Performance Analysis of a Real-Time Cloud Based Bus Tracking System with Adaptive Prediction

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   05.12.2018  5 Comments

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A PHP file dbconnect. With advances in the field of ICT and Internet of Things IoT , it has now become a common practice for several urban transportation operators to use to use location reporting systems such as GPS devices on-board their fleet, with the primary purpose of monitoring and managing their fleet as well as providing arrival time information to bus users [17]. The adaptive algorithm used in this work is based on the RMSE criterion.

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A Neural Network first learns from samples and captures functional relationships among the data. It is observed that the best performance is obtained with the adaptive algorithm since it is closest to the actual reading. The squared error formula is used to compute the error deviation.

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5 thoughts on “Xxjjxx

  1. Moreover, despite the well established bus network in Mauritius, passenger information systems are yet to be deployed. In the destination frame, a command button is inserted which retrieves the end user s destination address from the user request table.

  2. The function of the mobile application is to transmit the user GPS coordinates to the cloud server and display the predicted arrival time updated by the control station. The Google map is queried and returns a response calculated distance to the cloud server.

  3. A detailed performance comparison between the five algorithms and the adaptive one has also been made on two different bus routes for ten week days. The output of prediction algorithm is then used as input to the neural network to obtain a predicted arrival time.

  4. Table 4 gives the average MSE of the eight algorithms over the whole journey from the source to the destination for route 1. The end users devices contain an android application to query for the arrival times.

  5. If the connection fails, the application will display the reason behind the failed connection.

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