Co-prediction of multiple transportation demands in Ro-Ro terminal based on dates fine-grained temporal fusion transformer
Authors:
- Yukuan WangYWYukuan Wang
- Ryan Wen LiuRLRyan Wen Liu
- Jingxian LiuJLJingxian Liu
- Lichao YangLYLichao Yang
- Yang LiuYLYang Liu
- Ping HePHPing He
- 5 July 2025
- journal article
- Published by Elsevier in Computers & Industrial Engineering
- Vol. 208
Abstract
No abstract availableFunding Information
- National Key Research and Development Program of China (2022YFC3302702)
- National Natural Science Foundation of China (52471383)
- China Scholarship Council (202406950116)
This publication has 37 references indexed in Scilit:
- A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospectsAdvanced Engineering Informatics, 2024
- Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC dataApplied Energy, 2024
- Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State NetworkComputers and Electrical Engineering, 2024
- On time demand traffic estimation based on DBN with Horse herd optimization for next generation wireless networkExpert Systems with Applications, 2024
- Data clustering: application and trendsArtificial Intelligence Review, 2022
- A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networksExpert Systems with Applications, 2022
- An effective spatiotemporal deep learning framework model for short-term passenger flow predictionSoft Computing, 2022
- Passenger flow prediction in bus transportation system using deep learningMultimedia Tools and Applications, 2022
- Passenger demand forecasting in scheduled transportationEuropean Journal of Operational Research, 2020
- Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approachExpert Systems with Applications, 2020