A stochastic schedule-following simulation model of bus routes

Microsimulation models of bus routes allow transit operators to both better understand the dynamics of bus routes and facilitate better policy making. Several simulation models of bus routes have been proposed in the literature, including cellular-automata, bus-following and traffic-following models. The majority of these approaches aim to simulate the interactions of a bus with other buses (the bus-following model), with passengers or the surrounding traffic (the traffic-following model), but they all fail to consider the important interactions between buses and their schedules. In a conventional schedule-based public transport system, bus drivers aim to arrive at each stop on time. This means that they will either speed up or slow down if their vehicles are not meeting the schedule. The research within this paper is a novel contribution to the literature of bus route simulation. The authors introduce the first schedule-following model where buses try to adhere to their schedule in a conventional schedule-based public transport system. A simulated numerical analysis shows the characteristics of the proposed schedule-following model and compares it to existing models. Finally, the model is calibrated using Automatic Vehicle Location and Smart Card data from Brisbane, Australia. The results show good model performance against the observed data. The model is relatively simple, yet the fundamental mechanisms that drive the model are novel and it has the potential to be applied in any city with well-defined bus schedules.

  • Record URL:
  • Availability:
  • Supplemental Notes:
    • © 2019 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Kieu, Le-Minh
    • Ngoduy, Dong
    • Malleson, Nicolas
    • Chung, Edward
  • Publication Date: 2019-12


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01732628
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 2 2020 4:05PM