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Figure 3: Fare collection system model. Advertising mode data frame Signal Strength Indication. The composed of 5 smaller fields. After passenger confirmation, smartphone keeps scanning periodically during the trip to check if the beacon still nearby. Finally, trip itinerary field which indicates RSSI Received Signal Strength will be sent to billing server and fare calculated price Indication , it can be used to estimate distance sent back to smartphone to start payment procedure.
As passengers nowadays are likely to carry smartphones with BLE function and BLE beacons were attached to the vehicle it is possible to check when a person took and took off the vehicle. The system basic configuration is the following one:. Figure 4: The first step of the test was to measure The main features are its ability to run 35 km distance RSSI value while smartphone was inside of the with a single charge, complete curves as sharp as 25 vehicle, and the second step was to measure RSSI m radius and, inside the tram, the indoor noise level is value when smartphone was outside the vehicle 1 m below 70dB.
Because of its low floor property it away. Tests elements were: Test people.
The tram consists of 5 cars and in each end of configuration pictures can be seen below: Figure 6: Although paper results were obtained for this tram specifically, similar approach and ideas of this project can be used in other transportation systems, such as: For a certain distance present. If we 5.
Therefore, to improve our measurements results, Kalman filter was used. Kalman as a regression filter that tracks the 6. Conclusion condition of linear and non-linear states that contain noise. The filter predict and revise states of value past, First, in this paper it was introduced how present and future.
It is useful to predict and track automatic fare collection system works and its about model system . Next, it was explained how Bluetooth The prediction phase is expected when user enters Low Energy technology works including BLE the input for anticipated state in the previous time beacons and how they can used with automatic fare period.
The compensation phase is stage for collection system. Later, it was explained how to estimating the exact state of the process based on detect IN and OUT situations and improved results processed estimation state and real state. The were obtained by applying Kalman Filter.
This project is interesting as it concerns two nowadays IT important areas: Internet of Things and Machine Learning. Robert, T.
Kevin, W. Chris, C.