Implementing checkpoint-based movement data analysis using cordon networks: A framework validated with transportation case studies

Abstract

Checkpoint data are generated by movement past fixed "checkpoints," such as smart-card readers. The heterogeneity of checkpoint data sources, data structures, data models, and granularities means existing research on checkpoint-based movement analytics relies on bespoke or ad-hoc analytics frameworks. This work addresses this gap by developing a consistent framework for analyzing network-constrained checkpoint movement data based on the "cordon network." The cordon network is a simple, graph-based computational structure that captures the underlying spatial structure and heterogeneous granularity of movement through checkpoints. This paper explores the design, development, and testing of an analytics toolkit founded on the cordon network. The approach and its ability to handle heterogeneous checkpoint data added within transportation networks is validated using three diverse transportation case studies. While this study focuses on transportation-related checkpoint data, the discussion and conclusions outline key factors for extending the framework to other domains, providing guidelines for checkpoint movement analysis across different contexts.