Contemporary mobile devices integrate navigation, photography, communication, and information retrieval capabilities that fundamentally support travel activities. Despite these technological advances, existing travel applications predominantly depend on manual user input, resulting in incomplete trip documentation and diminished memory retention. This work introduces an intelligent mobile system that autonomously captures travel-related data through smartphone sensor integration, contextual analysis, and cloud infrastructure. The proposed framework records movement patterns, recognizes significant locations, delineates journey segments, correlates multimedia content with spatial-temporal contexts, and produces comprehensive trip narratives without requiring explicit user actions. Built upon a modular multi-tier architecture, the implementation ensures measurement precision, power efficiency, and data protection. Field deployment demonstrates robust trip identification, accurate media linkage, and reasonable energy consumption, validating that automated travel documentation significantly improves personal journey recording.
Kusuma et al. (Sat,) studied this question.