for smart sustainable cities using IoT and machine learning approaches
Congestion on road networks has a negative impact on sustainability in many cities through
the exacerbation of air pollution. Smart congestion management allows road users to avoid …
the exacerbation of air pollution. Smart congestion management allows road users to avoid …
Deep learning architecture for short-term passenger flow forecasting in urban rail transit
Short-term passenger flow forecasting is an essential component in urban rail transit
operation. Emerging deep learning models provide good insight into improving prediction …
operation. Emerging deep learning models provide good insight into improving prediction …
Short-term prediction of lane-level traffic speeds: A fusion deep learning model
Accurate and robust short-term traffic prediction is an important part of advanced traveler
information systems. With the development of intelligent navigation and autonomous driving …
information systems. With the development of intelligent navigation and autonomous driving …
Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network
G Jin, Y Cui, L Zeng, H Tang, Y Feng… - … Research Part C …, 2020 - Elsevier
Urban ride-hailing demand prediction is a long-term but challenging task for online car-
hailing system decision, taxi scheduling and intelligent transportation construction. Accurate …
hailing system decision, taxi scheduling and intelligent transportation construction. Accurate …
A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection
Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …
1970s. Nowadays, there is a need for prominent solutions capable of mining information …