Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models

Jomnonkwao, Sajjakaj; Uttra, Savalee; Ratanavaraha, Vatanavongs

doi:10.3390/su12010395

Cited by 34 publications

(22 citation statements)

References 17 publications

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“…To reduce the number of accidents Jomnonkwao, Uttra, and Ratanavaraha [ 16 ] in their paper provide plan which required future forecast data using regression models. Yuchi et al.…”

Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

186

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Introduction and Aims The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. Material and methods A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. Results Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. Conclusion These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).

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“…To reduce the number of accidents Jomnonkwao, Uttra, and Ratanavaraha [ 16 ] in their paper provide plan which required future forecast data using regression models. Yuchi et al.…”

Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

186

View full text Add to dashboard Cite

show abstract

“…The model works to create a relationship between a few or more input variables and an output variable by fitting a linear mathematical equation to the observed data. MLR is a computationally downscaling technique that is widely used in statistical analysis [40].…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

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“…Highway agencies are interested in provinces with high accident rates and therefore prepare a proactive response to accidents in such provinces. The proactive response includes necessary medical equipment and other necessary support in preparation at high-crash locations [14].…”

Section: Spatial View Of Accident Occurrencesmentioning

confidence: 99%

The Role of Data Visualization and Analytics of Highway Accidents

Sunkpho

Wipulanusat

2020

Walailak J Sci & Tech

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Thailand has been ranked as one of the most dangerous countries in terms of death from road accidents, representing ineffective road safety policies. The crucial mission of the Thai government is to provide safety and reduce accidents for road users on the highway system. This paper aims to explore the potential of using Business Intelligence (BI) in accident analysis. The availability of open accident data provides an opportunity for the BI, which can provide an advanced platform for conducting data visualization and analytics in both spatial and temporal dimensions in order to illustrate when and where the accidents occur. The accident data and provincial data were combined by using the Talend Data Integration tool. The combined data was then loaded into a MySQL database for data visualization using Tableau. The dashboard was designed and created by using Tableau as an analytical visualization tool to provide insights into highway accidents. This system is advised to be adopted by the Thai government, which can be used for data visualization and analytics to provide a mechanism to formulate strategy options and formulate appropriate contingency plans to improve the accident situation.

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Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models

Cited by 34 publications

References 17 publications

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

The Role of Data Visualization and Analytics of Highway Accidents

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