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Dynamic demand estimation and microscopic traffic simulation of a university campus transportation network

Author

Listed:
  • Shan Huang
  • Liya Guo
  • Yan Yang
  • Irene Casas
  • Adel W. Sadek

Abstract

This article documents the authors' experience with the modeling, simulation, and analysis of a university transportation system, using the TRansportation ANalysis and SIMulation System (TRANSIMS). The processes of data preparation and network coding are described, followed by the algorithm developed to estimate the dynamic 24-hour demand, which includes a procedure for estimating the ‘desirability’ of the different parking lots from readily available data. The dynamic demand estimation algorithm is validated by comparing estimated and observed parking lot occupancies, where it is shown that the algorithm is capable of replicating observed results. Finally, an example is included to demonstrate how the developed model can be used in campus transportation planning. Besides serving as a first case study for using TRANSIMS to model a university campus, the study's contributions include the development of a procedure for parking lot desirability ranking and a practical procedure for estimating dynamic demand on university campuses.

Suggested Citation

  • Shan Huang & Liya Guo & Yan Yang & Irene Casas & Adel W. Sadek, 2012. "Dynamic demand estimation and microscopic traffic simulation of a university campus transportation network," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(4), pages 449-467, January.
    • Handle: RePEc:taf:transp:v:35:y:2012:i:4:p:449-467
    DOI: 10.1080/03081060.2012.680818
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    Cited by:

    1. Hamad, Khaled & Obaid, Lubna, 2022. "Tour-based travel demand forecasting model for a university campus," Transport Policy, Elsevier, vol. 117(C), pages 118-137.
    2. Salvatore Leonardi & Natalia Distefano, 2023. "Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study," Sustainability, MDPI, vol. 15(4), pages 1-21, February.

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