https://tede.vgtu.lt/index.php/Transport <p style="text-align: justify;">The journal TRANSPORT publishes articles in the fields of: transport policy; fundamentals of the transport system; technology for carrying passengers and freight using road, railway, inland waterways, sea and air transport; technology for multimodal transportation and logistics; loading technology; roads, railways; airports, ports; traffic safety and environment protection; design, manufacture and exploitation of motor vehicles; pipeline transport; transport energetics; fuels, lubricants and maintenance materials; teamwork of customs and transport; transport information technologies; transport economics and management; transport standards; transport educology and history, etc.<br><a href="https://journals.vilniustech.lt/index.php/Transport/about">More information ...</a></p> Vilnius Gediminas Technical University en-US Transport 1648-4142 <p>Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University.</p> <p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</p> https://tede.vgtu.lt/index.php/Transport/article/view/20538 <p>Short-term metro passenger flow prediction plays a great role in traffic planning and management, and it is an important prerequisite for achieving intelligent transportation. So, a novel hybrid Support Vector Regression (SVR) model based on Twice Clustering (TC) is proposed for short-term metro passenger flow prediction. The training sets and test sets are generated by TC with respect to values of passenger flow in different time periods to improve the prediction accuracy. Furthermore, each obtained cluster is decomposed by using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and the Ensemble Empirical Mode Decomposition (EEMD) algorithm, respectively. The volatility of each component obtained after decomposition is further reduced. Then, the SVR model optimized by the Grey Wolf Optimization (GWO) algorithm is used to predict the decomposed components. Moreover, forecast based on one-month data from Xi’an Metro Line 2 Library Station (China). By comparing the prediction results of the TC condition, the Once Clustering (OC) condition and the non-clustering condition, it shows that the TC approach can adequately model the volatility and effectively improve the prediction accuracy. At the same time, experimental results show that the novel hybrid TC–CEEMDAN–GWO–SVR model has superior performance than Genetic Algorithm (GA) optimized SVR (SVR–GA) model and hybrid Back Propagation Neural Network (BPNN) model.</p> Sheng Wang Xinfeng Yang Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0 2024-11-21 2024-11-21 39 3 209–228 209–228 10.3846/transport.2024.20538