IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i18p11667-d916395.html
   My bibliography  Save this article

Emission Estimation of On-Demand Meal Delivery Services Using a Macroscopic Simulation

Author

Listed:
  • Maren Schnieder

    (The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK)

  • Chris Hinde

    (The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK)

  • Andrew West

    (The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK)

Abstract

While macroscopic simulations of passenger vehicle traffic within cities are now common practice, the integration of last mile delivery into a macroscopic simulation to evaluate the emissions has seldomly been achieved. In fact, studies focusing solely on last mile delivery generally focus on evaluating the delivery service itself. This ignores the effect the delivery service may have on the traffic flow in cities, and therefore, on the resulting emissions. This study fills this gap by presenting the results of two macroscopic traffic simulations of New York City (NYC) in PTV VISUM: (i) on-demand meal delivery services, where the emissions are evaluated for each OD-Pairs (i.e., each trip) and (ii) on-demand meal delivery services, where the emissions are evaluated for each link of the network (i.e., street). This study highlights the effect on-demand meal delivery has on the travelled distance (i.e., detours), congestion and emissions per km of every vehicle in the network, not just the delivery vehicles.

Suggested Citation

  • Maren Schnieder & Chris Hinde & Andrew West, 2022. "Emission Estimation of On-Demand Meal Delivery Services Using a Macroscopic Simulation," IJERPH, MDPI, vol. 19(18), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11667-:d:916395
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/18/11667/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/18/11667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maren Schnieder & Chris Hinde & Andrew West, 2021. "Land Consumption of Delivery Robots and Bicycle Couriers for On-Demand Meal Delivery Using GPS Data and Simulations Based on the Time-Area Concept," Sustainability, MDPI, vol. 13(20), pages 1-25, October.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Perera, Loshaka & Thompson, Russell G. & Wu, Wenyan, 2021. "Toll and subsidy for freight vehicles on urban roads: A policy decision for City Logistics," Research in Transportation Economics, Elsevier, vol. 90(C).
    4. Xiaohong Jiang & Jianxiao Ma & Huizhe Zhu & Xiucheng Guo & Zhaoguo Huang, 2020. "Evaluating the Carbon Emissions Efficiency of the Logistics Industry Based on a Super-SBM Model and the Malmquist Index from a Strong Transportation Strategy Perspective in China," IJERPH, MDPI, vol. 17(22), pages 1-19, November.
    5. Rosero, Fredy & Fonseca, Natalia & López, José-María & Casanova, Jesús, 2021. "Effects of passenger load, road grade, and congestion level on real-world fuel consumption and emissions from compressed natural gas and diesel urban buses," Applied Energy, Elsevier, vol. 282(PB).
    6. Maren Schnieder & Chris Hinde & Andrew West, 2021. "Sensitivity Analysis of Emission Models of Parcel Lockers vs. Home Delivery Based on HBEFA," IJERPH, MDPI, vol. 18(12), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    2. Sunarso Radhitya V.P. & Wibowo Budhi S., 2023. "The Impact of Consolidating On-Demand Food Delivery on Sustainability: A Simulation Study," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 286-297, January.
    3. Jurgis Zagorskas & Zenonas Turskis, 2024. "Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach," Sustainability, MDPI, vol. 16(7), pages 1-21, April.

    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. Maren Schnieder & Chris Hinde & Andrew West, 2021. "Sensitivity Analysis of Emission Models of Parcel Lockers vs. Home Delivery Based on HBEFA," IJERPH, MDPI, vol. 18(12), pages 1-21, June.
    2. Tan Wang & L. Jeff Hong, 2023. "Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 196-215, January.
    3. Léon Faure & Bastien Mollet & Wolfram Liebermeister & Jean-Loup Faulon, 2023. "A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    5. Furqan Dar & Samuel R. Cohen & Diana M. Mitrea & Aaron H. Phillips & Gergely Nagy & Wellington C. Leite & Christopher B. Stanley & Jeong-Mo Choi & Richard W. Kriwacki & Rohit V. Pappu, 2024. "Biomolecular condensates form spatially inhomogeneous network fluids," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    6. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    7. Nina Tiel & Fabian Fopp & Philipp Brun & Johan Hoogen & Dirk Nikolaus Karger & Cecilia M. Casadei & Lisha Lyu & Devis Tuia & Niklaus E. Zimmermann & Thomas W. Crowther & Loïc Pellissier, 2024. "Regional uniqueness of tree species composition and response to forest loss and climate change," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    9. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    10. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    11. Larissa Samaan & Leonie Klock & Sandra Weber & Mirjam Reidick & Leonie Ascone & Simone Kühn, 2024. "Low-Level Visual Features of Window Views Contribute to Perceived Naturalness and Mental Health Outcomes," IJERPH, MDPI, vol. 21(5), pages 1-35, May.
    12. Dennis Bontempi & Leonard Nuernberg & Suraj Pai & Deepa Krishnaswamy & Vamsi Thiriveedhi & Ahmed Hosny & Raymond H. Mak & Keyvan Farahani & Ron Kikinis & Andrey Fedorov & Hugo J. W. L. Aerts, 2024. "End-to-end reproducible AI pipelines in radiology using the cloud," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    13. Piotr Pryciński & Piotr Pielecha & Jarosław Korzeb & Jacek Pielecha & Mariusz Kostrzewski & Ahmed Eliwa, 2024. "Air Pollutant Emissions of Passenger Cars in Poland in Terms of Their Environmental Impact and Type of Energy Consumption," Energies, MDPI, vol. 17(21), pages 1-21, October.
    14. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    15. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    16. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    17. Jackie Grant & Mark Hindmarsh & Sergey E. Koposov, 2022. "The distribution of loss to future USS pensions due to the UUK cuts of April 2022," Papers 2206.06201, arXiv.org.
    18. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    19. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    20. Shukla, Mohak & Thakur, Ajay D., 2022. "An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

    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:jijerp:v:19:y:2022:i:18:p:11667-:d:916395. 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.