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Study on pollutant emissions of mixed traffic flow in cellular automaton

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  • Wang, Xue
  • Xue, Yu
  • Cen, Bing-ling
  • Zhang, Peng
  • He, Hong-di

Abstract

Based on mixed NaSch traffic flow model and the empirical formula of automobile exhaust emissions, emissions of particulate matter (PM), CO2, NOx and volatile organic compounds (VOC) are investigated with consideration of the different movement conditions, mixing ratios, the maximum velocity and lengths of vehicle. Pollutant emissions are determined by traffic characteristics from free-flow to congestion. Emissions of mixed traffic increase with the mixing ratio increasing. The impact of traffic state on emissions is discussed in detail by introducing the distribution functions for three types of motion states. Results show that decelerating vehicles discharge the most pollutants even if accelerating and decelerating vehicles in the congested state have a great impact on pollutant emissions in the mixed traffic flow. The effect of the maximum velocity and the length of vehicles on emission are investigated, respectively. It is found that the maximum velocity of the short vehicles has a significant impact on emissions when the mixing ratio and length of vehicles are fixed. The larger the maximum velocity of the short vehicles, the more the emissions is, but the maximum velocity of long vehicles does not display obvious effect. When the mixing ratio is given, the longer the vehicle, the less the emissions are due to restrictions on the loop road.

Suggested Citation

  • Wang, Xue & Xue, Yu & Cen, Bing-ling & Zhang, Peng & He, Hong-di, 2020. "Study on pollutant emissions of mixed traffic flow in cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315316
    DOI: 10.1016/j.physa.2019.122686
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    References listed on IDEAS

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    Cited by:

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    2. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Yanxin Zhang & Yu Xue & Yanfeng Qiao & Bingling Cen, 2022. "Analytical Solution of the Mixed Traffic Flow Cellular Automaton FI Model with the Next-Nearest-Neighbor Interaction," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    4. Qiao, Yanfeng & Xue, Yu & Cen, Bingling & Zhang, Kun & Chen, Dong & Pan, Wei, 2024. "Study on particulate emission in two-lane mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    5. Qiao, Yan-feng & Xue, Yu & Wang, Xue & Cen, Bing-ling & Wang, Yi & Pan, Wei & Zhang, Yan-xin, 2021. "Investigation of PM emissions in cellular automata model with slow-to-start effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    6. Ma, Ke & Wang, Hao & Ruan, Tiancheng, 2021. "Analysis of road capacity and pollutant emissions: Impacts of Connected and automated vehicle platoons on traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    7. Pérez Cruz, José Roberto & Lakouari, Noureddine & Marzoug, Rachid & Pérez Sansalvador, Julio César, 2023. "Pedestrian–vehicle interactions at unsignalized mid-block crosswalks: Effects on traffic flow, CO2 emissions, and energy dissipation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Mohammad Maghrour Zefreh & Adam Torok, 2021. "Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Environmental External Costs," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    9. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui, 2020. "Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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