Cruising for parking remains a localized but consequential management problem because it adds avoidable delay, concentrates congestion around high-demand destinations, and weakens the operational performance of curbside systems. This paper presents a management- and planning-oriented synthesis of a validated GPS-based parking-search identification framework and evaluates what that established measurement approach contributes to operational decision-making using three linked empirical components: a labeled ground-truth corpus of 3,550 journeys, an external dynamic park-and-visit dataset of 161 journeys, and a filtered large-scale archive of 868,561 consumer light-vehicle trips ending in Frankfurt. The results show that the predictive model achieves a mean absolute error below one minute across sampling rates from 1 to 15 seconds, reduces prediction error materially relative to a constant-duration baseline, and remains robust when transferred to an external dataset collected with a different application. When applied at city scale, the model indicates that 33% of trips end with immediate parking, while the overall mean parking-search duration is 1 minute 30 seconds and rises to 2 minutes 15 seconds among trips with non-zero search. The evidence also shows a substantial reduction in vehicle speed near parking-search activity and identifies central districts with systematically elevated mean search duration. Taken together, the results show that GPS-derived parking-search intelligence can be translated into credible, decision-ready evidence for targeted curb regulation, parking guidance, infrastructure prioritization, and more realistic travel-time planning.