Improving Demand Prediction in Bike Sharing System by Learning Global Features

author: Ming Zeng, Carnegie Mellon University
published: Oct. 12, 2016,   recorded: August 2016,   views: 1585
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

A bike sharing system deploys bicycles at many open docking stations and makes them available to the public for shared use. These bikes can be checked-in and checked-out at any of the docking stations. Predicting daily visits is important for service providers to optimize bike allocation and station maintenance. In this paper, we formulate this prediction problem as a regression task. Through data analysis, we develop several features that are very helpful in predictions. Moreover, we demonstrate that there are significant differences among the patterns of visits at different stations. To improve prediction accuracy, we propose station-centric augmented with global feature transformation. The gradient boosting decision tree (GBDT) and neural network (NN) techniques are leveraged to extract global features. The experimental results demonstrate that the proposed model offers better prediction performance compared to two baseline approaches.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: