Improving Transit On-time Performance with AVL Data: A Timetable Approach

This paper describes an approach that can be used for improving on-time performance at transit agencies. It takes advantage of data from an automated vehicle location (AVL) system, in particular the schedule adherence information. Schedule adherence refers to the difference between real time and scheduled time of arrivals or departures. On-time performance, on the other hand, is a percentage value used to indicate buses arriving or departing late, on-time, or early. A methodology that can be used to update the bus timetables by using AVL schedule adherence data is described. Before this methodology can be applied, the AVL data need to be cleaned, manipulated, and stored in a database to allow the processing of the data. Using statistical analysis, the distribution of the data can be determined. Once the statistical distribution is known, the main goal is to maximize the density area of the on-time performance range, which is based on the on-time performance parameters. From this distribution, the optimal value is obtained and it is used to update the times in the timetables. A validation process is provided to ensure the updating takes effect. Then, a comparison process is used to assess the on-time performance improvements. In addition, a simulation process is presented to provide a different perspective than the statistical methodology. To demonstrate the applicability of this research, a case study using data from Miami-Dade Transit is included and on-time performance calculations for Routes 99 and 57 are presented. Ideas for future research in this area are also identified.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 16p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01154281
  • Record Type: Publication
  • Report/Paper Numbers: 10-3486
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 11:45AM