IoT Based Bus Arrival Time Prediction Using Artificial Neural Network (ANN) for Smart Public Transport System (SPTS)

: Advancement of the public transport system is important to modern society for reliable performance. Intelligent public transport system can utilize the time very effectively to give better performance to the society. Fast advancement in equipment, programming, and correspondence innovations has encouraged the rise of Internet-associated devices that give perceptions and information gathering from modern reality. By interfacing, an internet-enabled device with the public transport system leads to the intelligent public transport system. This paper proposed intelligent public transport with IoT enabled system to give an accurate prediction to the arrival time of the bus to the particular bus stops. Here, Artificial Neural Network (ANN) is used as a prediction algorithm and ANN is trained with different traffic parameters and environmental conditions. Parameters which are considered in the proposed system includes Distance(D), Waiting Time at Stops (WTS), Red signal Duration at Traffic Signal (RSD), Traffic Density (TD), Turning Density (TRD), Rush hours (RH), Weekends (WE), Weather conditions (WC), Number of passengers in the bus (NP), Public Holiday (PH), Road Type (RT). These parameters and the measured real-time arrival time of the bus in different stops for 10 days is used for training the ANN. This trained ANN is implemented on the server-side. In this paper, the performance of the proposed IOT-ANN-SPTS system is compared with the existing previous related works. From the performance analysis, it is shown that the proposed system produced less error (MAE=134.582, RMSE=197.738, MAPE=28.87) when compared to the other methods.

• Publication year

  2020

• Venue

  International Journal of Intelligent Engineering and Systems

• Publication date

  2020-02-29

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.22266/ijies2020.0229.29
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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