Bus Arrival Time Prediction Using a Modified Amalgamation of Fuzzy Clustering and Neural Network on Spatio-Temporal Data

  • Sonia KhetarpaulEmail author
  • S. K. Gupta
  • Shikhar Malhotra
  • L. Venkata Subramaniam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)


This paper presents a dynamic model that can provide prediction for the estimated arrival time of a bus at a given bus stop using Global Positioning System (GPS) data. The proposed model is a hybrid intelligent system combining Fuzzy Logic and Neural Networks. While Neural Networks are good at recognizing patterns and predicting, they are not good at explaining how they decide their input parameters. Fuzzy Logic systems, on the other hand, can reason with imprecise information, but require linguistic rules to explain their fuzzy outputs. Thus combining both helps in countering each other’s limitations and a reliable and effective prediction system can be developed. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions. The accuracy of result is 86.293% obtained


Spatio-temporal data GPS data Exponential smoothing Data clustering Neural networks 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sonia Khetarpaul
    • 1
    Email author
  • S. K. Gupta
    • 1
  • Shikhar Malhotra
    • 2
  • L. Venkata Subramaniam
    • 3
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyDelhiIndia
  2. 2.Department of Computer Science and EngineeringThapar UniversityPatialaIndia
  3. 3.IBM Research LaboratoryDelhiIndia

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