TY - JOUR
T1 - A DBSCAN-based framework to mine travel patterns from origin-destination matrices
T2 - Proof-of-concept on proxy static OD from Brisbane
AU - Behara, Krishna N.S.
AU - Bhaskar, Ashish
AU - Chung, Edward
N1 - Funding Information:
The authors are thankful to the Brisbane City Council (BCC) for providing the Bluetooth data and the Queensland University of Technology (QUT) for supporting this research. The conclusions of this paper reflect the understandings of the authors, who are responsible for the accuracy of the findings.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - Bluetooth
KW - DBSCAN
KW - Geographical window
KW - Structural proximity
KW - Typical OD matrices
KW - Typical travel patterns
UR - http://www.scopus.com/inward/record.url?scp=85114471046&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2021.103370
DO - 10.1016/j.trc.2021.103370
M3 - Journal article
AN - SCOPUS:85114471046
SN - 0968-090X
VL - 131
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 103370
ER -