Clustering Activity-Travel Behavior Time Series using Topological Data Analysis

TL;DR

This paper introduces a novel Divide and Combine clustering method for activity-travel behavior time series, leveraging Topological Data Analysis to reveal patterns and differences across decades and cohorts.

Contribution

It presents a new clustering approach combining TDA and time series features, applicable beyond transportation to various categorical time series data.

Findings

Identified three main activity-travel pattern clusters over 30 years.

Detected cohort-based differences in activity-travel behaviors.

Method is versatile for analyzing categorical time series in different domains.

Abstract

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.