Decreasing Trends in Road Traffic Mortality in Poland: A Twenty-Year Analysis

The temporal trend of road traffic mortality in China from 2004 to 2020

Background

Road traffic accidents are one of the critical disasters that cause massive negative influences to the social economy and people's property, health and life safety. The purpose of this study is to analyze the temporal trend of road traffic mortality in China from 2004 to 2020, and further analyze the key factors that the influencing changes in China's road traffic mortality and provides information for the development of targeted interventions to reduce the number of preventable deaths.

Methods

The data were retrieved from the Chinese Death Cause Monitoring Data Set from 2004 to 2020. The road traffic mortality rates were standardized by the Sixth National Population Census (6th NPC) data. Joinpoint regression method was manipulated to analyze temporal trends of standardized road traffic mortality. The study used annual percentage change (APC) and average annual percentage change (AAPC) calculated by the Joinpoint regression model to describe trends in road traffic mortality rates.

Results

The overall age-adjusted road traffic mortality in China showed a declining trend from 2004 to 2020 (AAPC2004-2020 = - 4.2%), from 20.9 to 12.92 per 100,000. Rural road traffic mortality rates were generally higher than the one in urban areas, and males were generally higher than females. There was an overall downward trend of the standardized road traffic mortality in the East and Central regions between 2004 and 2020. It was worth noting that the road traffic mortality rates in the Western region showed an upward trend from 2006 to 2011 (APC2006-2011 = 3.3%) and continued to decline after 2011 (APC2011-2020 = - 6.7%). The road traffic mortality rates of aged 65 years and older was highest, which required focused attention.

Conclusions

From 2004 to 2020, the road traffic mortality rates in China generally declined. At the same time, there was a slow reduction or even an upward trend in road traffic mortality rates among the elderly and in western regions. Rural males are a priority group for road traffic injury prevention.

Forecasting time trends of fatal motor vehicle crashes in Iran using an ensemble learning algorithm

OBJECTIVE

This study aimed to introduce the random forest (RF) method as a valuable tool for short-term crash frequency prediction. Besides, the study compares the forecast efficiency of the RF model with the classical seasonal autoregressive integrated moving average (SARIMA) model in the multivariate time-series analysis of crash counts.

METHODS

To this end, fatal accidents reported by the police and intercity traffic flow extracted from the loop detectors were aggregated in intercity highways at the country's level monthly from Farvardin 1395 to Mordad 1400. The first 55 data points were used as the training sample, and the remaining ten months were considered the test sample. The Box-Jenkins and random forest machine learning methods were employed for short-term crash frequency prediction. The mean absolute percentage error (MAPE) criterion was utilized to compare the forecast accuracy of the developed models.

RESULTS

The performance of the random forest model (MAPE = 2.6) with the exogenous variables of traffic flow, crash year, and month outperformed the best SARIMA (1,0,0) (1,0,0)12 model (MAPE = 5.7) with traffic flow as the regressor.

CONCLUSIONS

This study suggests that the random forest as an ensemble learning algorithm is a better crash prediction tool compared to the classical Box-Jenkins method, accounting for the non-linear dependencies in crash count time-series. Besides, the results illustrate that the multivariate SARIMA (SARIMAX) model significantly outperforms its univariate counterpart, accounting for the simultaneous impacts of exogenous variables.