Title
Unsupervised Learning for Public Transport Delay Pattern Analysis
Author
Cheng, Y. (TU Delft Transport and Planning)
Krishnakumari, P.K. (TU Delft Transport and Planning) 
Department
Transport and Planning
Date
2024
Abstract
To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily punctuality information from the automatic vehicle location data for a singular public transport route. This information is then translated into a visual representation through aggregated daily delay distribution profile images, offering insights into the spatial and temporal distribution of delays. The delay distribution finds expression in the arrangement of pixels within these profile images. The essence of these images is further distilled through image pattern recognition using the neural network architecture of ResNet50. Employing the k-means algorithm, we cluster these images based on their similarity, revealing five distinct daily delay patterns. The analysis of these patterns offers insight into their unique characteristics, yielding noteworthy outcomes. These findings hold the potential to provide public transport operators with an enriched comprehension of the dynamics of delays occurring on a specific line.
Subject
analytic data visualization
automatic vehicle location
data and data science
performance measures
public transportation
quality
visualization in transportation
To reference this document use:
http://resolver.tudelft.nl/uuid:047fa48d-de15-43ef-b96c-31291d989b42
DOI
https://doi.org/10.1177/03611981231215333
Embargo date
2024-07-04
ISSN
0361-1981
Source
Transportation Research Record
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 Y. Cheng, P.K. Krishnakumari