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 • 2 Apr 2022 • Wenxiang Li, Yuanyuan Li, Ziyuan Pu, Long Cheng, Lei Wang, Linchuan Yang
Integrating the trip data with the COPERT model, this study calculates the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) to estimate the CO2 emission reduction of each ridesplitting trip.
no code implementations • 4 Feb 2022 • Meixin Zhu, Simon S. Du, Xuesong Wang, Hao, Yang, Ziyuan Pu, Yinhai Wang
Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
no code implementations • 9 Dec 2021 • Yifan Zhuang, Ziyuan Pu, Jia Hu, Yinhai Wang
Besides, the quantized IT-MN achieves an inference time of 0. 21 seconds per image pair on the edge device, which also demonstrates the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.
1 code implementation • 19 Apr 2021 • Lyuyi Zhu, Kairui Feng, Ziyuan Pu, Wei Ma
The diffusion attack aims to select and attack a small set of nodes to degrade the performance of the entire prediction model.
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.
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.
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 • 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 • 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 • 13 Oct 2019 • Meixin Zhu, Jingyun Hu, Hao, Yang, Ziyuan Pu, Yinhai Wang
Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i. e., compared to other modes, people would be more willing to tolerate long-distance metro trips.
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.