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Investigation of PM emissions in cellular automata model with slow-to-start effect

Author

Listed:
  • Qiao, Yan-feng
  • Xue, Yu
  • Wang, Xue
  • Cen, Bing-ling
  • Wang, Yi
  • Pan, Wei
  • Zhang, Yan-xin

Abstract

In this paper, combining with the empirical particulate emission model, we studied Particulate Matter (PM) emission of two typical cellular automata traffic models (VDR and TT models) with slow-to-start rules under periodic boundary condition and open boundary condition. Numerical simulations show the two slow-to-start models have different emissions under the same initial and boundary conditions. Under periodic boundary condition, the metastable traffic flow emits the most particulate matter for VDR model, and the traffic flow in decelerated state emits the most. For TT model, the vehicles with the most emissions are those in the state of traffic congestion and deceleration. Moreover, the emission characteristics of particulate matter in the two models are analyzed and compared. Under open boundary condition, the resulting phase diagram and flux diagram to reflect traffic congestion are obtained. Research shows that the injection probability and removal probability have a great impact on PM emissions. Numerical simulation displays that the trend of particulate emission varies with the different state of vehicle movements in spite of the same removal probability. For different removal probabilities, different maximum particle concentrations are emitted from traffic flows in the same movement state. Moreover, the emission of VDR model is significantly different from that of TT model in vehicle deceleration state.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002685
    DOI: 10.1016/j.physa.2021.125996
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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).

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