Gasoline prices and their relationship to the number of fatal crashes on U.S. roads

Safaei, Nima; Zhou, Chao; Safaei, Babak; Masoud, Ahmad A.

doi:10.1016/j.treng.2021.100053

Cited by 22 publications

(6 citation statements)

References 35 publications

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“…Different variations of linear regression models (Polynomial, Quantile, Elastic Net, Poison, Negative Binomial, Ordinal, Principal Components, Lasso, Ridge, etc) have successfully been used for creating robust prediction models with comparable performance to non-linear models 29 . Lasso and Ridge Regression are two distinct methods of reducing the variance of linear regression models by shrinking their coefficients.…”

Section: Lasso and Ridge Regressionmentioning

confidence: 99%

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

Srivastava¹,

Safaei²,

Khaki³

et al. 2021

Preprint

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Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

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Section: Lasso and Ridge Regressionmentioning

confidence: 99%

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

Srivastava¹,

Safaei²,

Khaki³

et al. 2021

Preprint

Self Cite

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“…Prior empirical research on the impact of gasoline prices and other economic factors on road safety outcomes utilized data from a single country or multiple countries. With the exception of the study by Naqvi et al (2020), most studies on the effect of gasoline price on crashes focused on the United States (Chi et al, 2011(Chi et al, , 2012(Chi et al, , 2013(Chi et al, , 2015Safaei et al, 2021). Grabowski and Morrisey (2004) found that in the short run, for a 10% decrease in real gasoline price, the fatality rate increased by 1.6% in the United States.…”

Section: Introductionmentioning

confidence: 99%

“…The impact of an increase in gasoline price was higher on younger drivers and female drivers. An increase in gasoline price leads to a reduction in fatal non-motorcycle crashes and an increase in fatal motorcycle crashes (Safaei et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Short‐ and long‐run impacts of gasoline price and macroeconomic factors on road traffic safety in Nigeria

Akinyemi

2023

African Development Review

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This paper examined the short‐ and long‐run impacts of gasoline price, macroeconomic factors and road length on road traffic crashes, injuries and fatalities in Nigeria. Annual data from 1995 to 2019 and autoregressive distributed lag approach were employed. Results suggest that gasoline price, per capita income, road length and population density significantly influence road safety outcomes. Gasoline price has a positive short‐run impact on crashes, injuries, and fatalities contrary to findings in developed countries. Road crashes tend to decrease when income increases in the short term. Population density leads to improvement in road safety outcomes while road length exacerbates it. Government's policy on gasoline price increase could worsen road safety outcomes unless it is accompanied by improvement in road infrastructure, safe public transport and economic growth,

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“…Due to instability in the prices of petroleum products in the global market and need to minimize environmental pollution such as noise and greenhouse emission generated by IC engine [30], this study seeks to examine the effects of baffle holes on the fluid flow characteristics and forces on the baffle walls. These affect the fuel consumption and noise generated by the engine as well as muffler durability.…”

mentioning

confidence: 99%

Numerical Investigation of the Effect of Muffler Geometry on Engine Performance

Ibitoye¹,

Adegun²,

Sanni³

2022

JMechE

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Muffler is a part of an exhaust system fitted to IC engines for damping noise and to convey hot gases from the combustion chamber. Several research efforts have been put into the study of muffler. This is due to their significant effects on noise reduction, fuel consumption, efficiency and life span of engine. Good muffler minimizes noise, back pressure and engine fuel consumption. Currently, the price of petroleum products in the global market is high and very unstable. Therefore, conservation of fuel is very important, particularly to the end user. This study seeks to examine the effects of baffle holes on the fluid flow characteristics and forces on the baffle walls. Three muffler models were designed using Autodesk Investor 2015. ANSYS 16.0 was used as the CFD working tools to predict the flow characteristics and forces on the walls. Samples A, B and C were modeled to have 7, 13 and 26 holes, respectively while the diameter of the holes for samples A, B and C were 50, 37.5 and 25 mm, respectively. Navier-Stokes and energy transport equations govern the three muffler models. Muffler sample A displayed the lowest backpressure of 20.235 Pa, average wall temperature of 986.311 K and best fuel efficiency characteristics. Sample B had the lowest forces on the wall while Muffler C has the least advantage when compared with other models. Muffler sample B gave the optimum design characteristic considering fuel consumption engine efficiency and muffler durability.

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Gasoline prices and their relationship to the number of fatal crashes on U.S. roads

Cited by 22 publications

References 35 publications

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

Short‐ and long‐run impacts of gasoline price and macroeconomic factors on road traffic safety in Nigeria

Numerical Investigation of the Effect of Muffler Geometry on Engine Performance

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