Dynamic Traffic Allocation Model Considering the Effects of Vehicle Emission Diffusion


  • Huan Zhang School of Automotive and Transportation, Xihua University
  • Yuxia Wang School of Automotive and Transportation, Xihua University
  • ChuanHua Zeng School of Automotive and Transportation, Xihua University




vehicle emission, emission diffusion effect, generalised impedance, dynamic traffic allocation


Vehicle exhausts diffuse into roadside crowd breathing zones, thereby jeopardising human health. This study applies dynamic traffic distribution theories to comprehensively consider the impact of vehicle emission diffusion. The results provide a theoretical basis for improving the diffusion of urban traffic pollution to benefit the surrounding environment for roadside crowds. Firstly, a multi-vehicle cellular transport model that is suitable for analysing dynamic traffic distribution is constructed considering the distinct emission factors of various types of vehicles. Secondly, a multi-vehicle emission model is established to consider a range of driving conditions. Then, the concept of roadside crowd exposure risk is introduced, and we describe a method for calculating the total amounts of pollutants emitted by vehicles and inhaled by roadside crowds. The impact of vehicle emission diffusion is comprehensively discussed in terms of vehicle emissions and roadside crowd exposure risk. A generalised impedance function considering the influence of vehicle exhaust emission diffusion is also established based on the weighted average of actual vehicle travel time, multi-model emissions and roadside crowd exposure risk. Finally, this generalised impedance function is integrated into the dynamic optimal user allocation model, and a dynamic traffic allocation model considering the influence of vehicle emission control is developed.



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How to Cite

Zhang, H., Wang, Y., & Zeng, C. (2023). Dynamic Traffic Allocation Model Considering the Effects of Vehicle Emission Diffusion. Promet - Traffic&Transportation, 35(2), 184–194. https://doi.org/10.7307/ptt.v35i2.38