Pengaruh Harga Bensin Terhadap Kecelakaan Lalu Lintas Di Indonesia

The Impact of Gasoline Price on Trac Accident in IndonesiaTraffic accident ranks the ninth largest of the cause of death in Indonesia. The most of researches studying Indonesia on traffic accidents were only blaming on human, motor vehicles, and environment as main culprits, not incorporating economic factors into the models. This study aims to analyze the impact of real gasoline prices on trac accident in Indonesia and the factors of influence them. This research employs time series data from 1970 to 2013 with OLS analysis world crude oil prices as instrument variable. The estimator results show that real price of gasoline and the policy of USAge of motorcycle light insignificant on traffic accident. Meanwhile, real GDP and asphalt roads significantly decrease the traffic accident. However, motorcycles significantly increase the traffic accident

Impact Evaluation of the Louisville-Shively-Jefferson County Traffic Alcohol Programs

This report is an evaluation of alcohol enforcement programs conducted by the Louisville, Jefferson County, and Shively police agencies in the Louisville metropolitan area. The following four types of data were collected in order to evaluate the traffic alcohol programs; accident data, arrest and adjudication data, cost-effectiveness, and public opinion data. Results from the before-and-after comparisons and time-series analysis show alcohol-related accidents decreased significantly during the study period, There was a 34.4 percent reduction in alcohol-related accidents during hours of special enforcement and a 30.4 percent reduction during all hours of the day. Time-series analysis of accident data showed a 27.1 percent decrease during hours of increased enforcement and a 26.1 percent decrease during all hours. Results from time-series analysis also indicated that the enforcement programs increased the DUI arrest rate by at least 50 percent in each of the jurisdictions studied. Inclusion of the Slammer Law as a control variable revealed the proportion of convictions among DUI arrests increased by nearly 449 percent. Based on costs associated with the program (enforcement, jail costs, and court costs) and benefits (reduced accident costs and DUI fines); the benefit-cost ratio was 2.81 to 5.67 depending upon the basis for accident costs. The public opinion survey showed strong support for the traffic alcohol programs and 87 percent of the respondents indicated that increased enforcement was an effective means of reducing drinking and driving. In addition, 82 percent of those responding indicated the programs had reduced their chances of an accident

Road Traffic Accident Variations in Lagos State, Nigeria: A Synopsis of Variance Spectra

The major objective of this research is to examine the variation patterns of road traffic accident in Lagos State. The study used mostly secondary data; accident records and vehicular situation were obtained from the Nigeria police force and Federal Road Safety Commission. The data were obtained for a period of thirty two (32) years from 1970-2001. The analysis of the number and type of vehicles involved in road traffic accidents revealed that private cars, buses and taxis were more prone to accidents in Lagos State. The 16 harmonics for the selected L.G.A’s considered contribute above 90% of the total variance in the time series. This means that more than 90% of road traffic accident in Lagos State could be attributed to recklessness on the part of drivers, ignorance of high way codes, over speeding etc. Also, the dominant cycles of road traffic accidents observed in the study area have periodicities of 32.00 and 16.00 years with the most dominant being 32years. This means that the dominant and strongest road traffic accident pattern of Lagos State repeats itself every 32years. Based on the findings, recommendations were proffered.Keywords: Accident; traffic; variations; variance spectra; Lagos Stat

