This study adopts chronic data of public bike systems regarding the numbers of available bikes and spaces of each station. Six operational indicators were developed in light of risk management, including the risk of insufficient bikes, risk of insufficient spaces, reliability, fluctuation, waiting time, and utilization rate. We collected Taipei's ＂YouBike＂ data with a time span of 6 months for every 5 min., or a data size of 8,080,128. The findings include: (1) identification of jive types of spatial hotspots and three types of temporal hot periods; (2) clarification of the operational characteristics on weekdays and weekends, upon which differential pricing and backup mechanism were based; (3) proposal of geographic factors that would affect the status of bike stations - a complement to the current criteria for building new stations; and (4) quantification of the joint effect of adjacent stations to ease hotspots along with an approach to active bike dispatch. This study demonstrated multiple applications given very limited data types. The authorities can use the model for better operations management. It is suggested that more open data to be released for innovative shared transport.