Unraveling the propagation dynamics of passenger congestion in public transportation systems using big smart card data

Abstract

We propose a comprehensive framework to explore propagation of passenger crowdedness in public transportation systems using 74.16 million trip chains. A deep learning method HSTGCNT is adopted which firstly models normal spatio-temporal dependencies in time series data, and assigns an anomaly score to new data based on its prediction error. The derived discrete anomaly events are further aggregated into groups based on their spatio-temporal proximity by using a clustering algorithm that binds shared nearest neighbors together based on pairwise correlations. The clustering results uncover several key stages located in a propagation chain, such as a core event, adjacent events with spatio-temporal proximity, or a broadcasting group. Weibo data serves as a third-party dataset to verify the above anomaly clusters. Results show that the framework is effective to detect outlier clusters, with recall value being 0.86. Findings may assist public safety departments in taking pro-active actions to interrupt chaos from broadcasting.