Search Results for author: Ziyuan Pu

Found 13 papers, 5 papers with code

Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data

no code implementations2 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.

Interpretable Machine Learning

TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer

no code implementations4 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.

Trajectory Prediction

Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection

no code implementations9 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.

Edge-computing Pedestrian Detection +1

Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models

1 code implementation19 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.

Adversarial Attack Management +1

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

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.


Personalized Context-Aware Multi-Modal Transportation Recommendation

no code implementations13 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.


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

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