An Online Learning Approach to Eliminate Bus Bunching in Real-time

Luis Morriea-Matias , Oded Cats, Joao Gama, Joao Mendes-Moreira, Jorge Freire de Sousa

Research output: Contribution to journalArticleScientificpeer-review

29 Citations (Scopus)
14 Downloads (Pure)


Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable information from raw location-based data. State-of-the-art tools and methodologies such as Regression Analysis, Probabilistic Reasoning and Perceptron's learning with Stochastic Gradient Descent constitute building blocks of this predictive methodology. The prediction's output is then used to select and deploy a corrective action to automatically prevent Bus Bunching. The performance of the proposed method is evaluated using data collected from 18 bus routes in Porto, Portugal over a period of one year. Simulation results demonstrate that the proposed method can potentially reduce bunching by 68% and decrease average passenger waiting times by 4.5%, without prolonging in-vehicle times. The proposed system could be embedded in a decision support system to improve control room operations.
Original languageEnglish
Pages (from-to)460–482
Number of pages23
JournalApplied Soft Computing
Publication statusPublished - 2016


  • Online learning
  • Bus Bunching
  • Stochastic Gradient Descent
  • Travel Time Prediction

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