In recent times, travel sequence planning and recommendation systems that take touring properties into consideration have a significant role in aiding tourists for defining the Point-of-Interests (POIs) they will visit. Although several works have focused on suggesting lists or routes of POIs, they do not address POIs with dynamic spatial and temporal properties (i.e., live events) that may attract the tourist's attention. In this thesis, we present a system aiming to generate dynamic and personalized travel routes at a given destination based on tourist's preferences and dynamic events that may attract a large number of tourists by using data collected from Location-Based Social Networks (LBSNs) such as Twitter. To achieve this, features were extracted from the collected data (posts) to filter them as tourism related posts and unrelated posts. Thereafter, the tourism related posts were clustered based on location to detect the tourism-related events, then marking unusual temporal slices that may contain potential events and meet tourists' preferences and interests. Furthermore, our system classifies the tourism-related events into several subtopics that fall under the tourism and travel industry, such as festivals, concerts, and sport tournaments. The classified events are merged with the existing POIs at the destination in order to have a real POIs list to provide the tourist with a rich list of attractions that match his/her preferences. Finally, our system computes the similarity between the properties of each attraction (event/POI) and the tourist's interests in order to generate a travel route of the top ranked attractions (events/POIs), these attractions were visualized on a dynamic map. In our experiments, we select Dubai city in United Arab Emirates (UAE) as an example to build a real POIs list which contains 225 POIs and build a list of events based on dataset collected from Twitter and finally generate travel routes of a combination of the detected events and the existing POIs.