Yu Lei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yu Lei has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
Guangyan He
College of Computer Science, Chongqing University, Chongqing, China
1 shared publication- Hui Zu
Chongqing Vehicle Test and Research Institude, Chongqing, Chongqing, China
1 shared publication - Lei Ye
Institute of Electronic and Engineering Technology, Harbin Institute of Technology, Harbin 150001, China
1 shared publication - Lei Ye
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
1 shared publication - Lingfeng Qi
Chongqing University
1 shared publication Lingqiu Zeng
College of Computer Science, Chongqing University, Chongqing, China
1 shared publication
Latest Publications
- 19 September 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3718-3725
Research on Travel Time Prediction of Multiple Bus Trips Based on MDARNN
The accuracy of travel time prediction is not only decided by the state of the current journey but also affected by that of the transfer journey. This paper focuses on the transit transfer problem of urban public transport and aims to improve the performance of multi-route bus travel time prediction. In this paper, two kinds of infect factors, which are macro infect factors and local infect factors, are defined. Then, a pre-processing approach is proposed to construct a travel chain, which is used to compensate the shortage of raw data set. Multi-route bus travel time is divided into four parts, which are travel time of the current driving segment (CDS), bus stop dwelling time module, stop-stop travel time, and transfer point waiting time. A parrel processing architecture, which includes four learning blocks, is designed to fulfill the prediction process of the above four time parts. The classic LSTM model is used to predict the CDS travel time and transfer point waiting time. An improved DA-RNN model, the MDARNN model, is proposed to predict the bus stop dwelling time module, stop-stop travel time. Moreover, a real-time traffic flow model is used to calibrate the prediction time of all four sub-models. Experiment results prove the validity of the proposed methods.
- 19 September 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3718-3725
Research on Travel Time Prediction of Multiple Bus Trips Based on MDARNN
The accuracy of travel time prediction is not only decided by the state of the current journey but also affected by that of the transfer journey. This paper focuses on the transit transfer problem of urban public transport and aims to improve the performance of multi-route bus travel time prediction. In this paper, two kinds of infect factors, which are macro infect factors and local infect factors, are defined. Then, a pre-processing approach is proposed to construct a travel chain, which is used to compensate the shortage of raw data set. Multi-route bus travel time is divided into four parts, which are travel time of the current driving segment (CDS), bus stop dwelling time module, stop-stop travel time, and transfer point waiting time. A parrel processing architecture, which includes four learning blocks, is designed to fulfill the prediction process of the above four time parts. The classic LSTM model is used to predict the CDS travel time and transfer point waiting time. An improved DA-RNN model, the MDARNN model, is proposed to predict the bus stop dwelling time module, stop-stop travel time. Moreover, a real-time traffic flow model is used to calibrate the prediction time of all four sub-models. Experiment results prove the validity of the proposed methods.
- Open Access
- 26 April 2021
- Published by Wiley in Mobile Information Systems
- Vol. 2021, 1-13
An MEC Architecture-Oriented Improved RRT Algorithm for Regional Trajectory Planning
Multi-access Edge Computing (MEC), which could provide real-time computing ability, is considered as an effective approach to improve performance of Vehicular Ad Hoc Network (VANET). MEC could process regional vehicles information and generate real-time road hazard features, which could be used to realize trajectory planning progress of vehicles. In this paper, an MEC-oriented VANET infrastructure is presented, and a road hazard feature-based trajectory planning method is proposed. Back Propagation (BP) neural network is employed to predict road hazard feature changing, while a hazard-based cost function is defined. Then, an improved Rapidly Exploring Random Tree (RRT) algorithm is proposed for novel regional trajectory planning. A joint simulation is done based on SUMO and NS3 platforms. Simulation results verify the effectiveness and stability of the proposed algorithm.
- 26 April 2021
- Published by Wiley in Mobile Information Systems
- Vol. 2021, 1-13
Open AccessAn MEC Architecture-Oriented Improved RRT Algorithm for Regional Trajectory Planning
Multi-access Edge Computing (MEC), which could provide real-time computing ability, is considered as an effective approach to improve performance of Vehicular Ad Hoc Network (VANET). MEC could process regional vehicles information and generate real-time road hazard features, which could be used to realize trajectory planning progress of vehicles. In this paper, an MEC-oriented VANET infrastructure is presented, and a road hazard feature-based trajectory planning method is proposed. Back Propagation (BP) neural network is employed to predict road hazard feature changing, while a hazard-based cost function is defined. Then, an improved Rapidly Exploring Random Tree (RRT) algorithm is proposed for novel regional trajectory planning. A joint simulation is done based on SUMO and NS3 platforms. Simulation results verify the effectiveness and stability of the proposed algorithm.