Loading...
Loading...

Go to the content (press return)

A deep learning approach for estimating inventory rebalancing demand in bicycle sharing systems

Author
Mrazovic, P.; Larriba, J.; Matskin, Mihhail
Type of activity
Presentation of work at congresses
Name of edition
42nd IEEE International Conference on Computers, Software, and Applications
Date of publication
2018
Presentation's date
2018-07-23
Book of congress proceedings
2018 IEEE 42nd Annual Computer Software and Applications Conference: 23-27 July 2018, Tokyo, Japan: proceedings
First page
110
Last page
115
DOI
10.1109/COMPSAC.2018.10213
URL
https://ieeexplore.ieee.org/document/8377840 Open in new window
Abstract
Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent ...
Keywords
Bicycles, Deep learning, Demand prediction, Forecasting, Learning approach, Learning models, MIMO systems, Multi input multi output, Public bicycle sharing systems, Service disruptions, Short term memory, Smart cities, Smart city, Sporting goods, Time series forecasting, Time series forecasting Application programs
Group of research
DAMA-UPC - Data Management Group

Participants

  • Mrazovic, Petar  (author and speaker )
  • Larriba Pey, Josep  (author and speaker )
  • Matskin, Mihhail  (author and speaker )