Yinong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yinong Liu has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Benxiao Lou
Central South University
1 shared publication - Dan Zhou
School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China
1 shared publication - Guobin Gu
Nanning University
1 shared publication - Huan Xiong
Guangxi Key Laboratory of Intelligent Transportation System (ITS), Guilin University of Electronic Technology, Guilin 541004, China
1 shared publication - Jianqiu Chen
Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation, Nanning University, Nanning 530200, China
1 shared publication - Tao Wang
Guilin University of Electronic Technology
1 shared publication
Latest Publications
- Open Access
- 2 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (17) , 7756
Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.
- 2 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (17) , 7756
Open AccessRoad Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.