Time series count data models: an empirical application to traffic accidents

Count data are primarily categorised as cross-sectional, time series, and panel. Over the past decade, Poisson and Negative Binomial (NB) models have been used widely to analyse cross-sectional and time series count data, and random effect and fixed effect Poisson and NB models have been used to analyse panel count data. However, recent literature suggests that although the underlying distributional assumptions of these models are appropriate for cross-sectional count data, they are not capable of taking into account the effect of serial correlation often found in pure time series count data. Real-valued time series models, such as the autoregressive integrated moving average (ARIMA) model, introduced by Box and Jenkins have been used in many applications over the last few decades. However, when modelling non-negative integer-valued data such as traffic accidents at a junction over time, Box and Jenkins models may be inappropriate. This is mainly due to the normality assumption of errors in the ARIMA model. Over the last few years, a new class of time series models known as integer-valued autoregressive (INAR) Poisson models, has been studied by many authors. This class of models is particularly applicable to the analysis of time series count data as these models hold the properties of Poisson regression and able to deal with serial correlation, and therefore offers an alternative to the real-valued time series models. The primary objective of this paper is to introduce the class of INAR models for the time series analysis of traffic accidents in Great Britain. Different types of time series count data are considered: aggregated time series data where both the spatial and temporal units of observation are relatively large (e.g., Great Britain and years) and disaggregated time series data where both the spatial and temporal units are relatively small (e.g., congestion charging zone and months). The performance of the INAR models is compared with the class of Box and Jenkins real-valued models. The results suggest that the performance of these two classes of models is quite similar in terms of coefficient estimates and goodness of fit for the case of aggregated time series traffic accident data. This is because the mean of the counts is high in which case the normal approximations and the ARIMA model may be satisfactory. However, the performance of INAR Poisson models is found to be much better than that of the ARIMA model for the case of the disaggregated time series traffic accident data where the counts is relatively low. The paper ends with a discussion on the limitations of INAR models to deal with the seasonality and unobserved heterogeneity

Impact Evaluation of the Traffic Alcohol Program in Lexington, Kentucky

This report is an evaluation of increased police enforcement to reduce alcohol-related accidents in Lexington-Fayette County, Kentucky. Three types of data were coIlected as a means of evaluating the Traffic Alcohol Program; accident data, arrest and adjudication data, and cost-effectiveness data. Results from before-and-after comparisons and time-series analysis show alcohol related accidents decreased significantly during the study period. When comparing two years before with three years during the increased enforcement, the percent reduction in alcohol-related accidents was 28.1 percent using standard before and after analysis and 29.1 percent using time-series analysis. During the same time period, alcohol related fatal or injury accidents decreased 26.7 percent. Arrests have averaged 3,686 per year for the three years of increased enforcement as compared to 929 the year before. The DUI conviction rate has remained at approximately 90 percent throughout the program. Based on costs associated with the program (enforcement, jail costs, and court costs) and benefits (reduced accident costs, fines for DUI, and fines for other offenses); the benefit-cost ratio was 3.71

An Evaluation of the "Belia Di Jalan Raya" Road Safety Programme

In November 1996, a road safety programme known as the Belia di Jalan Raya road safety programme was carried out in Hulu Langat. The main objective of this programme was to reduce traffic accidents and injuries, especially among the motorcyclists.This thesis presents the impact of the one year safety programme in the Hulu Langat district. It provides an integrated approach to address accident problems by the enhancement of traffic enforcement, education and public information. The accident data were collected 23 months before and 12 months after the programme, in two comparison locations, Hulu Langat and Shah Alam. Shah Alam was used as a matched-pair control in this analysis. The Before and After analysis and the Box-Jenkins time series modeling technique were used to evaluate the effectiveness of the safety programme. Using the Before and After Chi-square test, it was found that the proportion of accidents in these two districts are Significantly different. This implies that the safety programme has a Significant effect on the reduction in the number of accidents, injuries and fatalities in Hulu Langat. The Box-Jenkins Time Series analysiS indicates an average reduction of 57 accidents, 8 hospitalized non-motorcyclist casualties, 15 non-motorcyclist fatalities, 18 hospitalized motorcyclists, 3 motorcyclist deaths, 3 young hospitalized non-motorcyclist casualties and 1 fatal motorcyclist in every month during the study period. In contrast, there had been an average increase in accidents and casualties in the comparison district, Shah Alam. The Survival Analysis shows that there was a significant improvement in overall traffic safety status in Hulu Langat after the implementation of the safety programme. In contrast, changes in traffic safety status in the comparison location, Shah Alam, did not resemble improvement in Hulu Langat in direction and magnitude

