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Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Author

Listed:
  • Karthiga Kasi

    (Division of Transportation Engineering, Department of Civil Engineering, College of Engineering Guindy, Anna University, Chennai 600025, Tamil Nadu, India)

  • Gunasekaran Karuppanan

    (Division of Transportation Engineering, Department of Civil Engineering, College of Engineering Guindy, Anna University, Chennai 600025, Tamil Nadu, India)

Abstract

Vehicle platoon studies are essential for understanding and managing traffic on urban arterial roads. The identification of vehicle platoons on urban roads has drawn more attention in recent years. Researchers have been exploring various methods and algorithms to detect and classify platoons, as well as investigating the benefits and implications of their presence on road capacity, safety, fuel consumption, and environmental pollution. The present study formulated a three-step strategy to identify vehicle platoons in the urban road network under heterogeneous traffic conditions. The proposed three steps are recognizing vehicle interaction, the estimation of critical headway, and vehicle platoon identification. Traffic data were collected for 13 h in a six-lane divided urban arterial road using an infrared sensor. A Python program was developed to recognize vehicle platoons. The results revealed that out of a total of 42,500 vehicles observed, 74% of vehicles were in vehicle platoons. The characteristics of the identified vehicle platoons were studied, thus focusing on key aspects such as platoon size, intra-platoon headway, platoon stream speed, and vehicle composition in the platoon. The results revealed a linear relationship between the percentage of vehicles in the platoon and traffic volume. The findings of the study will be beneficial in examining platoon-based data aggregation, the utilization of road capacity, and traffic flow optimization.

Suggested Citation

  • Karthiga Kasi & Gunasekaran Karuppanan, 2024. "Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads," Sustainability, MDPI, vol. 16(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:724-:d:1318981
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    References listed on IDEAS

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    1. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    2. Jiang, Rui & Wu, Qing-Song & Zhu, Zuo-Jin, 2002. "A new continuum model for traffic flow and numerical tests," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 405-419, June.
    3. Gunay, Banihan, 2007. "Car following theory with lateral discomfort," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 722-735, August.
    4. Jiang, Yi & Li, Shuo, 2005. "Characteristics of Vehicle Platoons at Isolated Intersections," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 44(1).
    5. Raffaele Mauro & Andrea Pompigna, 2022. "A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    6. Ruili Wang & Mingzhe Liu & Ray Kemp & Min Zhou, 2007. "Modeling Driver Behavior On Urban Streets," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 903-916.
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