Airport Parking Demand Estimation via Flight Schedule Matching

The patent introduces an intermediary matching mechanism that connects parking lot data with flight schedules through temporal and spatial relationships. The system uses flight arrival/departure times, airport location, and parking lot characteristics as intermediary variables to infer passenger flow patterns without requiring direct access to passenger manifests or sensitive travel data.

The patent replaces direct mechanical data access (requiring cooperation with airport authorities for passenger data) with a statistical inference system. Instead of directly accessing passenger information, the system uses publicly available flight schedules, historical parking patterns, and machine learning models to predict passenger flow, substituting direct data access with indirect statistical modeling.

The patent segments the prediction system into distinct functional modules: data collection module (aggregating flight schedules and parking data), data preprocessing module (cleaning and normalizing data), matching module (temporal-spatial alignment of parking events with flights), and prediction module (generating forecasts). This segmentation reduces overall system complexity by making each component independent and manageable.

The patent creates a universal data processing framework that handles multiple data types (flight schedules, parking transactions, temporal patterns) through a single integrated architecture. The same core algorithms and data structures serve multiple purposes: predicting passenger flow, optimizing pricing strategies, and analyzing parking patterns, thereby reducing complexity through multi-functionality.

The patent performs preliminary actions by pre-processing and storing historical parking data and flight schedule data in optimized formats before actual prediction needs arise. The system pre-calculates temporal patterns, creates indexed data structures for rapid querying, and prepares feature representations that can be quickly processed during real-time prediction, significantly reducing processing time when actual forecasts are needed.

The patent applies parameter changes by transforming raw historical data into normalized feature parameters suitable for efficient computational processing. The system converts timestamps into time-of-day features, aggregates parking data into hourly/daily patterns, and transforms flight schedules into departure-time intervals. These parameter transformations enable faster processing while maintaining estimation accuracy.