Train Alighting Prediction Using Single-Station Data

The patent divides the prediction system into independent station-level units, where each station predicts its own alighting counts using only local data. This segmentation eliminates the need for complex network-wide data collection while maintaining prediction capability at each individual station.

The patent extracts and utilizes only the essential data elements needed for prediction (passer-by counts, train arrival/departure times, train intervals) from each station, discarding the requirement for comprehensive data from all stations. This extraction approach simplifies the information requirements while preserving core prediction functionality.

The patent extracts only the minimum necessary data elements (passer-by counts, train timing information) that can be obtained from existing single-station infrastructure, eliminating the need for expensive network-wide data collection systems while maintaining sufficient prediction accuracy.

The patent employs simple, low-cost measurement approaches at each station using existing infrastructure rather than investing in expensive, complex network-wide systems. The solution uses readily available data from basic sensors and timing devices already present at individual stations.

The patent implements independent real-time prediction at each station using only locally available data streams, eliminating the need for complex coordinated data collection across multiple stations. Each station operates autonomously in real-time using its own passer-by counts and train timing information.

Each station performs its own real-time prediction using its own local data, making the system self-sufficient at the station level. This eliminates the need for complex inter-station data sharing and coordination, as each station serves its own prediction needs independently.