Abstract: A fare gate in a transportation system including a barrier, a sensor, and an actuator. The actuator moves the barrier from the open position into the closed position. The sensor is mounted on the barrier and in one position captures sensor data across an aisle formed by the fare gate. A transit user in front of the barrier in another position is identified by the sensor. The transit user passes through the fare gate using the aisle. The actuator moves the barrier from a closed position to an open position to clear passage through the aisle. Using the sensor, the aisle is scanned with a movement of the barrier. The sensor is calibrated based on a predetermined point with movement of the barrier to align the sensor and the predetermined point.

Abstract: A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.

Abstract: A fare gate in a transportation system including a barrier, a sensor, and an actuator. The actuator moves the barrier from the open position into the closed position. The sensor is mounted on the barrier and in one position captures sensor data across an aisle formed by the fare gate. A transit user in front of the barrier in another position is identified by the sensor. The transit user passes through the fare gate using the aisle. The actuator moves the barrier from a closed position to an open position to clear passage through the aisle. Using the sensor, the aisle is scanned with a movement of the barrier. The sensor is calibrated based on a predetermined point with movement of the barrier to align the sensor and the predetermined point.

Abstract: A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.