Time series modeling in traffic safety research

Lavrenz, Steven; Vlahogianni, Eleni I.; Gkritza, Konstantina; Ke, Yue

doi:10.1016/j.aap.2017.11.030

Cited by 60 publications

(28 citation statements)

References 92 publications

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“…Andreii and Bugudui (2011) has presented an econometric modeling of GDP time series in the US economy. Other similar forecasting models using residual analysis were found in Okyere et al (2015), Boshnakov (2016) and Lavrenz et al (2018). Martin et al (2017) showed checking regression assumptions (errors normality, constant variance, residual analysis plots) in regression analysis fitting experimental data via residual plots.…”

Section: Introductionmentioning

confidence: 57%

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Residual Analysis for Auto-Correlated Econometric Model

Elsayir

2019

Journal of Mathematics and Statistics

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The aim of this article is to provide residual analysis for a time series data of Gross Domestic Product (GDP) of the Sudan. An econometric time series model with macroeconomic variables is conducted to examine the goodness of fit using residual. Many statistical tests are used in time series models in order to make it a stationary series. After applying these tests, the time series became stationary and integrated; thus, Box-Jenkins procedure is used for the determination of ARIMA, AR (0,1,0) in this study. This identified technique is useful for analyzing this study.

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Section: Introductionmentioning

confidence: 57%

“…If the points in a residual plot indicates appropriateness of a linear or non-linear regression model for the data, Lavrenz et al (2018) The residuals plot shows a certain pattern. Random pattern might be an indication of goodness of fit to the data of a linear model.…”

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confidence: 99%

Residual Analysis for Auto-Correlated Econometric Model

Elsayir

2019

Journal of Mathematics and Statistics

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“…Andreii and Bugudui [6] have presented an econometric modeling of GDP time series in the US economy. Other similar forecasting models using residual analysis were found in Okyere et al [7], Boshnakov [8] and Lavrenz et al [9]. Martin et al [10] showed checking regression assumptions (errors normality, constant variance, residual analysis plots) in regression analysis fitting experimental data via residual plots.…”

Section: Introductionmentioning

confidence: 65%

“… Examining residuals plots to check appropriateness of regression model for the data [11].  If the points in a residual plot indicates appropriateness of a linear or non-linear regression model for the data, Lavrenz et al [9].…”

Section: Residuals Analysismentioning

confidence: 99%

Residual Analysis for Auto-Correlated Econometric Model

Elsayir

2019

OJS

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“…The use of ITS in motor-vehicle-crash analysis includes the effect of high fuel price (Naqvi et al 2020), ban on cellphone use (Liu et al 2019), the effect of 20 mph traffic speed zones on crashes (Grundy et al 2009), and the effect of change in street lightning on crashes and crimes (Steinbach et al 2015). Lavrenz et al (2018) reviewed time series modeling in traffic safety research. Although many studies have been conducted on motor vehicle crashes using ITS, including those mentioned above, limited studies have examined the impact of earthquake on motor vehicle crashes using ITS.…”

Section: Interrupted Time Series Analysis For Impact Evaluationmentioning

confidence: 99%

Increased motor vehicle crashes following the 2016 Kumamoto earthquake, Japan: an interrupted time series analysis of property damage crashes

Maruyama

Taguchi

2021

Nat Hazards

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Driving after natural disasters entails a substantial amount of stress; therefore, the number of motor vehicle crashes may increase. However, few studies have examined this issue. This study investigated motor vehicle crashes after the 2016 Kumamoto earthquake in Japan. Monthly data about crashes resulting in property damage from 49 municipalities in Kumamoto from 2015 to 2018 were used. An interrupted time series analysis using Poisson or negative binomial regression models was conducted for 49 municipalities; the models were estimated for four classified areas to obtain the robust results. We found that property damage crashes increased significantly in the heavily affected area (Relative Risk (RR) = 1.48, 95% Confidence interval (CI): 1.29, 1.71) and the affected area (RR = 1.25, 95% CI: 1.15, 1.36) after the earthquake. A mountainous area showed a reduction in property damage crashes despite its heavy damage (RR = 0.74, 95% CI: 0.67, 0.82), which can be attributed to the closure of its main gate routes. The unaffected area showed no difference before and after the earthquake. Geographical presentation of the result demonstrates a clear positive association of earthquake damage and increased crashes. The findings of this study highlight the importance of motor-vehicle-crash alerts after an earthquake.

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Time series modeling in traffic safety research

Cited by 60 publications

References 92 publications

Residual Analysis for Auto-Correlated Econometric Model

Residual Analysis for Auto-Correlated Econometric Model

Residual Analysis for Auto-Correlated Econometric Model

Increased motor vehicle crashes following the 2016 Kumamoto earthquake, Japan: an interrupted time series analysis of property damage crashes

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