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Modelling the Effects of Traffic-Calming Introduction to Volume–Delay Functions and Traffic Assignment

Author

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  • Jan Paszkowski

    (Politechnika Krakowska, ul. Warszawska 24, 31-155 Kraków, Poland
    Westsächsische Hochschule Zwickau, Kornmarkt 1, 08056 Zwickau, Germany)

  • Marcus Herrmann

    (Westsächsische Hochschule Zwickau, Kornmarkt 1, 08056 Zwickau, Germany)

  • Matthias Richter

    (Westsächsische Hochschule Zwickau, Kornmarkt 1, 08056 Zwickau, Germany)

  • Andrzej Szarata

    (Politechnika Krakowska, ul. Warszawska 24, 31-155 Kraków, Poland)

Abstract

Traffic calming is introduced to minimise the negative results of motor vehicle use, for example, low safety level or quality of life, high noise and pollution. It can be implemented through the introduction of road infrastructure reducing the velocity and the traffic volume. In this paper, we studied how traffic-calming influences the traffic assignment. For the research, a traffic-calming measure of speed cushions on the Stachiewicza street in Krakow was taken. A method of extracting trajectories from aerial footage was shown, and it was used to build a model. For a given example, through driving characteristics research and microscopic modelling, volume–delay BPR functions were estimated—for a street with and without traffic calming. Later, a toy network of two roads of the same length, connecting the same origin and destination, was simulated using an equilibrium traffic assignment method. Simulations were conducted both with the use of PTV Vissim and Visum software and through individual calculations. According to the results of this paper, there was a difference in traffic volume according to the equilibrium traffic assignment in the aforementioned toy network as a function of total network traffic volume.

Suggested Citation

  • Jan Paszkowski & Marcus Herrmann & Matthias Richter & Andrzej Szarata, 2021. "Modelling the Effects of Traffic-Calming Introduction to Volume–Delay Functions and Traffic Assignment," Energies, MDPI, vol. 14(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3726-:d:579470
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    References listed on IDEAS

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    1. Denos C. Gazis & Robert Herman & Richard W. Rothery, 1961. "Nonlinear Follow-the-Leader Models of Traffic Flow," Operations Research, INFORMS, vol. 9(4), pages 545-567, August.
    2. Florian, Michael & Gaudry, Marc & Lardinois, Christian, 1988. "A two-dimensional framework for the understanding of transportation planning models," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 411-419, December.
    3. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
    4. Daganzo, Carlos F. & Geroliminis, Nikolas, 2008. "An analytical approximation for the macroscopic fundamental diagram of urban traffic," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 771-781, November.
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    Cited by:

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    2. Elżbieta Macioszek & Anna Granà & Paulo Fernandes & Margarida C. Coelho, 2022. "New Perspectives and Challenges in Traffic and Transportation Engineering Supporting Energy Saving in Smart Cities—A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 15(12), pages 1-8, June.
    3. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    4. Heriberto Pérez-Acebo & Robert Ziolkowski & Hernán Gonzalo-Orden, 2021. "Evaluation of the Radar Speed Cameras and Panels Indicating the Vehicles’ Speed as Traffic Calming Measures (TCM) in Short Length Urban Areas Located along Rural Roads," Energies, MDPI, vol. 14(23), pages 1-17, December.
    5. Nicola Berloco & Stefano Coropulis & Giuseppe Garofalo & Paolo Intini & Vittorio Ranieri, 2023. "Analysis of the Factors Influencing Speed Cushion Effectiveness in the Urban Context: A Case Study Experiment in the City of Bari, Italy," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    6. Alicja Sołowczuk, 2021. "Effect of Traffic Calming in a Downtown District of Szczecin, Poland," Energies, MDPI, vol. 14(18), pages 1-21, September.
    7. Jindong Wang & Jianguo Ying & Shengchuan Jiang, 2022. "An Adaptive Traffic-Calming Measure and Effectiveness Evaluation in a Large Urban Complex of Shanghai, China," Sustainability, MDPI, vol. 14(20), pages 1-10, October.

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