Road Investment and Traffic Safety: An International Study

Investment in roads and traffic safety: linked to economic development? A European comparison

This study analyzes how economic resources invested in roads may affect mortality, depending on the level of economic development of a country. To this end, 23 European countries were classified into two groups-high-income countries and low-income countries-according to their average Gross Domestic Product (GDP) per capita over the period 1998-2016. The economic resources are considered through the investment in construction and the maintenance expenditure. Further variables are included to control for several factors related to the infrastructure, socioeconomics, legislation, and meteorology. Fixed-effects panel data models were built separately for the interurban road network of each group of countries. These models also capture the international inequalities within each group and the country-specific national trend for the study period. The main results indicate a reduction effect on the fatality rate of road maintenance expenditure (in both groups), and of the investment in construction (in the low-income countries). Other variables-such as proportion of motorways, motorization rate, unemployment rate, GDP per capita, alcohol consumption, Demerit Point System, and mean annual precipitation-showed statistically significant results as well. Finally, the country-specific fixed effects and the country-specific trend were mapped geographically, to better reflect national conditions for achieving lower fatality rates in the high-income countries, and greater progress in reducing fatalities in the low-income countries. In the end, this study provides evidence to policy-makers that can help to achieve a safer and more sustainable transport system, namely, how to tackle an ongoing major problem-traffic-related deaths-when attending and allocating the economic resources that road infrastructure needs.

Lives Saved in Low- and Middle-Income Countries by Road Safety Initiatives Funded by Bloomberg Philanthropies and Implemented by Their Partners between 2007-2018

Over the past 12 years, Bloomberg Philanthropies (BP) and its partner organisations have implemented a global road safety program in low- and middle-income countries. The program was implemented to address the historically increasing number of road fatalities and the inadequate funding to reduce them. This study evaluates the performance of the program by estimating lives saved from road safety interventions implemented during the program period (2007–2018) through to 2030. We estimated that 311,758 lives will have been saved by 2030, with 97,148 lives saved up until 2018 when the evaluation was conducted and a further 214,608 lives projected to be saved if these changes are sustained until 2030. Legislative changes alone accounted for 75% of lives saved. Concurrent activities related to reducing drink driving, implementing legislative changes, and social marketing campaigns run in conjunction with police enforcement and other road safety activities accounted for 57% of the total estimated lives saved. Saving 311,758 lives with funding of USD $259 million indicates a cost-effectiveness ratio of USD $831 per life saved. The potential health gains achieved through the number of lives saved from the road safety initiatives funded by Bloomberg Philanthropies represent a considerable return on investment. This study demonstrates the extent to which successful, cost-effective road safety initiatives can reduce road fatalities in low- and middle-income countries.

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis' framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2-26, 27-57, 58-105, 106-192, and 193-364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2-26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

Improvement of Road Safety through Appropriate Cargo Securing Using Outliers

The article focuses on evaluating a transportation experiment that intends to improve road safety by analyzing transport shocks that significantly affect the system of securing the load, vehicle, driver, and other aspects. Within Europe, improper or insufficient securing of loads causes up to 25% of all cargo vehicle accidents. Our transport experiment consists of eight rides of a Tatra truck. The measured values of shocks (acceleration coefficients) are statistically evaluated. Three hypotheses are established for these purposes. First, it is proven that the probability distributions of the shocks values differ statistically significantly among individual rides, namely in their shape and median value. Thus further statistical analyses are performed separately for the individual rides, axes, and the accelerometer locations. These analyses prove significant exceedances of the normatively set limits given by the EN 12195-1:2010 standard, which is potentially risky. Especially for the z axis and y axis, the set 20% limit was exceeded in 75.0% and 56.3% of cases, respectively. In the case of the x axis, the established 20% limit was practically not exceeded at all. The analysis of exceeding the permitted limits (the statistical evaluation of the second and third established hypothesis) is based on boxplots that graphically describe the individual rides, as well as on the found contaminated log-normal distribution of the values of the acceleration coefficients in the individual rides. The last hypothesis regarding exceeding the double value of the permitted limit is rejected. Methods of statistical comparison are used during data analysis. The probability distribution of acceleration coefficients is modeled using a contaminated log-normal distribution.