Azin Ghasemi, Loubna Ali
Recent years the appearance of city transportation has changed one of the most important parts of this change, is bike sharing systems. This system has activity in most cities. This system generates massive amount of operational data. one of the most significant challenges is imbalance of bikes availability in station in peek-time. However, most of the research are focused on increasing the accuracy of the forecasting demands or the change of spatial temporal slightly changes. Integration historical data analysis, live data and short-term forecasting in operational framework Less attention have been received. In this research the station data of Berlin Next bike with 5 min interval are gathered and the store in MySQL database. after that design a integrate framework for explore on historical data and demonstrate live data and short-term forecasting based on regression model with Tableau. the analysis of the result shows the demand problem at stations depends more on imbalance on temporal and then on weather conditions. The suggested framework is demonstrating Integration of descriptive analysis and prediction in a unit environment could help operational decision making and advance planning bike sharing systems.