no code implementations • 9 Nov 2022 • Talha Azfar, Jinlong Li, Hongkai Yu, Ruey Long Cheu, Yisheng Lv, Ruimin Ke
This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments.
no code implementations • 2 Aug 2020 • Ruimin Ke, Zhiyong Cui, Yanlong Chen, Meixin Zhu, Hao Yang, Yinhai Wang
It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
no code implementations • 24 May 2020 • Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang
Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model's input data contains different patterns of missing values.
no code implementations • 5 Mar 2019 • Ruimin Ke, Wan Li, Zhiyong Cui, Yinhai Wang
In this model, we first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices.
1 code implementation • 29 Jan 2019 • Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).
2 code implementations • 20 Feb 2018 • Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Ziyuan Pu, Yinhai Wang
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
1 code implementation • 7 Jan 2018 • Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang
In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed.