A stochastic model for stop-and-go phenomenon in traffic oscillation: On the prospective of macro and micro traffic flow

Wen, Jianghui; Hong, Lijiang; Dai, Min; Xiao, Xinping; Wu, Chaozhong

doi:10.1016/j.amc.2022.127637

Cited by 14 publications

(7 citation statements)

References 42 publications

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“…Starting from the top root node, all patients are gathered together. After the division of the root node, patients are assigned to different sub-nodes, and then further divided according to the influencing factors in the sub-nodes until all patients are respectively classified into a certain category (namely leaf node) [58,59].…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

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Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established. Then, a two-stage model combining K-means cluster analysis and random forest (RF) is proposed to evaluate and predict the risk of CVD, and the predicted results are compared with the methods of Bayesian discrimination, K-means cluster analysis and RF. The results show that the prediction effect of the proposed two-stage model is better than that of the compared methods. Moreover, several suggestions for the government, the medical industry and the public are provided based on the research results.

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Section: Random Forestmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

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“…In terms of driving factors decomposition of carbon emission, the existing decomposition methods include impact (I) affected by population (P), affluence level (A), and technology (T) (IPAT) (Yue et al 2013 ); index decomposition analysis (IDA) (Wang et al 2022 ; Yin and Mao 2023 ); logarithmic mean Divisia index (LMDI) (Gao and Zhu 2020 ; Rao et al 2022b ); GDIM (Liu et al 2022b ; Wen et al 2023 ); and STIRPAT (Huang et al 2021 ). Based on these methods, an extended LMDI model with scale effect of scientific research was established, and the results showed that improving scientific research efficiency and optimizing industrial structure can effectively reduce carbon emissions (Gao et al 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

Rao

Huang

Chen

et al. 2023

Environ Sci Pollut Res

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The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province’s carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry’s cleaner production and sustainable development of society.

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“…The third class of models is hybrid models of traffic flows. Hybrid models are devoted to papers [44][45][46][47][48][49][50]. These models combine macro and micro models' capabilities.…”

Section: Hybrid Models Of the Traffic Flowsmentioning

confidence: 99%

“…Such models are designed based on neural networks [45] with self-learning and self-control functions. A hybrid model is proposed to investigate the stopping and driving phenomenon caused by passing vehicles in front, combining capabilities at macro and micro levels [46]. First, under the stochastic nature of driving behavior, Brownian noise is added to the speed difference to modify the model of vehicle passing.…”

Section: Hybrid Models Of the Traffic Flowsmentioning

confidence: 99%

Modeling of Traffic Flows Sustainability on Highway Network Stretches

Vojtov,

Muzylyov,

Karnaukh

et al. 2023

Applied Sciences

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Assessing the transport flow robustness is a significant aspect of a qualitative solution to traffic management problems. Therefore, management should be based on appropriate criteria, accounting for different factors characterizing traffic flow sustainability. That’s why it is crucial to establish the impact rate for each group of factors on the robustness criterion. Therefore, the current study aims to obtain the dependence of the criterion changes for traffic flow sustainability on the traffic jam occurrence when changing the gradients’ product of traffic flow density and its speed. The value of the robustness criterion allows for performing an impact rating for input factors on traffic flow sustainability. All factors affecting transport flow robustness are divided into three groups. Based on simulation results, factors rating that impact the robustness margin value of the traffic flow is presented. Length and weight of automobiles are at first place according to impact terms on the sustainability loss of the traffic flow. In second place of impact on sustainability loss are the temporary factors group and factors group that considers the roadway environment’s infrastructure. Hence, the results can be used to analyze sustainability traffic flows in controlled highway network stretches and develop measures to increase sustainability reserve.

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A stochastic model for stop-and-go phenomenon in traffic oscillation: On the prospective of macro and micro traffic flow

Cited by 14 publications

References 42 publications

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

Modeling of Traffic Flows Sustainability on Highway Network Stretches

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