Mining Smart Card Data for Travellers’ Mini Activities

In the context of public transport modeling and simulation, the author address the problem of mismatch between simulated transit trips and observed ones. They point to the weakness of the current travel demand modeling process; the trips it generates are overly optimistic and do not reflect the real passenger choices. To explain the deviation of simulated trips from the observed trips, they introduce the notion of mini-activities the travelers do during the trips. They propose to mine the smart card data and identify characteristics that help detect the mini activities.They develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, they build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the trip characteristics converge to the target distributions. They test their method on the trip data set collected in Nancy, France. The evaluation results demonstrate a very important reduction of the trip generation error, and a good capacity to cope with new simulation scenarios.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2852493
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Chidlovskii, Boris
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3676-3685
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Activity choices; Data mining; Fare collection; Intelligent agents; Markov chains; Monte Carlo method; Public transit; Smart cards; Transit riders; Travel demand; Trip generation
• Subject Areas: Data and Information Technology; Passenger Transportation; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01690056
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 27 2018 3:43PM