Evaluating the Impacts of a Bus-Rapid Transit on Users' Temporal Patterns Using Cross Correlation Distance and Sampled Hierarchical Clustering Applied to Smart Card Data

Smart card data has proven to be useful to characterize public transit ridership and travel behavior. However, public transit smart card data is often treated by only using every record separately so that it is difficult to find some research on each traveler’s daily profile. In this study, the authors propose a cross correlation distance method to cluster the smart card user’s daily profile in the Société de transport de l’Outaouais, Canada. The segmentation method is original considering at the same time cross correlation distance, sampling and hierarchical clustering. It is applied on two distinct datasets, one being before the implementation of a new bus-rapid transit (BRT) system in the region, the other being after the new system has been put in place. The approach permits a straightforward comparison of the before and after situations, and helps to understand the changes in travel behavior in an individual manner. The experiment shows that 60.75% users have changed their pattern in weekdays, so introduction of BRT may represent a large impact to users. Finally, the authors discuss the limitation such as the exogenous factors and give some perspective of the method they developed.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • He, Li
  • Trépanier, Martin
  • Agard, Bruno
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Bus rapid transit; Cluster analysis; Data mining; Public transit; Smart cards; Travel behavior
• Geographic Terms: Montreal (Canada)
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01626900
• Record Type: Publication
• Report/Paper Numbers: 17-03711
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2017 9:27AM