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Analyzing travel behavior in transportation networks within a city is significant to understand the user’s activity and travel pattern in relation to making improved city plans for the future. Unlike the traditional travel diary survey, GPS data have helped researchers to analyze Big Data with enriched travel information in an automated way. The focus of this research was to identify user activity and travel pattern from GPS data logs. We proposed three different approaches, including Geohash clustering, the GIS-based approach, and Combined Geohash–GIS approach, for automatic user activity and trip recognition in a continuous and aggregate manner. We developed different individual models considering different dwell times for the above three approaches. We considered three different testing scenarios based on specified tolerance levels, including simple, moderate, and critical testing to identify trip only, activity only, and sequential activity–trip analysis. In comparison with other approaches, the Combined Geohash–GIS approach considering 5 min dwell time accurately classified data with about 95% accuracy. The proposed Combined Geohash–GIS approach could significantly enhance the efficiency and accuracy of GPS travel surveys by correctly recognizing user activity and trip patterns. This proposed combined approach could serve as a foundation for a future model system of full-scale travel information identification with GPS data.