Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Traffic flow prediction is an essential part of the intelligent transport system. This is the
accurate estimation of traffic flow in a given region at a particular interval of time in the future …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …

The trend of the era of the Internet of Everything has promoted the integration of various
industries and the Internet of Things (IoT) technology, and the scope of influence of the IoT is …

Abstract Machine Learning has played a major role in various applications including
Autonomous Vehicles and Intelligent Transportation Systems. Utilizing a deep convolutional …

The expansion of the Internet of Things has resulted in new creative solutions, such as smart
cities, that have made our lives more productive, convenient, and intelligent. The core of …

摘摇要: 对深度学习模型应用于时空序列预测的最新进展进行总结. 首先介绍时空序列数据的
属性及类型, 并进行相应的实例化与表示. 接着针对时空序列数据存在的3 …

针对现有预测模型不能充分提取交通流时空特征的问题, 提出一种基于改进卷积神经网络(
convolutional neural network, CNN) 和长短时记忆(long short-term memory, LSTM) …

The predictive cruise control (PCC) is a promising method to optimize energy consumption
of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and …