Clustering Activity-Travel Behavior Time Series using Topological Data Analysis
It addresses the challenge of analyzing big traffic data for transportation researchers, offering a generally applicable method for clustering categorical time series, though it appears incremental as it combines existing techniques.
The paper tackles clustering activity-travel behavior time series by proposing a Divide and Combine approach using K-means with features from Time Series Analysis and Topological Data Analysis, finding that patterns from 1990 to 2017 can be grouped into three clusters and supporting claims about differences across survey cohorts.
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new Divide and Combine based approach to do K means clustering on activity-travel behavior time series using features that are derived using tools in Time Series Analysis and Topological Data Analysis. Clustering data from five waves of the National Household Travel Survey ranging from 1990 to 2017 suggests that activity-travel patterns of individuals over the last three decades can be grouped into three clusters. Results also provide evidence in support of recent claims about differences in activity-travel patterns of different survey cohorts. The proposed method is generally applicable and is not limited only to activity-travel behavior analysis in transportation studies. Driving behavior, travel mode choice, household vehicle ownership, when being characterized as categorical time series, can all be analyzed using the proposed method.