A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane. (October 2021) - Behara, Krishna N.S.; Bhaskar, Ashish; Chung, Edward
Highlights: Structural proximity measure to cluster high-dimensional OD matrices. Prior identification of subspaces to address multi-density OD database. Two-level DBSCAN approach to identify optimal parameters. Estimation of typical OD matrices from clusters of typical travel patterns. Proposed approach performed better than k-medoids, spectral, and hierarchical. Abstract: Limited studies exist in the literature on demand related travel patterns, the analysis of which requires a rich database of Origin Destination (OD) matrices with appropriate clustering algorithms. This paper develops a methodological framework to explore typical travel patterns from multi-density high dimensional matrices and estimate typical OD corresponding to those patterns. The contributions of the paper are multi-fold. First, to cluster high-dimensional OD matrices, we deploy geographical window-based structural similarity index (GSSI) as proximity measure in the DBSCAN algorithm that captures both OD structure and network related attributes. Second, to address the issue of multi-density data points, we propose clustering on individual subspaces. Third, we develop a simple two-level approach to identify optimum DBSCAN parameters. Finally, as proof-of-concept, the proposed framework is applied on proxy OD matrices from real Bluetooth data (B-OD) from Brisbane City Council region. The OD matrix clusters, typical travel patterns, and typical B-OD matrices are estimated for this study region. The analysis reveals nine typical travel patterns. The methodology was also found to perform better when GSSI was used instead of Euclidian distance as a proximity measure, and two-level DBSCAN instead of K-medoids, Spectral, and Hierarchical methods. The framework is generic and applicable for OD matrices developed from other data sources and any spatiotemporal context. DBSCAN is chosen for this study because it does not require a pre-determined number of clusters, and it identifies outliers as noise.