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

Abstract

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.

Changes in Urban Mobility Related to the Public Bike System with Regard to Weather Conditions and Statutory Retail Restrictions

“…in 2018 when compared to 2017). The higher average usage of the ŁPB bikes on working days is concordant with observations made in other cities [65]. Negligible year-on-year changes in time distribution of trip percentages for weekdays prove that Łódź residents have consistent transport behaviour in the working week and reveal slightly bigger differences during weekends for the analysed time aggregation.…”

RETRACTED ARTICLE: Forecasting reference evapotranspiration using data mining and limited climatic data

“…ANN application in hydrology due to its high nonlinear functional characteristic has provided rapidly many advantages in river flows extrapolation Cigizoglu (2003), rainfallrunoff modeling Firat (2008), sediment forecasting Wang et al (2008), and ET-ref modeling Kumar et al (2002). Researchers have obtained outstanding results by using different algorithms of ANN to model the reference evapotranspiration as a function of climatic data Trajkovic et al (2003), Keskin and Terzi (2006), Parasuraman et al (2007), Doğan (2009), Sathishkumar and Cho (2020), Sudheer et al (2003), and Zanetti et al (2007) in their reference evapotranspiration estimation, simplified the ANN inputs data to air temperature, extraterrestrial solar radiation and daily light hours. Recently, Khoob (2008) and Landeras et al (2008) used similar data set without the daily light to estimate successfully the reference evapotranspiration.…”

RETRACTED ARTICLE: Application of active remote sensing in confirmation rights and identification of mortgage supply-demand subjects of rural land in Guangdong Province

“…In this paper, Fuzzy Systematic Evaluation System (FSES) has been proposed to analyze the affecting variables residential mortgage demand and other restrictions on households' use of a mortgage in Guangdong Province using active remote sensing (Muhammed Shafi et al, 2018;Sathishkumar & Cho, 2020). The Guangdong province is detected and monitored to classify the high and low demand for a mortgage product, which utilizes the multi-resolution analysis process while examining the image frequencies of the various province of Guangdong.…”