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Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Author

Listed:
  • Justina Ranceva

    (Department of Roads, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania)

  • Rasa Ušpalytė-Vitkūnienė

    (Department of Roads, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania)

Abstract

A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

Suggested Citation

  • Justina Ranceva & Rasa Ušpalytė-Vitkūnienė, 2024. "Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1412-:d:1335274
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    References listed on IDEAS

    as
    1. Sönke Beckmann & Sebastian Trojahn & Hartmut Zadek, 2023. "Process Model for the Introduction of Automated Buses," Sustainability, MDPI, vol. 15(19), pages 1-36, September.
    2. Abderrahman Ait-Ali & Jonas Eliasson, 2022. "The value of additional data for public transport origin–destination matrix estimation," Public Transport, Springer, vol. 14(2), pages 419-439, June.
    3. Philipp Heyken Soares & Leena Ahmed & Yong Mao & Christine L Mumford, 2021. "Public transport network optimisation in PTV Visum using selection hyper-heuristics," Public Transport, Springer, vol. 13(1), pages 163-196, March.
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