Comparison of Support Vector and Non-Linear Regression Models for Estimating Large-Scale Vehicular Emissions, Incorporating Network-Wide Fundamental Diagram for Heterogeneous Vehicles

Saedi, Ramin; Verma, Rajat; Zockaie, Ali; Ghamami, Mehrnaz; Gates, Timothy J.

doi:10.1177/0361198120914304

Cited by 18 publications

(22 citation statements)

References 36 publications

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“…On the basis of COVID-19 data from January and February 2020 and floating population data from January, February and August 2020, COVID-19 infection areas were divided, and positive and negative sample construction data sets were built. Since the nine spatial factors used in this study may show multicollinearity, which will cause a serious deviation in the operation results of the LR model, collinearity diagnosis of different factors should be carried out first (Saedi et al, 2020). The product of tolerance (TOL) and the variance inflation factor (VIF) is equal to 1, which is also a common indicator that reflects the degree of collinearity of factors.…”

Section: Logistic Regression Model Trainingmentioning

confidence: 99%

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

Zhou

Wang

et al. 2021

Front. Environ. Sci.

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COVID-19 is a highly infectious disease and public health hazard that has been wreaking havoc around the world; thus, assessing and simulating the risk of the current pandemic is crucial to its management and prevention. The severe situation of COVID-19 around the world cannot be ignored, and there are signs of a second outbreak; therefore, the accurate assessment and prediction of COVID-19 risks, as well as the prevention and control of COVID-19, will remain the top priority of major public health agencies for the foreseeable future. In this study, the risk of the epidemic in Guangzhou was first assessed through logistic regression (LR) on the basis of Tencent-migration data and urban point of interest (POI) data, and then the regional distribution of high- and low-risk epidemic outbreaks in Guangzhou in February 2021 was predicted. The main factors affecting the distribution of the epidemic were also analyzed by using geographical detectors. The results show that the number of cases mainly exhibited a declining and then increasing trend in 2020, and the high-risk areas were concentrated in areas with resident populations and floating populations. In addition, in February 2021, the “Spring Festival travel rush” in China was predicted to be the peak period of population movement. The epidemic risk value was also predicted to reach its highest level at external transportation stations, such as Baiyun Airport and Guangzhou South Railway Station. The accuracy verification showed that the prediction accuracy exceeded 99%. Finally, the interaction between the resident population and floating population could explain the risk of COVID-19 to the highest degree, which indicates that the effective control of population agglomeration and interaction is conducive to the prevention and control of COVID-19. This study identifies and predicts high-risk areas of the epidemic, which has important practical value for urban public health prevention and control and containment of the second outbreak of COVID-19.

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Section: Logistic Regression Model Trainingmentioning

confidence: 99%

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

Zhou

Wang

et al. 2021

Front. Environ. Sci.

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“…The coefficients are estimated using the Lasso method, i.e. by finding the minimum value of equation (2).…”

Section: Lasso Modelmentioning

confidence: 99%

“…Since China's water pollution prevention and control work, water pollution management has been effective, but there are still many problems, such as small-scale water pollution management, resulting in serious water pollution; water quality is unstable, some rivers water quality deterioration; urban sewage treatment capacity is insufficient, sewage directly into the river; industrial waste high cost of water treatment, low cost of pollution; dense population, limited environmental capacity; public awareness of environmental protection is not strong, public participation channels The public is not aware of environmental protection and the channels for public participation are not smooth. In the context of China's current ecological civilization construction, the integration of water pollution prevention and control into national economic and social development planning has become one of the indispensable components of China's socio-economic development [1][2].…”

Section: Introductionmentioning

confidence: 99%

Linear Regression for Water Pollution Control Planning under Support Vectors

2021

WPPCP

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China is facing a serious situation in water pollution prevention and control, and the uncertainty of regional development should be fully considered when formulating water pollution prevention and control planning, and its scientificity should be improved through effective mathematical methods. Water resources and water environment are closely related to human health, and China has not yet established a comprehensive set of water resources and environment monitoring network, so the water pollution prevention and control planning process is crucial to the implementation of water pollution control planning. Due to the relatively low level of economic development in China, the state attaches less importance to water pollution prevention and control issues, and therefore also has a certain impact on the water environment. In order to reduce the problems caused by water pollution, the Water Resources Management Committee has organised the preparation of water pollution control plans. This paper presents an empirical analysis of the support vector model for water pollution prevention and control planning in China. The results of the study show that the introduction of the support vector approach to water pollution prevention and control planning in China can effectively improve the scientific nature of the planning. This paper firstly introduces the support vector model, which is a multivariate model designed based on the support vector method, and can be used to analyse multiple indicators and conduct comprehensive analysis in the prediction process; finally, the planning scheme should be designed with full consideration of water pollution prevention and control planning to avoid conflicts between different water resources and environmental issues. However, there are many problems with the use of the support vector approach in water pollution prevention and control planning that warrant further research.

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“…A relationship between the network-wide weighted average of traffic flow and density is used to define the NFD. These variables are calculated as the space-mean weighted averages of the link flows and densities, with link weights equal to the product of link length and number of lanes ( 33 , 36 – 38 ).…”

Section: Mesoscopic Simulation Frameworkmentioning

confidence: 99%

Impacts of Connected and Autonomous Vehicles on Traffic Flow with Heterogeneous Drivers Spatially Distributed over Large-Scale Networks

Fakhrmoosavi

Saedi

Zockaie

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.

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Comparison of Support Vector and Non-Linear Regression Models for Estimating Large-Scale Vehicular Emissions, Incorporating Network-Wide Fundamental Diagram for Heterogeneous Vehicles

Cited by 18 publications

References 36 publications

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

Linear Regression for Water Pollution Control Planning under Support Vectors

Impacts of Connected and Autonomous Vehicles on Traffic Flow with Heterogeneous Drivers Spatially Distributed over Large-Scale Networks

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