Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models
Volume: 22, Issue: 8, Pages: 4813 - 4824
Published: Aug 1, 2021
Abstract
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure...
Paper Details
Title
Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models
Published Date
Aug 1, 2021
Volume
22
Issue
8
Pages
4813 - 4824
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History