The subjectivity of selecting training parameters is an important factor affecting the accuracy
of short‐term passenger flow prediction of rail transit by long short‐term memory (LSTM) …

Traffic flow prediction is a challenging task in intelligent transportation systems. To improve
the accuracy of traffic flow prediction, graph convolutional neural networks and traffic …

Among the many issues facing the world, the issue of traffic accidents, many security
facilities have developed research on fingerprints, palmistry biometrics and other biometrics …

This paper presents a novel method to estimate queue length at signalised intersections
using connected vehicle (CV) data. The proposed queue length estimation method does not …

Nowadays, the intelligent transportation concept has become one of the most important
research fields. All of us depend on mobility, even when we talk about people, provide …

Recent research mainly applies deep learning (DL) methods to short-term traffic forecasting.
However, there is a growing interest in long-term forecasting, which allows action …

The study addresses proton exchange membrane fuel cell (PEMFC) internal temperature
predictions and the designed thermal management strategy based on the predicted …

With the capability of communicating with surrounding vehicles and infrastructures,
connected and automated vehicles (CAVs) can safely drive closer with reduced headway …

针对短时交通流量预测模型受噪声数据影响预测误差较大的问题, 提出了一种改进的长短期记忆
网络(long short-term memory, LSTM) 的短时交通流量预测模型———MVF-LSTM 模型 …

Estimation models from connected vehicles often assume low level parameters such as
arrival rates and market penetration rates as known or estimate them in real-time. At low …