A rule-based model for Seoul Bike sharing demand prediction using weather data

• Published in: European journal of remote sensing Vol. 53; no. sup1; pp. 166 - 183
• Main Authors: V E, Sathishkumar, Cho, Yongyun
• Format: Journal Article
• Language: English
• Published: Cagiari Taylor & Francis 22.06.2020; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Coefficient of variation; Data Mining; Datasets; Demand; GIS; intelligent transport system; Meteorological data; Prediction models; Predictions; predive analytics; Regression analysis; Regression models; Scientific papers; Statistical analysis; Statistical models; Sustainability; Weather
• ISSN: 2279-7254; 2279-7254
• DOI: 10.1080/22797254.2020.1725789

This research paper presents a rule-based regression predictive model for bike sharing demand prediction. In recent days, Pubic rental bike sharing is becoming popular because of is increased comfortableness and environmental sustainability. Data used include Seoul Bike and Capital Bikeshare program data. Both data have weather data associated with it for each hour. For both the dataset, five statistical models were trained with optimized hyperparameters using a repeated cross validation approach and testing set is used for evaluation: (a) CUBIST (b) Regularized Random Forest (c) Classification and Regression Trees (d) K Nearest Neighbour (e) Conditional Inference Tree. Multiple evaluation indices such as R 2 , Root Mean Squared Error, Mean Absolute Error and Coefficient of Variation were used to measure the prediction performance of the regression models. The results show that the rule-based model CUBIST was able to explain about 95 and 89% of the Variance (R 2 ) in the testing set of Seoul Bike data and Capital Bikeshare program data respectively. An analysis with variable importance was carried to analyse the most significant variables for all the models developed with the two datasets considered. The variable importance results have shown that Temperature and Hour of the day are the most influential variables in the hourly rental bike demand prediction.