An online learning approach to eliminate Bus Bunching in real-time

• Type: Article in International Scientific Journal
• Date: 2016-06-24

Authors

Luís Moreira-Matias (Author) Other; Oded Cats (Author) Other; João Gama (Author) FEP; João Mendes-Moreira (Author) FEUP; Jorge Freire de Sousa (Author) FEUP

Journal

Title: Applied Soft Computing Journal

Vol. 47

Pages: 460-482

ISSN: 1568-4946

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 57 Citations

Scopus - 67 Citations

Other information

Authenticus ID: P-00K-KNH

DOI: 10.1016/j.asoc.2016.06.031

Abstract (EN): 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. (C) 2016 Published by Elsevier B.V.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23