Many metropolitan cities motivate people to exploit public bike-sharing programs as
alternative transportation for many reasons. Due to its’ popularity, multiple types of research
on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level,
station-level, or user-level to predict the public bike demand. Previously, the research on the
public bike demand prediction primarily focused on discovering a relationship with weather
as an external factor that possibly impacted the bike usage or analyzing the bike user trend
in one aspect. This work hypothesizes two external factors that are likely to affect public
bike demand: weather and air pollution. This study uses a public bike data set, daily
temperature, precipitation data, and air condition data to discover the trend of bike usage
using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random
Forest. After conducting the research, each algorithm’s output is evaluated with performance
comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an
efficient classifier for the bike demand prediction by weather and precipitation, and Decision
Tree performs best for the bike demand prediction by air pollutants. Also, the three class
labelings in the daily bike demand has high specificity, and is easy to trace the trend of the
public bike system.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13353482 |
| Date | 15 December 2020 |
| Creators | Min Namgung (9380318) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Performance_Comparison_of_Public_Bike_Demand_Predictions_The_Impact_of_Weather_and_Air_Pollution/13353482 |
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