Inter-Block Flow Prediction with Relation Graph Network for Cold-start on Bike-Sharing System

2022 ◽  
pp. 1-1
Author(s):  
Mingda Jiang ◽  
Chao Li ◽  
Kehan Li ◽  
Zidong Yang ◽  
Hao Liu
Keyword(s):  
Cold Start ◽  
Flow Prediction ◽  
Bike Sharing ◽  
Relation Graph

scholarly journals Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

2020 ◽  
pp. 1-1
Author(s):  
Jingjing Gu ◽  
Qiang Zhou ◽  
Jingyuan Yang ◽  
Yanchi Liu ◽  
Fuzhen Zhuang ◽  
...  
Keyword(s):  
Flow Prediction ◽  
Bike Sharing

A Multi-graph Convolutional Network Framework for Tourist Flow Prediction

2021 ◽  
Vol 21 (4) ◽  
pp. 1-13
Author(s):  
Wei Wang ◽  
Junyang Chen ◽  
Yushu Zhang ◽  
Zhiguo Gong ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Data Science ◽  
Service Providers ◽  
Tourism Industry ◽  
Convolutional Network ◽  
Flow Prediction ◽  
Challenges And Opportunities ◽  
Real World Datasets ◽  
Relation Graph ◽  
Smart Tourism ◽  
Tourist Flow

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.

EVALUATING AND TESTING BIOFUELS TO MEET COLD START AND ALTITUDE RELIGHT REQUIREMENTS

2014 ◽  
Vol 13 (4) ◽  
pp. 319-337
Author(s):  
Joel Jean ◽  
Alain Fossi ◽  
Alain deChamplain ◽  
Bernard Paquet
Keyword(s):  
Cold Start
Sign in / Sign up

Export Citation Format

Share Document