Exploring the influence of contributing factors and impact degree on bus emissions in real-world conditions

Wang, Chao; Ye, Zhirui; Bi, Hui

doi:10.1007/s11356-021-12945-3

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…Wang et al [24] analyzed the factors affecting emissions from city buses. The authors used a backpropagation neural network (BPNN) to analyze multiple factors and concluded that such factors as delay time, location, and stops significantly affect emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

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Section: Literature Reviewmentioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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“…Because of the ability of dealing with multifactor, uncertainty, and nonlinearity problems without prior presuming the data distribution, the back propagation neural network (BPNN) is used here to handle the problem of multiple inputs and a single output with respect to biodiesel IV (Wang et al 2021). BPNN is a multilayer feedforward neural network that makes the predicted output value of the BPNN approach the expected output value by continuously adjusting the network weight and bias through the error of back propagation.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Qualitative and quantitative analysis of the influence of biodiesel fatty acid methyl esters on iodine value

Huang

Bao

et al. 2021

Environ Sci Pollut Res

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Iodine value (IV) is an indicator to evaluate the degree of unsaturation (DU) of biodiesel. It reflects the biodiesel degradation and oxidation stability (OS) and also has an effect on viscosity, low-temperature flow properties (LTFP), and the combustion performance. To construct a theoretical system for the simultaneous optimization of LTFP and OS of biodiesel using IV, 52 measured experimental data are used to investigate the qualitative and quantitative relationship between IV and biodiesel composition. The relationships between biodiesel physicochemical properties and IV are investigated in this work. The qualitative analysis shows that the poly-unsaturated fatty acid methyl esters (FAMEs) contribute to an increase in IV, whereas saturated and mono-unsaturated FAMEs decrease IV. Multiple linear regression (MLR) and artificial neural network (ANN) are used to estimate IV from FAMEs. The correlation coefficient, root mean squared error (RMSE), and mean absolute percentage error (MAPE) are respectively 0.976, 2.45, and 1.76% for the MLR model and 0.983, 2.14, and 1.57% for the back propagation neural network (BPNN) model; these values indicate the high accuracy of these methods. The performances of the proposed models were compared with three existing IV prediction models and validated by another databank. The results indicate that the application of the developed BPNN model is better and more comprehensive. Additionally, a preliminary conclusion is that biodiesel with a low percentage of both long-chain saturated and poly-unsaturated FAMEs can have solidifying point (SP) and OS in the proper range. Biodiesel with a low IV is generally more combustible and efficient.

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“…For the study of bus emissions, Wang C et al [9] analyzed the factors influencing bus emissions and the degree of influence. The results showed that the delay and the bus stop had significant effects on emissions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of carbon emission distribution characteristics of the bus stop area in the net-connected environment

Tian,

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The bus stop area is an important area for vehicles to generate traffic emissions. In order to study the characteristics of vehicle emissions in the bus stop area under the net-connected environment, a mixed traffic flow simulation environment is constructed based on the analysis of the travel behavior of manual driving vehicles, ACC vehicles and CACC vehicles, and the influence of different factors on vehicle carbon emissions in the bus stop area is analyzed by using the car-following model and lane-changing model. Firstly, the full speed difference model, ACC model and CACC model are selected as the car-following models for manual driving vehicles and net-connected vehicles respectively, and the lane-changing model is constructed using the discretionary lane change model and the mandatory lane change model; the VT-Micro model is selected as the carbon emission calculation model; finally, MATLAB is used for numerical simulation to simulate and analyze the traffic emission distribution characteristics of the bus stop area under the net-connected environment. The results show that the carbon emissions in the upstream of the bus stop increase as the proportion of bus increases, the traffic volume increases and the proportion of net-connected vehicle decreases. The carbon emission hotspot was identified as the upstream of the stop near the inner lane. This result can provide a basis for carbon emission management at bus stops in the future.

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Exploring the influence of contributing factors and impact degree on bus emissions in real-world conditions

Cited by 3 publications

References 43 publications

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Qualitative and quantitative analysis of the influence of biodiesel fatty acid methyl esters on iodine value

Analysis of carbon emission distribution characteristics of the bus stop area in the net-connected environment

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