As the technology in automation and computation advances, traffic data can be easily collected from multiple sources, such as sensors and surveillance cameras. To extract value from the huge volumes of available data requires the capability to process and extract patterns in large datasets. In this paper, a machine learning method embedded within a big data analytics platform is constructed by using random forests method and Apache Hadoop to predict highway travel time based on data collected from highway electronic toll collection in Taiwan. Various prediction models are then developed for highway travel time based on historical and real-time data to provide drivers with estimated and adjusted travel time information.