In this paper, we propose a travel time estimation and prediction (TTEP) framework to enhance the driving efficiency on highways through the Internet of Vehicles (IoV). Highway travel time estimation and prediction are important for the drivers in a long-distance traveling. The accurate travel time information on highways is the key to improve the efficiency of transportation systems. When current flow status is collected through the IoV, TTEP can accurately estimate and predict highway travel time by the proposed weighted root-mean-square similarity (Weighted-RMSS) method. In addition, when current flow status is unavailable at the present time, we propose the multiple slope-based linear regression (Multi-SBLR) method to predict highway travel time only using historical traffic data. Furthermore, the spatiotemporal mobilities of vehicles on highways are analyzed and explored to improve the prediction accuracy of the proposed Weighted-RMSS and Multi-SBLR methods. To verify the feasibility and superiority of TTEP, we adopt the open Electronic Toll Collection data of highways in Taiwan to evaluate the prediction accuracy of our approaches. Experimental results show that our approaches outperform existing methods and can significantly reduce the prediction errors of highway travel time. In particular, we further implement the Android-based and web-based systems of TTEP to predict and compare travel time at different departure times and locations for highway drivers.