Public Transport Delay Pattern Analysis By Unsupervised Learning Approach

Abstract

To analyze latent multiple specific patterns in the line-based public transport daily delay occurrence, a data-driven explorative analysis of public transport daily delay spatial-temporal distribution pattern is performed based on the k-means clustering algorithm. Firstly, we used aggregated daily delay profile to visualize how the delay is distributed in space and time. And the pattern of daily delay distribution is represented by the image features. Secondly, the image features are extracted by the pre-trained neural network ResNet50, and the output image feature vector are used for implementing unsupervised k-means clustering algorithm. Finally, the k-means clustering results reveal five different daily delay patterns. The distinctive characteristics of these five delay patterns are analyzed and lead to some significant results, which could provide public transport operators with a better understanding of how delays occur on a specific line.