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Simulation of Reducing Pollutants and Fuel Consumption Through the Connection of Commercial Vehicles with Infrastructure

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Abstract

The objective of this study was to investigate the situation in which vehicles are connected to technology, allowing drivers to receive notifications as they approach potentially hazardous zones, with a particular emphasis on commercial vehicles. For this purpose, the simulation software features were described, and the methodology for obtaining simulation data was elucidated. The effect of vehicle-to-infrastructure communication on pollution and fuel consumption was examined, and suitable simulation software and tools were chosen for the investigation; The suitable plugins for establishing the scenario settings were selected; Software for facilitating communication between vehicles was developed; The proposed system for mitigating pollution and reducing fuel consumption was assessed. The simulation was conducted using commercial vehicles manufactured by Iran Khodro Diesel Company. In creating a realistic simulation, the process involved designing and defining a scenario that realistically explored the reduction of carbon dioxide emissions, fuel consumption, and the integration of transportation infrastructure connected to vehicles. The scenario conditions for the Iranian market take into account factors such as road conditions and the specific characteristics of commercial vehicles. The results of the simulation suggested that, as the vehicle approaches the warning range, there was a potential reduction in carbon dioxide emissions of up to 4%. In addition, fuel consumption was reduced by 5% if it is connected to the infrastructure in just 30% of commercial vehicles. Consequently, it was shown that it is possible to achieve savings of up to 1045.36 million liters of fuel annually.

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  1. Application Programming Interfaces.

References

  1. Alam, A., et al.: Heavy-duty vehicle platooning for sustainable freight transportation: a cooperative method to enhance safety and efficiency. IEEE Control. Syst. Mag. 35(6), 34–56 (2015)

    Article  Google Scholar 

  2. Jia, D., Ngoduy, D.: Enhanced cooperative car following traffic model with the combination of V2V and V2I communication. Transp. Res. Part B 90, 172–191 (2016)

    Article  Google Scholar 

  3. Michigan Department of Transportation (MDOT). Traffic Monitoring Information System (TMIS). http://mdotnetpublic.state.mi.us/ tmispublic (2014). Accessed 20 March 2020

  4. Qin, Y., Wang, H., Ran, B.: Stability analysis of connected and automated vehicles to reduce fuel consumption and emissions. J. Transport. Eng. Part A Syst. 144, 04018068 (2018). https://doi.org/10.1061/JTEPBS.0000196

    Article  Google Scholar 

  5. Alfaseeh, L., Djavadian, S., Farooq, B., Hatzopoulou, M.: Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations. Transport. Res. Transport. Environ. 73, 142e51 (2019). https://doi.org/10.1016/j.trd.2019.06.008

  6. Jameel, F., Javed, M.A., Ngo, D.T.: Performance analysis of cooperative V2V and V2I communications under correlated fading. IEEE Trans. Intell. Transp. Syst. 21(8), 3476–3484 (2020)

  7. Hamdar, S.H., Qin, L., Talebpour, A.: Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework. Transp. Res. Part C Emerg. Technol. 67, 193–213 (2016). https://doi.org/10.1016/j.trc.2016.01.017

    Article  Google Scholar 

  8. Zhao, W., Ngoduy, D., Shepherd, S., Liu, R., Papageorgiou, M.: A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transport. Res. Part C 95, 802e821 (2018). https://doi.org/10.1016/j.trc.2018.05.025

  9. Yang, H., Rakha, H., Ala, M.V.: Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans. Intell. Transp. Syst. 18(6) (2017)

  10. Yu, C., Feng, Y., Liu, H.X., Ma, W., Yang, X.: Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transport. Res. Part B 112, 89e112 (2018). https://doi.org/10.1016/j.trb.2018.04.007

    Article  Google Scholar 

  11. Hu, Z., Zheng, Z., Wang, T., Song, L., Li, X.: Roadside unit caching: Auction-based storage allocation for multiple content providers. IEEE Trans. Wireless Commun. 16(10), 6321–6334 (2017)

    Article  Google Scholar 

  12. Steinmetz, E., et al.: A stochastic geometry model for vehicular communication near intersections. In: IEEE Globecom Workshops, GC Wkshps, San Diego, CA, pp. 1–6 (2015)

  13. Akhtar, N., Ergen, S.C., Ozkasap, O.: Vehicle mobility and communication channel models for realistic and efficient highway VANET simulation. IEEE Trans. Veh. Technol. 64(1), 248–262 (2015)

