BLE RSSI-Based Detection of Freight Wagon Passages at Railway Control Points

Accurate per-wagon occupancy accounting at freight stations — knowing which wagon entered or exited which track and when — is a prerequisite for automated shunting management, yet existing technologies — axle counters, RFID, computer vision, and LPWAN IoT — each provide only a subset of the required information and depend on dedicated infrastructure or favourable conditions. This paper investigates whether two fixed BLE gateways, combined with Eddystone-TLM beacon nodes proposed for mounting on freight wagon bodies, can classify passage direction from RSSI signals without training data, site-specific calibration, or track modification. The enabling mechanism is wagon-body attenuation: as a wagon passes between the receivers, its metallic body creates a temporal asymmetry in the RSSI envelopes that encodes travel direction. We present a five-stage online pipeline at O (1) memory per packet: a two-sided CUSUM detector with adaptive per-event baseline estimation segments the RSSI stream; a three-stage validation filter rejects partial passes, lateral paths, and near-gateway reversals; and direction is classified by the normalized Temporal Centroid shift — a speed-invariant feature requiring no training data — with a cascade fallback for ambiguous short windows. Combined with the beacon MAC address as a wagon identifier, the system generates structured occupancy events directly consumable by station management systems. Validated on 151 labelled events across eight scenario categories at Urtaul freight station and the TSTU test polygon, the pipeline achieves 96.7% accuracy (95% Wilson CI: [92.5%, 98.6%]) zero wrong-direction predictions across all 84 directional events (exact Clopper-Pearson 95% CI for the wrong-direction rate: [0%, 3.5%])", a Random Forest baseline on the same features confirms supervised learning adds no measurable benefit over the training-free approach within this feature space.