IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6793-d830054.html
   My bibliography  Save this article

Coupling Virtual Reality Simulator with Instantaneous Emission Model: A New Method for Estimating Road Traffic Emissions

Author

Listed:
  • Maria Rosaria De Blasiis

    (Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy)

  • Chiara Ferrante

    (Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy)

  • Fulvio Palmieri

    (Industrial, Electronic and Mechanical Engineering Department (DIIEM), Roma TRE University, 00146 Roma, Italy)

  • Valerio Veraldi

    (Research and Innovation for Sustainable Environment—R.I.S.E. Ltd., 00147 Roma, Italy)

Abstract

The article presents a new methodology for traffic emissions modeling by coupled the use of dynamic emissions models with a virtual reality driving simulator. The former allows the drivers’ behavior to be studied through a virtual reality driving test, focusing the attention on how traffic flow conditions combined with road geometrical characteristics influence the driving behavior. The latter is used to model the instantaneous vehicle emissions, starting from the driving data provided by the driving simulator. The article analyzes the relationship among three factors: the driving behavior, the pollutant emissions, and the traffic flow condition. The results highlight the influence of the drivers’ behavior on fuel consumption and emissions factors. Under high traffic flow, despite the reduction of the average vehicle speed, the average emissions level increases due to the increased vehicle accelerations and decelerations, which influence the behavior of the engine and the aftertreatment system. The proposed approach points out the relationship between vehicle emissions and drivers’ behavior. Since the coupling among instantaneous emissions modeling and geometry-functionality conditions of the road reveals important elements that traditional approaches miss, the proposed method provides a new way to increase the efficiency of road design and management, from the environmental point of view.

Suggested Citation

  • Maria Rosaria De Blasiis & Chiara Ferrante & Fulvio Palmieri & Valerio Veraldi, 2022. "Coupling Virtual Reality Simulator with Instantaneous Emission Model: A New Method for Estimating Road Traffic Emissions," Sustainability, MDPI, vol. 14(11), pages 1-13, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6793-:d:830054
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6793/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/11/6793/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. de Jong, Gerard & Ben-Akiva, Moshe, 2007. "A micro-simulation model of shipment size and transport chain choice," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 950-965, November.
    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.

    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. Sara Martins & Pedro Amorim & Bernardo Almada-Lobo, 2018. "Delivery mode planning for distribution to brick-and-mortar retail stores: discussion and literature review," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 785-812, December.
    2. Gerard de Jong & Reto Tanner & Jeppe Rich & Mikkel Thorhauge & Otto Anker Nielsen & John Bates, 2017. "Modelling production-consumption flows of goods in Europe: the trade model within Transtools3," Journal of Shipping and Trade, Springer, vol. 2(1), pages 1-23, December.
    3. Zhang, Xiunian & Lam, Jasmine Siu Lee, 2018. "Shipping mode choice in cold chain from a value-based management perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 147-167.
    4. Konstantinus, Abisai & Zuidgeest, Mark & Hess, Stephane & de Jong, Gerard, 2020. "Assessing inter-urban freight mode choice preference for short-sea shipping in the Southern African Development Community region," Journal of Transport Geography, Elsevier, vol. 88(C).
    5. Feo, María & Espino, Raquel & García, Leandro, 2011. "An stated preference analysis of Spanish freight forwarders modal choice on the south-west Europe Motorway of the Sea," Transport Policy, Elsevier, vol. 18(1), pages 60-67, January.
    6. Valerio Gatta & Edoardo Marcucci, 2013. "Ex-Post Implications Of Ex-Ante Data Acquisition Strategies In Multiagent-Type Urban Freight Policy Evaluation," Working Papers 0613, CREI Università degli Studi Roma Tre, revised 2013.
    7. Ferrari, Paolo, 2014. "The dynamics of modal split for freight transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 163-176.
    8. Steffen Jaap Bakker & E. Ruben van Beesten & Ingvild Synn{o}ve Brynildsen & Anette Sandvig & Marit Siqveland & Asgeir Tomasgard, 2023. "STraM: a framework for strategic national freight transport modeling," Papers 2304.14001, arXiv.org.
    9. Sharman, Bryce W. & Roorda, Matthew J., 2013. "Multilevel modelling of commercial vehicle inter-arrival duration using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 94-107.
    10. Abate, Megersa & Vierth, Inge & de Jong , Gerard, 2014. "Joint econometric models of freight transport chain and shipment size choice," Working papers in Transport Economics 2014:9, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    11. Massiani, Jérôme, 2014. "A micro founded approach to the valuation of benefits of freight travel time savings," Research in Transportation Economics, Elsevier, vol. 47(C), pages 61-69.
    12. Tapia, Rodrigo J. & de Jong, Gerard & Larranaga, Ana M. & Bettella Cybis, Helena B., 2020. "Application of MDCEV to infrastructure planning in regional freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 255-271.
    13. Marcucci, Edoardo & Gatta, Valerio, 2017. "Investigating the potential for off-hour deliveries in the city of Rome: Retailers’ perceptions and stated reactions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 142-156.
    14. Meisel, Frank & Kirschstein, Thomas & Bierwirth, Christian, 2013. "Integrated production and intermodal transportation planning in large scale production–distribution-networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 62-78.
    15. Ottemöller, Ole & Friedrich, Hanno, 2019. "Modelling change in supply-chain-structures and its effect on freight transport demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 23-42.
    16. Ferrari, Paolo, 2018. "Some necessary conditions for the success of innovations in rail freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 747-758.
    17. Arencibia, Ana Isabel & Feo-Valero, María & García-Menéndez, Leandro & Román, Concepción, 2015. "Modelling mode choice for freight transport using advanced choice experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 252-267.
    18. Piendl, Raphael & Liedtke, Gernot & Matteis, Tilman, 2017. "A logit model for shipment size choice with latent classes – Empirical findings for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 188-201.
    19. Keya, Nowreen & Anowar, Sabreena & Bhowmik, Tanmoy & Eluru, Naveen, 2021. "A joint framework for modeling freight mode and destination choice: Application to the US commodity flow survey data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    20. Keya, Nowreen & Anowar, Sabreena & Eluru, Naveen, 2019. "Joint model of freight mode choice and shipment size: A copula-based random regret minimization framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 97-115.

    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:14:y:2022:i:11:p:6793-:d:830054. 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.