Bike-sharing Demand Prediction based on Artificial Intelligence Algorithm Using Weather Data

To alleviate traffic congestion and environmental pollution in heavily populated Seoul, South Korea, the city has actively introduced a bike-sharing service. As the demand for this service increases, efficient supply and management of shared bikes are becoming critically important. Because bike-shar...

Full description

Saved in:
Bibliographic Details
Main Authors: Lee, Yebeen, Son, Hyungju, Ahn, Jiin, Cho, Seokheon
Format: Conference Proceeding
Language:English
Subjects:
and bike-sharing service
artificial intelligence algorithm
Bicycles
bike-sharing demand prediction
Data models
machine learning algorithm
Meteorology
Prediction algorithms
Predictive models
Random forests
Time measurement
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To alleviate traffic congestion and environmental pollution in heavily populated Seoul, South Korea, the city has actively introduced a bike-sharing service. As the demand for this service increases, efficient supply and management of shared bikes are becoming critically important. Because bike-sharing demand correlates with the weather, we performed a prediction for bike demand based on artificial intelligence algorithms utilizing weather data. The Multiple Linear Regression, Random Forest Regression, and Multi-Layer Perceptron artificial intelligence algorithms were considered and applied to processed weather data collected in Seoul using various methods. The results revealed that the best performance in predicting bike-sharing demand was achieved by a model trained by the Random Forest Regression algorithm, which considers historical weather and demand data and separates itself into two sub-datasets by weekdays and weekends, but without distinguishing itself with the information of the four seasons.
ISSN:2158-4001
DOI:10.1109/ICCE59016.2024.10444462