Informing road traffic intervention choices in South Africa: the role of economic evaluations

Time series traffic collision analysis of London hotspots: Patterns, predictions and prevention strategies

Despite recent measures on accident prevention, road collisions, mainly on London's "A" roads, persist as accident sources, endangering vulnerable users in particular. Analysing evidence from London's A-Roads unveils issues concerns and trends. This study utilises extensive data to target factors magnifying accidents: speed, traffic, vulnerable interactions. Stats 19 and transport data including volumes, types, speeds, and congestion parameters are all analysed alongside the collision data. The descriptive statistics have been employed to understand nature of data in the first instance. This has supported the process to cleanse the data outliers or periods where were subjected to incidents and interventions. Predictive model development is conducted to analyse and forecast accident frequency using ARIMA and SARIMAX models forecasted accident rates and interventions. ARIMA yielded higher accuracy. Method of analysis resulted in a statistically reliable formulation of the main factors, enabling use of this method for similar cities across the world. Formulated analysis revealed key contributors as population density, weather, and time of the day. The analysis of data supported identification of strategies emerging as infrastructure improvements, traffic control measures and severity and vulnerable users affected in particular. The analysis reveals distinct exhibits of causation, leading to focused recommendations on infrastructure enhancements, traffic control measures, and the impact on severity and vulnerable users, deviating from prior research findings. Insights aid safer London roads, have global predictive and mitigation value.

Implementation of a model of awareness-raising for taxi motorcyclists in Benin in relation to helmet use: a quasi-experimental study

Background

This study aims to test the effectiveness of an awareness-raising model designed based on the theory of planned behaviour regarding helmet use for motorcycle taxi drivers.

Methods

This quasi-experimental study took place in the cities of Parakou (intervention group) and Porto Novo (control group). Over a three-month period, a package of awareness-raising activities, based on the theory of planned behaviour, have been implemented in the intervention area. Data relate to knowledge, attitudes and practices regarding helmet use was collected prospectively before the intervention, at the end, and 6 months later. Stata 15 was used for data analysis. Chi-square or Fisher, Student’s or Kruskal-Wallis tests was carried out. The difference-in-difference method was used to determine the specific effect of the awareness activities.

Results

After the intervention, there was an improvement in the total score in both groups compared to baseline. The total score increased by 0.2 (0.06–0.3) in the experimental group when the number of sessions attended increased by one (p = 0.005). The difference-in-difference estimator measured among subjects who attended at least one awareness session, controlling for socio-demographic variables, showed a significantly higher difference in the total score of subjects in the experimental group compared to those in the control group both at the end of the interactive sessions and 6 months later.

Conclusion

This model improves the helmet-wearing behaviour of motorbike taxi drivers in the experimental area. It could be adapted and applied to other socio-professional groups and other types of users.

[Assessment of road safety intervention implementation in Benin in 2019]

OBJECTIVE

The study aims to assess the level of implementation of road safety interventions in Benin.

METHOD

The research is based on an evaluative study of road safety aimed to analyze the implementation and logic of road safety interventions, conducted in Benin in 2019. It combined a review of the gray literature and a qualitative component. The data were collected through documents and interviews in structures involved in road safety management.

RESULTS

Road safety was a national priority with one lead institution and several structures involved. There was a lack of consensus among stakeholders, insufficient framework documents, resources, legislative texts, and study data. Few roads were in good condition and very few allowed the separation of two-wheeled vehicles. The vehicle fleet was outdated. Various activities were carried out to raise awareness, to educate the population and to enforce the texts but they were insufficient and poorly coordinated. Reference hospitals had the minimum service to deal with trauma cases. The interventions had not yet resulted in a reduction in the number of injuries and fatalities by accidents, which was increasing.

CONCLUSION

Benin has made great efforts in the area of road safety. However, there are still some shortcomings to take into account.

Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

Safer spaces: The impact of a reduction in road fatalities on the life expectancy of South Africans

In this paper we determine the burden on society of fatalities resulting from road traffic injuries (RTIs) in South Africa. We express the burden in terms of reduced life expectancy and years of potential life lost (YPLL). Our main data source is the Injury Mortality Survey (IMS), a retrospective descriptive study carried out in South Africa. Using the mortality rates by sex and age from the IMS we calculate actual life expectancy at birth. In our counterfactual analysis we assume a 15 % reduction in road fatalities per year over a period of 10 years. A comparison of the estimated actual and counterfactual life expectancies suggests that the average gain in life expectancy at birth would be 0.58 years. Since the overwhelming majority of road traffic fatalities are male (75.7 %), there is a considerable gender difference. Men would gain on average 0.85 years while women would gain 0.30 years in life expectancy, closing the gender gap in life expectancy by about 14 %. We then discuss how a reduction in RTIs could be achieved. South Africa's legislation addresses several of the important aspects of road safety (e.g. seat belt use, drink driving restrictions, speed limits, infrastructure improvements), however, enforcement is relatively weak and should be improved. There are a raft of measures that have been well researched in other countries, most interventions aim to modify the behaviour of road users and have been found to be cost effective. In addition to stricter enforcement, evidence from social science suggests that compliance could be increased through a change in social norms regarding road usage.

Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid

In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the municipality of Madrid from 2014 to 2018. To do this, a regression analysis has been carried out using a random effects negative binomial regression (RENB). The results of the model show that Uber and Cabify services are associated with a decrease in fatal and serious accidents in Madrid.

Implementation of a model of awareness-raising for taxi motorcyclists in Benin in relation to helmet use: protocol for a quasi-experimental study

BACKGROUND

In the large cities of Benin, motorcycle taxi drivers, mainly between the ages of 20 and 40, are particularly exposed to accidents due to their profession. User awareness, along with legislative reforms and enforcement measures, would reduce the incidence of crashes and injuries. This study aims to test the effectiveness of an awareness-raising model regarding helmet use for motorcycle taxi drivers.

METHODS

This is a quasi-experimental study that will take place in the cities of Parakou (intervention group) and Porto Novo (control group). Over a three-month period, a package of awareness-raising activities will be implemented in the intervention area, targeting a group of motorcycle taxi drivers. The messages to be developed for awareness-raising will focus on the most frequently influencing factors, as identified by the baseline collection. These key messages will be disseminated through various tools and communication channels (banners, motorcycle stickers and motorcycle taxi uniforms, interactive sessions). Data will be collected prospectively via a self-reported questionnaire and observation, carried out before the intervention, at the end, and 6 months later. The data will relate to knowledge, attitudes and practices regarding helmet use. The analysis will compare the indicators between the groups, as well as between the pre- and post-intervention phase. The KoboCollect software will be used for data entry and processing, and Stata 15 will be used for data analysis. Chi-square or Fisher, Student's or Kruskal-Wallis tests will be used for the comparisons. The difference-in-difference method will be used to determine the specific effect of the awareness activities.

DISCUSSION

This study will assess the contribution of awareness messages to changing the behaviour of motorcycle taxi drivers by determining the specific effect of the intervention.

Estimating effects of Uber ride-sharing service on road traffic-related deaths in South Africa: a quasi-experimental study

Background

Road traffic deaths are a substantial barrier to population health improvement in low-income and middle-income countries (LMICs). In South Africa, the road-traffic injury mortality (RTM) rate of 27 per 100 000 population is twice the global average, over 60% of which are alcohol-related. Recent US studies suggest the Uber ride-sharing service may reduce alcohol-related RTM, however RTM burden in the USA is relatively low and transport behaviours differ from LMICs.

Methods

Using certification data from all deaths occurring in South Africa in the years 2010–2014 (n=2 498 216), we investigated the relative change in weekly road traffic-related death counts between provinces which received Uber services (beginning in 2013) against those that did not using a difference-in-differences approach.

Results

Weekly road traffic-related deaths in provinces with Uber were lower following Uber introduction than in comparison provinces without Uber. The effect size was larger in the province which had Uber the longest (Gauteng) and among young adult males (aged 17–39 years). However, the absolute effects were very small (<2 deaths per year) and may coincide with seasonal variation.

Conclusions

Overall, findings did not support either an increase or large decrease in province-level road traffic-related deaths associated with Uber introduction to South Africa. More localised investigations in South Africa and other LMICs are needed.

Comparison of influencing factors on outcomes of single and multiple road traffic injuries: A regional study in Shanghai, China (2011-2014)

INTRODUCTION

To identify key intervention factors and reduce road traffic injury (RTI)-associated mortality, this study compared outcomes and influencing factors of single and multiple road traffic injuries (RTIs) in Shanghai.

METHODS

Based on the design of National Trauma Data Bank, this study collected demographic, injury, and outcome data from RTI patients treated at the four largest trauma centers in Shanghai from January 2011 to January 2015. Data were analyzed with descriptive statistics, univariate analysis, and hierarchical logistic regression analysis.

RESULTS

Among 2397 participants, 59.4% had a single injury, and 40.6% had multiple injuries. Most patients' outcome was cure or improvement. For single-RTI patients, length of stay, body region, central nervous system injury, acute renal failure, multiple organ dysfunction syndrome, bacterial infection, and coma were significantly related to outcome. For multiple-RTI patients, age, admission pathway, prehospital time, length of stay, number of body regions, body region, injury condition, injury severity score, and coma were significantly related to outcome.

CONCLUSIONS

Emergency rescue in road traffic accidents should focus on high-risk groups (the elderly), high-incidence body regions (head, thorax, pelvis) and number of injuries, injury condition (central nervous system injury, coma, complications, admission pathway), injury severity (critically injured patients), and time factors (particularly prehospital time).