A Brief Overview of Road Accidents and Recent Preventive Methodologies for Accident Hotspots in the United Arab Emirates

This paper highlights major causes of road accidents in the United Arab Emirates (UAE) and explores the possibility of reducing them through modern in-vehicle control technologies. Mostly, road safety data from the Global Road Safety Facility (GSRF), Road Safety Polices and Regulations for UK and UAE have been reviewed for comparison and analysis. It contains a descriptive analysis of road accident data which was taken from Ministry of Interior (MOI) - UAE website. It shows how the Pareto Principle applies to most of the road accidents in the UAE with young males’ poor driving habits and higher maximum speed limits being the major causes and, a systematic approach as per the Nilsson Power Model, to tackle these issues. It ends with the conclusion that, although high speed limits on urban roads and highways are some of the critical factors in causing dangerous road accidents but, it can be tackled with by implementing strict road safety policies and enforcing them with modern in vehicle technologies.

Interpolation of Quantile Regression to Estimate Driver’s Risk of Traffic Accident Based on Excess Speed

Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender, percent of urban zone driving and night time driving. This study proposes an approximation of quantile regression coefficients by interpolating only a few quantile levels, which can be chosen carefully from the unconditional empirical distribution function of the response. Choosing the levels before interpolation improves accuracy. This approximation method is convenient for real-time implementation of risky driving identification and provides a fast approximate calculation of a risk score. We illustrate our results with data on 9614 drivers observed over one year.

Environmentally-friendly electric vehicles: a silent menace to vulnerable road users?

Prediction models have been extensively used in the field of road safety, however, none of these models have been particularly applied to zero-emission electric vehicle (EV) related injuries so far; which may lead to different outcomes due to their inaudible engines. Using an optimizable classification tree, this first-ever study aims to predict the likelihood of personal injury severities stemming from EV-related crashes on Britain's roads. The prediction model was found to be capable of detecting significant and insignificant factors. The factors provide important insights into how the severity of injuries can be reduced in the future deployment of EVs. Although there was an increased risk for injuries classified as ‘slight severity’, particularly at lower urban speed limits, several predictors are suggesting that EVs do not pose more of a risk to a certain group. Contrary to popular belief, no convincing evidence has been found to suggest that eco-friendly EVs are ‘silent killers’ for vulnerable road users.

Evaluating the citywide Edinburgh 20mph speed limit intervention effects on traffic speed and volume: A pre-post observational evaluation

Objectives Traffic speed is important to public health as it is a major contributory factor to collision risk and casualty severity. 20mph (32km/h) speed limit interventions are an increasingly common approach to address this transport and health challenge, but a more developed evidence base is needed to understand their effects. This study describes the changes in traffic speed and traffic volume in the City of Edinburgh, pre- and 12 months post-implementation of phased city-wide 20mph speed limits from 2016–2018. Methods The City of Edinburgh Council collected speed and volume data across one full week (24 hours a day) pre- and post-20mph speed limits for 66 streets. The pre- and post-speed limit intervention data were compared using measures of central tendency, dispersion, and basic t-tests. The changes were assessed at different aggregations and evaluated for statistical significance (alpha = 0.05). A mixed effects model was used to model speed reduction, in the presence of key variables such as baseline traffic speed and time of day. Results City-wide, a statistically significant reduction in mean speed of 1.34mph (95% CI 0.95 to 1.72) was observed at 12 months post-implementation, representing a 5.7% reduction. Reductions in speed were observed throughout the day and across the week, and larger reductions in speed were observed on roads with higher initial speeds. Mean 7-day volume of traffic was found to be lower by 86 vehicles (95% CI: -112 to 286) representing a reduction of 2.4% across the city of Edinburgh (p = 0.39) but with the direction of effect uncertain. Conclusions The implementation of the city-wide 20mph speed limit intervention was associated with meaningful reductions in traffic speeds but not volume. The reduction observed in road traffic speed may act as a mechanism to lessen the frequency and severity of collisions and casualties, increase road safety, and improve liveability.

Geospatial Analysis of Road Traffic Accidents and Emergency Response Optimization in Kano Metropolis, Nigeria

BackgroundIncrease in occurrence of road traffic accidents in Kano metropolis have resulted into continuous loss of lives, injuries and increased people’s exposure to risk. This study examined road traffic accidents emergency response within Kano metropolis with a view to enhancing its efficiency through establishing communication and synergy between Emergency Healthcare Facilities (EHCF), ambulances and accident hotspots. MethodsGPS surveying was conducted to obtain the location and attributes of the major EHCF, accident hotspots along the junctions of the highways and the 2 existing ambulances at Kano State Fire Service and Federal Road Safety Corp head offices (KSFS and FRSC). Road traffic data (vector format) was digitized from Worldview 3 satellite image (2018, 30cm spatial resolution) from which two major road classes were identified (highways and minor roads) along with their speed limits of 50km/hr and 30km/hr respectively. Time distances were determined based on length and speed limits. Nearest Neighbor and Network analysis (closest facility, shortest route and location-allocation) analyses were conducted. ResultsThe result revealed a variation in the distribution patterns of EHCH, ambulances and accident spots. Closest ambulance facility analysis shows that it takes the FRSC ambulance about 9.41 minutes to reach to accident spot 18 (Maiduguri Road, after NNPC), and 7.52 minutes to travel to AKTH as the closest EHCF. On the other hand, it takes the same ambulance about 3 times the time taken to spot 18 and 4 times the time taken to AKTH to reach to Court road incident spot (spot 16) and IRPH as the closest EHCF. This signifies greater chances of death of almost all victims across the metropolis due to inability to provide CPS within the first 4 minutes before reaching to the hospital. However, in case of Pediatric emergencies, the analysis of closest EHCF from accident spots revealed that it takes less than one minute to travel from accident spots 13, 14 and 15 to IRPH as the closest Pediatric EHCF. Equally, similar time is taken to travel from incident spots 20 and 23 to Sir Sunusi and MMS hospitals respectively. Location-allocation analysis identified eight new locations based on maximum of 4 minutes impedance cutoff from all directions towards the incidents spots. ConclusionIt is concluded that the prevailing road traffic accident emergency response system within the metropolis is inefficient. Therefore, more ambulances should be strategically positioned to fasten emergency response.