no code implementations • 7 May 2017 • Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, Xiaolei Ma
Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network.
no code implementations • 16 Jan 2017 • Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Na, Yong Wang, Yunpeng Wang
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy.
no code implementations • 11 Oct 2020 • Xiaolei Ma, Enze Huo, Haiyang Yu, Honghai Li
Truck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements.
no code implementations • 12 Apr 2021 • Haoyang Yan, Xiaolei Ma
However, the predefined fixed adjacent matrix is limited in reflecting the actual dependence of traffic flow.