    Article  Google Scholar 

  14. Ploeg, J., Serrarens, A.F.A., Heijenk, G.: Connect & drive: design and evaluation of cooperative adaptive cruise control for congestion reduction. J. Mod. Transp. 19(3), 207–213 (2011)

    Article  Google Scholar 

  15. Kan, Z., Tang, L., Kwan, M.P., Ren, C., Liu, D., Pei, T., Liu, Y., Deng, M., Li, Q.: Finegrained analysis on fuel-consumption and emission from vehicles trace. J. Clean. Prod. 203, 340e352 (2018). https://doi.org/10.1016/j.jclepro.2018.08.222

  16. He, X., Wu, X.: Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system. Transport. Res. Transport Environ. 63, 907e922 (2018). https://doi.org/10.1016/j.trd.2018.07.014

  17. Fluidmesh, . Dsrc roadside unit. https://www.fluidmesh.com/dsrc-roadside-unit/. Accessed on 02/09/2020

  18. Liu, Y., Dai, H.-N., Wang, Q., Shukla, M.K., Imran, M.: Unmanned aerial vehicle for internet of everything: Opportunities and challenges. Comput. Commun. 155, 66–83 (2020).

  19. Naik, G., Choudhury, B., Park, J.: IEEE 802.11bd & 5G NR V2X: evolution of radio access technologies for V2X communications. CoRR (2019)

  20. SAE On-Road Automated Vehicle Standards Committee and Others, Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE Standard J 3016, 2018, pp. 1–16

  21. ETSI, En 302 637–2 v1.3.2 – Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications, Part 2: Specification of Cooperative Awareness Basic Service (2014)

  22. Boban, M., d’Orey, P.M.: Measurement-based Evaluation of Cooperative Awareness for V2V and V2I Communication. In Proceedings of the IEEE Vehicular Networking Conference (VNC), 3–5 Dec. 2014, Paderborn, Germany, 1–8 (2014)

  23. Milliken, W. F., Milliken, D. L.: Race Car Vehicle Dynamics. Society of Automotive Engineers (1995)

  24. Priemer, C., Friedrich, B.: A decentralized adaptive traffic signal control using V2I communication data. 2009 12th. Int. IEEE Conf. Intell. Transp. Syst. 765e770 (2009). https://doi.org/10.1109/ITSC.2009.5309870

  25. Feng, Y., Yu, C., Liu, H.X.: Spatiotemporal intersection control in a connected and automated vehicle environment. Transport. Res. C Emerg. Technol. 89, 364e383 (2018). https://doi.org/10.1016/j.trc.2018.02.001

  26. Lee, J., Park, B. (Brian), Yun, I.: Cumulative travel-time responsive real-time intersection control algorithm in the connected vehicle environment (2013)

  27. Javed, M.A., Nafi, N.S., Basheer, S., Aysha Bivi, M., Bashir, A.K.: Fog-assisted cooperative protocol for traffic message transmission in vehicular networks. IEEE Access 7, 166148–166156 (2019)

    Article  Google Scholar 

  28. Calvert, S., Schakel, W., van Arem, B.: Evaluation and modelling of the traffic flow effects of truck platooning. Transp. Res. Part C: Emerg. Technol. 105, 1–22 (2019)

    Article  Google Scholar 

  29. Javed, M.A., Zeadally, S., Hamida, E.B.: Data analytics for cooperative intelligent transport systems. Vehicul. Commun. 15, 63–72 (2019)

    Article  Google Scholar 

  30. Kim, D., Velasco, Y., Wang, W., Uma, R.N., Hussain, R., Lee, S.: A new comprehensive rsu installation strategy for cost-efficient vanet deployment. IEEE Trans. Vehicul. Technol. 66(5), 4200–4211 (2017)

    Google Scholar 

  31. Rajamani, R.: Vehicle Dynamics and Control. 2. ed. Mechanical Engineering Series. Springer, New York, NY Heidelberg (2012)

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Authors and Affiliations

  1. Faculty of Mechanical Engineering Conversion Energy at Iran University of Technology, Tehran, Iran

    Amirhossein Barzigar

  2. Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Pardis Shahid Abbaspour, Tehran, Iran

    Abbas Naeimi

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  1. Amirhossein Barzigar
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  2. Abbas Naeimi
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Barzigar, A., Naeimi, A. Simulation of Reducing Pollutants and Fuel Consumption Through the Connection of Commercial Vehicles with Infrastructure. Int. J. ITS Res. 22, 189–204 (2024). https://doi.org/10.1007/s13177-024-00388-2

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  • DOI: https://doi.org/10.1007/s13177-024-00388-2

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