Travel-time prediction is a very important and foundational function in ATIS. Base on this information, travelers can choose appropriate route to avoid traffic jam. It helps to increase highway capacities and tackle the traffic jams. Most travel time prediction methods rely on an accurate historical database. Inaccurate database usually results in significant impacts for travel time prediction. We proposed a Bayesian based method for generating historical travel time. This method first compares the real time data with those of historical database. If there exists no significant difference, historical data is used. Otherwise, a Bayesian Updating method is invoked to compromise the real time data. We developed three variations of Bayesian Updating method, which are standard deviation weighted, variance weighted, and no Bayesian Updating, respectively. We applied these three models on the highways using real world data. The testing results show that all these three models yield satisfactory results. The standard weighted Bayesian Updating method performed best when the length of road segment is less than 10 km.