Context-Aware Travel Time Prediction and Route Optimization Using Heterogeneous Traffic and Event Data: A Comprehensive Survey

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions.