Prediction of Bus Arrival Time Using Intelligent Computing Methods

  • Aditya KhampariaEmail author
  • Rubina Choudhary


Bus transportation plays a vital role in recent society. If the arrival time of buses at their respective destinations is accurate, the usage of private vehicles, fuel consumption, and traffic congestion can be reduced. In today era, most of the industries, universities, colleges, etc., provide the facility of transportation to their employees, staff, and students to pick up and drop from the prescheduled stoppages. Objective of this research is to exploit the artificial neural network (ANN) model techniques on the collected historical data using GPS. In this work, artificial neural network (ANN) and radial basis function (RBF) have been applied to collected data through GPS. In this work, the model is evaluated against standard feed-forward back-propagation algorithm (BPA) and radial basis function (RBF), which is used for prediction of bus arrival/departure time using five-month historical data of Lovely Professional University, Phagwara (Punjab), and reached the conclusion that radial basis function (RBF) is observed as the most intelligent model used in the computing bus arrival time using unpredictable factors as compared to back-propagation algorithm. Real-time prediction of bus arrival time has a number of applications for cargo delivery, transit services, and area of logistics.


Bus arrival time GPS data ANN RBF Real-time prediction 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLovely Professional UniversityJalandharIndia
  2. 2.Department of Quality Assurance (Academics)Lovely Professional UniversityJalandharIndia

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