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

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

Morriea-Matias, Luis (NEC Laboratories Europe)
Cats, O. (TU Delft Transport and Planning)
Gama, Joao (LIAAD-INESC TEC; Universidade do Porto)
Mendes-Moreira, Joao (Universidade do Porto; LIAAD-INESC TEC)
Freire de Sousa, Jorge (UGEI-INESC TEC; Universidade do Porto)

2016

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.

Online learning
Bus Bunching
Stochastic Gradient Descent
Travel Time Prediction

http://resolver.tudelft.nl/uuid:a1b7fb4a-4970-4620-9008-46c05f79cd2d

https://doi.org/10.1016/j.asoc.2016.06.031

2019-02-01

1568-4946

Applied Soft Computing, 47, 460–482

Institutional Repository

journal article

© 2016 Luis Morriea-Matias, O. Cats, Joao Gama, Joao Mendes-Moreira, Jorge Freire de Sousa