Road traffic mortality attributable to alcohol in Russia

Background: The road accident mortality rates in Russia are the highest in Europe. The growing evidence suggests that drunk driving is the leading cause of fatal road accidents in Russia. ^ Objectives: The aim of the present study was to evaluate the effect of changes in aggregate-level alcohol consumption on fatal road traffic accidents in Russia between 1970 and 2015. Methods: Age-standardized sex-specific male and female traffic accidents mortality data for the period 1970-2015 and data on alcohol consumption per capita were analyzed by means ARIMA (autoregressive integrated moving average) time series analysis. Results: Alcohol consumption was significantly associated with both male and female traffic accidents mortality rates: a 1 liter increase in overall alcohol consumption would result in a 3.5% increase in the male accident mortality rate and in 2.1% increase in the female mortality rate. The results of the analysis suggest that 38.3% of all male accident deaths and 25.2% female deaths in Russia could be attributed to alcohol. Conclusions: This is the first time-series analysis of overall level of alcohol consumption and road traffic mortality rates in Russia, which has shown that population drinking, is the strong predictor of road traffic fatalities at the aggregate level

Trend and Seasonality in Fatal Road Accidents in the U.S. States in 2006–2016

Understanding the dynamics of the daily number of fatal road traffic accidents is important for local authorities, police departments, healthcare facilities and insurance companies, enabling them to design preventive measures, provide appropriate emergency service and care and reliably estimate traffic accident insurance costs. In the present study, using the Fatality Analysis Reporting System provided by the U.S. National Highway Traffic Safety Administration, we construct a daily time series of the number of accidents for each state of the United States. We model the trend as well as yearly and weekly seasonality present in the time series and provide respective trend and seasonality statistics. Differences in accident rates and yearly seasonality between states were detected, clustering analysis being applied to identify clusters of states with similar yearly seasonality, weekly seasonal patterns for different states proving to be about the same

Impact Evaluation of the Lexington-Fayette County Traffic Alcohol Program (1982-1986)

This report is a final evaluation of four years of increased police enforcement to reduce alcohol-related accidents in Lexington-Fayette County, Kentucky. Three types of data were collected as a means of evaluating the Traffic Alcohol Program; accident data, arrest and adjudication data, and cost-effectiveness data. Results from before-and-after comparisons and time-series analysis show alcohol-related accidents decreased significantly during the study period. When comparing two years before with four years during the increased enforcement, the reduction in alcohol-related accidents during hours of increased enforcement was 37.3 percent using standard before and after analysis and 36.4 percent using time-series analysis. For all hours of the day, alcohol-related accidents decreased by 30.3 percent. During the same time period, alcohol-related fatal or injury accidents decreased 29.1 percent. Arrests have averaged 3,220 per year for the four years of increased enforcement as compared to 929 the year before. The DUI conviction rate has remained at approximately 90 percent throughout the program. Based on costs associated with the program (enforcement, jail costs, and court costs) and benefits (reduced accident costs, fines for DUI, and fines for other offenses); the benefit-cost ration was 3.81. If reduced accident costs were eliminated and only direct income was used as benefits, then the benefit-cost ratio would be 1.20

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research

This research investigates road traffic accident severity in the UK, using a combination of machine learning, econometric, and statistical methods on historical data. We employed various techniques, including correlation analysis, regression models, GMM for error term issues, and time-series forecasting with VAR and ARIMA models. Our approach outperforms naive forecasting with an MASE of 0.800 and ME of -73.80. We also built a random forest classifier with 73% precision, 78% recall, and a 73% F1-score. Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for Explainable AI, highlighting influential factors like Driver_Home_Area_Type and Road_Type. Our study enhances understanding of accident severity and offers insights for evidence-based road safety policies.Comment: 3