Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this thesis, it first proposes a segment-based map matching method to locate GPS trajectories onto the road network, and then extract users’ travel behaviours from their historical routes. Next, users’ travel behaviour frequencies are estimated by using collaborative filtering technique. This thesis defines two types of travel behaviours, appearance behaviour and transition behaviour, from users’ historical GPS trajectories and propose three personalized travel route recommendation methods¬, including CTRR, CTRR + and Map2R, to consider users’ personal travel preferences based on their historical GPS trajectories. A route with the maximum probability of a user’s travel behaviour is then generated. CTRR only considers user’s appearance behaviour and calculates the maximum probability route based on naïve Bayes model. Besides, CTRR is extended to CTRR+ by integrating distance with the user appearance behaviour probability. In MaP2R, it considers both appearance behaviour and transition behaviour, and calculate the maximum probability route based on Markov model. This thesis also conducts some case studies based on a real GPS trajectory dataset from Beijng, China. The experimental results show that the proposed CTRR methods achieve better results for travel route recommendations compared with the shortest distance path method, and both CTRR+ and MaP2R can enhance the performance of CTRR, respectively.