no code implementations • 5 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.
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.
no code implementations • 1 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.
no code implementations • 1 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.
no code implementations • 27 Oct 2019 • Meixin Zhu, Jingyun Hu, Ziyuan Pu, Zhiyong Cui, Liangwu Yan, Yinhai Wang
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet.
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 • 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 • 19 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.
no code implementations • 20 Dec 2023 • Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
no code implementations • 1 Jan 2024 • Shuang Li, Ziyuan Pu, Zhiyong Cui, Seunghyeon Lee, Xiucheng Guo, Dong Ngoduy
This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed.
no code implementations • 4 Mar 2024 • Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, Zhiyong Cui
Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management.
1 code implementation • 29 Feb 2024 • Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang, Zhiyong Cui, Shengbo Eben Li, Chengzhong Xu
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
2 code implementations • 10 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.
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.
1 code implementation • 29 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.
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.