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Improving Scheduling Through Performance Monitoring

In: Computer-aided Systems in Public Transport

Author

Listed:
  • Thomas J. Kimpel

    (Portland State University)

  • James G. Strathman

    (Portland State University)

  • Steve Callas

    (TriMet)

Abstract

Historically, schedulers and operations management personnel have made decisions with limited information about various states of the transit system. The present study highlights innovative uses of data collected via automatic vehicle location and automatic passenger count technologies in the areas of scheduling and operations management at TriMet, the transit provider for the Portland, Oregon metropolitan region. Two main topics are addressed in this paper. First, we look at efforts at TriMet involving the use of archived operations data to improve bus schedules. Second, we look at the role of operator behavior in relation to service reliability and steps the agency is taking to reduce run time variability and maintain vehicle headways through better management of operators. The quality, quantity, and disaggregate nature of data at TriMet has greatly enhanced the agency’s ability to generate performance reports as well as undertake special purpose studies targeting specific operational issues, providing essential feedback into the scheduling process.

Suggested Citation

  • Thomas J. Kimpel & James G. Strathman & Steve Callas, 2008. "Improving Scheduling Through Performance Monitoring," Lecture Notes in Economics and Mathematical Systems, in: Mark Hickman & Pitu Mirchandani & Stefan Voß (ed.), Computer-aided Systems in Public Transport, pages 253-280, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-73312-6_13
    DOI: 10.1007/978-3-540-73312-6_13
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    Cited by:

    1. Weng, JianCheng & Yu, JiangBo & Di, XiaoJian & Lin, PengFei & Wang, Jing-Jing & Mao, Li-Zeng, 2023. "How does the state of bus operations influence passengers’ service satisfaction? A method considering the differences in passenger preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    2. You-Jin Jung & Jeffrey M. Casello, 2020. "Assessment of the transit ridership prediction errors using AVL/APC data," Transportation, Springer, vol. 47(6), pages 2731-2755, December.

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