Predicting the future location of mobile objects has become an important and challenging problem. With the widespread use of mobile devices, applications of location prediction include location-based services, resource allocation, handoff management in cellular networks, animal migration research, and weather forecasting. Most current techniques try to predict the next location of moving objects such as vehicles, people or animals, based on their movement history alone. However, ignoring the dynamic nature of mobile behavior may yield inaccurate predictions, at least part of the time. Analyzing movement in its context and choosing the best movement pattern by the current situation, can reduce some of the errors and improve prediction accuracy. In this chapter, we present a context-aware location prediction algorithm that utilizes various types of context information to predict future location of vehicles. We use five contextual features related to either the object environment or its current movement data: current location; object velocity; day of the week; weather conditions; and traffic congestion in the area. Our algorithm incorporates these context features into its trajectory-clustering phase as well as in its location prediction phase. We evaluate the proposed algorithm using two real-world GPS trajectory datasets. The experimental results demonstrate that the context-aware approach can significantly improve the accuracy of location predictions.