Search Results for author: Zhiyong Cui

Found 12 papers, 4 papers with code

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

no code implementations19 Jun 2022 Meng-Ju Tsai, Zhiyong Cui, Hao Yang, Cole Kopca, Sophie Tien, Yinhai Wang

To better manage future roadway capacity and accommodate social and human impacts, it is crucial to propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies.

Management Time Series +1

Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications

no code implementations2 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.

Autonomous Driving Edge-computing +2

Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values

no code implementations24 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.

Imputation Traffic Prediction

Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit

1 code implementation29 Dec 2019 Jinlei Zhang, Feng Chen, Zhiyong Cui, Yinan Guo, Yadi Zhu

Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting.

Graph Markov Network for Traffic Forecasting with Missing Data

2 code implementations10 Dec 2019 Zhiyong Cui, Longfei Lin, Ziyuan Pu, Yinhai Wang

Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data.

Edge-computing Imputation +1

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang

A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.

Decision Making Friction +1

Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang

The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.


Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

no code implementations5 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.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

2 code implementations20 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.

Traffic Prediction

Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

1 code implementation7 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.

Time Series

A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation

no code implementations5 Jan 2018 Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang

The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation.

Cannot find the paper you are looking for? You can Submit a new open access paper.