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Impact Evaluation of Network Structure Differentiation on Traffic Noise during Road Network Design

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  • Haibo Wang

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Zhipeng Wu

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Xiaolin Yan

    (School of Civil and Transportation, Hebei University of Technology, Tianjin 300401, China)

  • Jincai Chen

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

To evaluate the characteristics of traffic noise under different network structures, an evaluation method is established to clarify the mechanism of traffic noise impacted by variational network structures. First, a stochastic user equilibrium (SUE) model considering intersection delays is developed to allocate traffic flows of different network structures, and a flow-based univariate noise prediction model is used to simulate network noise distribution. Secondly, differentiation indexes including the coefficient of inhomogeneity ( COI ) and the concept of Lane Area Ratio ( LAR ) are set to quantify the network structure differences. Finally, a structural equation model (SEM) is developed to investigate the influence mechanism of network structure differentiation on traffic noise. The following conclusions are obtained: (1) The impact of network density differentiation on traffic noise is mainly reflected in the changes of road traffic flow and speed. Traffic noise decreases as the network density increases. In this case, when network density increases by 1 km/km 2 , traffic noise decreases by 1.6 dB. As network density increases, which means a dispensation of traffic flow, traffic noise is diminished by a reduction in traffic flow and speed. (2) The impact of road spatial location differentiation on traffic noise mainly depends on the number of noise sources. Traffic noise increases with the dense distribution of roads. In this case, when the COI increases by 1, the traffic noise increases by 3.0 dB. A higher COI indicates that the region will be exposed to more noise sources, which leads to traffic noise raises. The study can provide an effective basis for traffic noise control at the initial stage of network planning.

Suggested Citation

  • Haibo Wang & Zhipeng Wu & Xiaolin Yan & Jincai Chen, 2023. "Impact Evaluation of Network Structure Differentiation on Traffic Noise during Road Network Design," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6483-:d:1120883
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    References listed on IDEAS

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    1. Fisk, Caroline, 1980. "Some developments in equilibrium traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 243-255, September.
    2. Mathias Basner & Sarah McGuire, 2018. "WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Effects on Sleep," IJERPH, MDPI, vol. 15(3), pages 1-45, March.
    3. Elise Van Kempen & Maribel Casas & Göran Pershagen & Maria Foraster, 2018. "WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Cardiovascular and Metabolic Effects: A Summary," IJERPH, MDPI, vol. 15(2), pages 1-59, February.
    4. Rainer Guski & Dirk Schreckenberg & Rudolf Schuemer, 2017. "WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Annoyance," IJERPH, MDPI, vol. 14(12), pages 1-39, December.
    5. Ni Sheng & U. Wa Tang, 2011. "Spatial Analysis of Urban Form and Pedestrian Exposure to Traffic Noise," IJERPH, MDPI, vol. 8(6), pages 1-14, June.
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    Cited by:

    1. Wencheng Yu & Ji-Cheng Jang & Yun Zhu & Jianxin Peng & Wenwei Yang & Kunjie Li, 2024. "Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network," Sustainability, MDPI, vol. 16(14), pages 1-16, July.

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