Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks

Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks

This study presents an innovative approach to hourly demand forecasting for bike-sharing systems using a multi-attribute, edge-weighted, Gated Graph Convolutional Network (GGCN). It addresses the challenge of imbalanced bike borrowing and returning demands across stations, aiming to enhance station...

Full description

Saved in:
Bibliographic Details
Main Authors: Hebin Guo, Kexin Li, Yutong Rou
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Gated graph convolutional neural network
traveler features
multi-feature edge weight
demand prediction
Online Access:https://ieeexplore.ieee.org/document/10756689/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10756689/

Similar Items