Place-Centered Bus Accessibility Time Series Classification with Floating Car Data: An Actual Isochrone and Dynamic Time Warping Distance-Based k-Medoids Method

Classifying a time series is fundamental task in temporal analysis. This provides valuable insights into the characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, yet be place-centered research. In this study, we have proposed an actual isochrone dynamic time-wrapping distance-based k-medoids method tested its applicability bus Using floating car data, our calculated area as measurement constructs for each hexagonal geographical unit within interest. We then warp distance between pairwise units used these distances k-medoid clustering. The optimized class number k was selected by considering elbow method, silhouette score, human examination. Our case study Hefei, China demonstrates feasibility classification. also discovered that resulting classes follow clear spatial patterns, indicating different may correlated with their location. To knowledge, first such classification data-driven can inform era which large quantities spatiotemporal data like are available.