Wenqi Lu

Orcid: 0000-0003-1076-6985

Affiliations:
  • Southeast University, School of Transportation, Nanjing, China
  • Beijing Jiaotong University, SChool of transportation planning and management, China
  • Hohai University, School of Transportation engineering, Nanjing, China (former)


According to our database1, Wenqi Lu authored at least 11 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Deploying Roadside Unit Efficiently in VANETs: A Multi-Objective Delay-Based Optimization Strategy Using Lagrangian Relaxation.
IEEE Trans. Intell. Transp. Syst., February, 2024

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.
Sensors, 2024

2023
Learning Car-Following Behaviors for a Connected Automated Vehicle System: An Improved Sequence-to-Sequence Deep Learning Model.
IEEE Access, 2023

2022
Lane-Level Traffic Speed Forecasting: A Novel Mixed Deep Learning Model.
IEEE Trans. Intell. Transp. Syst., 2022

Target encirclement of moving ride-hailing vehicle under uncertain environment: A multi-vehicle mutual rescue model.
Comput. Oper. Res., 2022

2021
Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis.
Sensors, 2021

2020
An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning.
IEEE Trans. Intell. Transp. Syst., 2020

A Hybrid Model for Lane-Level Traffic Flow Forecasting Based on Complete Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting.
IEEE Access, 2020

An Attention-based Approach for Traffic Conditions Forecasting Considering Spatial-Temporal Features.
Proceedings of the 5th IEEE International Conference on Intelligent Transportation Engineering, 2020

A Spatial and Temporal Combination Model for Traffic Flow: A Case Study of Beijing Expressway.
Proceedings of the 5th IEEE International Conference on Intelligent Transportation Engineering, 2020

2019
A Deep Learning Framework for Cycling Maneuvers Classification.
IEEE Access, 2019


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