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- W3208654549 abstract "Many metropolitan cities motivate people to exploit public bike-sharing programs asalternative transportation for many reasons. Due to its’ popularity, multiple types of researchon 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 thepublic bike demand prediction primarily focused on discovering a relationship with weatheras an external factor that possibly impacted the bike usage or analyzing the bike user trendin one aspect. This work hypothesizes two external factors that are likely to affect publicbike demand: weather and air pollution. This study uses a public bike data set, dailytemperature, precipitation data, and air condition data to discover the trend of bike usageusing multiple machine learning techniques such as Decision Tree, Naive Bayes, and RandomForest. After conducting the research, each algorithm’s output is evaluated with performancecomparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is anefficient classifier for the bike demand prediction by weather and precipitation, and DecisionTree performs best for the bike demand prediction by air pollutants. Also, the three classlabelings in the daily bike demand has high specificity, and is easy to trace the trend of thepublic bike system." @default.
- W3208654549 created "2021-11-08" @default.
- W3208654549 creator A5068071112 @default.
- W3208654549 date "2020-12-15" @default.
- W3208654549 modified "2023-09-23" @default.
- W3208654549 title "Performance Comparison of Public Bike Demand Predictions: The Impact of Weather and Air Pollution" @default.
- W3208654549 doi "https://doi.org/10.25394/pgs.13353482.v1" @default.
- W3208654549 hasPublicationYear "2020" @default.
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