IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p9871-d1175960.html
   My bibliography  Save this article

Motorway Traffic Emissions Estimation through Stochastic Fundamental Diagram

Author

Listed:
  • Andrea Gemma

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy)

  • Orlando Giannattasio

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy)

  • Livia Mannini

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy)

Abstract

Travel time, or, more generally, level of service, has always been considered the main parameter with which to design roads, particularly in extra-urban areas where geometries and policies, such as speed limits, play a key role in the performance achieved. Unfortunately, this type of approach does not consider the impact on emissions that is obtained when only performance-based goals are pursued. The paper deals with the analysis of the impact on emissions and fuel consumption under different traffic conditions, and we present a new methodology for emission estimation based on the stochastic formulation of the fundamental diagram in a highway environment. The proposed methodology estimates the emissions using a stochastic adaptation of the CORINAIR methodology based on COPERT software on both specific vehicle types and the average Italian vehicle fleet. As expected, due to the convexity of the emission function, accounting for speed dispersion leads to an increase in energy consumption and emissions. Tests show that the stochastic component can lead to an increase in the emission estimation up to 5.5% and, therefore, it should be considered. The methodology has been applied by means of real trajectories, and the results of the application show that performance optimization strategies can contrast with sustainability and emission reduction policies. Results show that for some vehicular classes, emissions or fuel consumption are highly dependent on speed, with different proportionalities. In all cases, the minimum consumption is obtained at speeds ranging from 70 to 90 km/h. The analysis of the curves shows that an increase in speeds, even to reach low speeds, generally leads to an increase in energy consumption and emissions per kilometer traveled and, therefore, is independent of the decrease in travel time.

Suggested Citation

  • Andrea Gemma & Orlando Giannattasio & Livia Mannini, 2023. "Motorway Traffic Emissions Estimation through Stochastic Fundamental Diagram," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9871-:d:1175960
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/9871/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/9871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikolić, Marija & Bierlaire, Michel & Farooq, Bilal & de Lapparent, Matthieu, 2016. "Probabilistic speed–density relationship for pedestrian traffic," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 58-81.
    2. Jabari, Saif Eddin & Zheng, Jianfeng & Liu, Henry X., 2014. "A probabilistic stationary speed–density relation based on Newell’s simplified car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 205-223.
    3. Qu, Xiaobo & Zhang, Jin & Wang, Shuaian, 2017. "On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 256-271.
    4. Luc Wismans & Eric Van Berkum & Michiel Bliemer, 2011. "Modelling Externalities using Dynamic Traffic Assignment Models: A Review," Transport Reviews, Taylor & Francis Journals, vol. 31(4), pages 521-545.
    5. Mohammad Halakoo & Hao Yang & Harith Abdulsattar, 2023. "Heterogeneity Aware Emission Macroscopic Fundamental Diagram (e-MFD)," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bai, Lu & Wong, S.C. & Xu, Pengpeng & Chow, Andy H.F. & Lam, William H.K., 2021. "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 524-539.
    2. Seo, Toru & Kawasaki, Yutaka & Kusakabe, Takahiko & Asakura, Yasuo, 2019. "Fundamental diagram estimation by using trajectories of probe vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 40-56.
    3. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    4. Storm, Pieter Jacob & Mandjes, Michel & van Arem, Bart, 2022. "Efficient evaluation of stochastic traffic flow models using Gaussian process approximation," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 126-144.
    5. Anupriya & Daniel J. Graham & Daniel Horcher & Prateek Bansal, 2021. "Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach," Papers 2104.02399, arXiv.org.
    6. Saif Eddin Jabari & Laura Wynter, 2016. "Sensor placement with time-to-detection guarantees," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 415-433, December.
    7. Ji, Jingwei & Lu, Ligang & Jin, Zihao & Wei, Shoupeng & Ni, Lu, 2018. "A cellular automata model for high-density crowd evacuation using triangle grids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1034-1045.
    8. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "A stochastic dynamic network loading model for mixed traffic with autonomous and human-driven vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    9. Yin, Ruyang & Zheng, Nan & Liu, Zhiyuan, 2022. "Estimating fundamental diagram for multi-modal signalized urban links with limited probe data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    10. Wang, Shuaian & Zhang, Wei & Qu, Xiaobo, 2018. "Trial-and-error train fare design scheme for addressing boarding/alighting congestion at CBD stations," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 318-335.
    11. Huang, Shenshi & Zhang, Teng & Lo, Siuming & Lu, Shouxiang & Li, Changhai, 2018. "Experimental study of individual and single-file pedestrian movement in narrow seat aisle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1023-1033.
    12. Zhang, Jin & Qu, Xiaobo & Wang, Shuaian, 2018. "Reproducible generation of experimental data sample for calibrating traffic flow fundamental diagram," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 41-52.
    13. Varga, Balázs & Tettamanti, Tamás & Kulcsár, Balázs & Qu, Xiaobo, 2020. "Public transport trajectory planning with probabilistic guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 81-101.
    14. Arwa S. Sayegh & Richard D. Connors & James E. Tate, 2018. "Uncertainty Propagation from the Cell Transmission Traffic Flow Model to Emission Predictions: A Data-Driven Approach," Service Science, INFORMS, vol. 52(6), pages 1327-1346, December.
    15. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    16. Xiang Wang & Po Zhao & Yanyun Tao, 2018. "Evaluating Impacts of Overloaded Heavy Vehicles on Freeway Traffic Condition by a Novel Multi-Class Traffic Flow Model," Sustainability, MDPI, vol. 10(12), pages 1-22, December.
    17. Li, Li & Jabari, Saif Eddin, 2019. "Position weighted backpressure intersection control for urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 435-461.
    18. Gore, Ninad & Arkatkar, Shriniwas & Joshi, Gaurang & Pulugurtha, Srinivas S., 2023. "A hazard-based model to derive travel time under congested conditions," Transport Policy, Elsevier, vol. 138(C), pages 1-16.
    19. Hu, Xu & Li, Dongshuang & Yu, Zhaoyuan & Yan, Zhenjun & Luo, Wen & Yuan, Linwang, 2022. "Quantum harmonic oscillator model for fine-grained expressway traffic volume simulation considering individual heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    20. Hänseler, Flurin S. & Lam, William H.K. & Bierlaire, Michel & Lederrey, Gael & Nikolić, Marija, 2017. "A dynamic network loading model for anisotropic and congested pedestrian flows," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 149-168.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9871-:d:1175960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.