24 citations · 55 across the 5 of their papers we have counts for
5 papers
Learning dynamic and hierarchical traffic spatiotemporal features with Transformer
Haoyang Yan, Xiaolei Ma
Traffic forecasting is an indispensable part of Intelligent transportation systems (ITS), and long-term network-wide accurate traffic speed forecasting is one of the most challengi…
Mining Truck Platooning Patterns Through Massive Trajectory Data
Xiaolei Ma, Enze Huo, Haiyang Yu +1
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…
Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models
Xiaolei Ma, Yi Li, Zhiyong Cui +1
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies o…
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Haiyang Yu, Zhihai Wu, Shuqin Wang +2
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in whi…
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Xiaolei Ma, Zhuang Dai, Zhengbing He +3
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. Spati…