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Significant research has been conducted in recent years to determine crash hotspots. This study focused on the effects of various traffic parameters, including average traffic speed and traffic volume, on the spatial distributions of freeway crashes. Specifically, this study analyzed the spatial distributions of crashes on the Qazvin–Abyek freeway in Iran using four-year crash records. Spatial crash clustering analysis was performed to identify hotspots and high cluster segments using global Moran’s I, local Moran’s I, and Getis-Ord Gi*. The global Moran’s I indicated that clusters were formed under the low range of hourly traffic volume (less than 1107 veh/h) and the high range of traffic speed (more than 97 km/h), which increased the number of heavy vehicle crashes in the early morning (time 03–06) around the 52 km segment. The results obtained from kernel density estimation (KDE), local Moran’s I, and Getis-Ord Gi* revealed similar crash hotspots. The results further showed different spatial distributions of crashes for different traffic hourly volumes, traffic speed, and crash times, and there was hotspot migration by applying different traffic conditions. These findings can be used to identify high-risk crash conditions for traffic managers and help them to make the best decisions to enhance road safety.

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

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Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.

The Transport Airplane Risk Assessment Methodology (TARAM) is a process for calculating risk associated with continued operational safety issues in the U.S. transport airplane fleet. TARAM is important because its risk-analysis calculations are used when making determinations of unsafe conditions in transport airplanes and when selecting and implementing corrective actions. This report assesses the TARAM process used by the FAA in its efforts to improve the overall safety of the transport airplane fleet. A healthy safety culture requires commitment to continuous improvement. This report provides recommendations to the FAA to address the gaps and strengthen the TARAM.

 Adverse weather conditions can have different effects on different types of road crashes. We quantify the combined effects of traffic volume and meteorological parameters on hourly probabilities of 78 different crash types using generalized additive models. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different crash types in case of precipitation, sun glare and high wind speeds. The largest effect of snow is found in case of single-truck crashes, while rain has a larger effect on single-car crashes. Sun glare increases the probability of multi-car crashes, in particular at higher speed limits and in case of rear-end crashes. High wind speeds increase the probability of single-truck crashes and, for all vehicle types, the risk of crashes with objects blown on the road. A comparison of the predictive power of models with and without meteorological variables shows an improvement of scores of up to 24%, which makes the models suitable for applications in real-time traffic management or impact-based warning systems. These could be used by authorities to issue weather-dependent driving restrictions or situation-specific on-board warnings to improve road safety.

In Vanishing Point , award winning journalist and author Tom Wilber pieces together the largely forgotten story of the bomber, Getaway Gertie , and an eclectic group of enthusiasts who have spent years searching for it. At the height of World War II, a B-24 Liberator bomber vanished with its crew while on a training mission over upstate New York. The final hours and ultimate resting place of pilot Keith Ponder and seven other US aviators aboard the plane remain mysteries to this day. The tale is at once a compelling instance of loss on the World War II American home front and a more extensive, largely unreported history. Ponder-a 21-year-old from rural Mississippi-and his crew were tragically unexceptional casualties in the monumental effort to recruit and train an air force en masse to counter the global conquest of Nazi Germany and Imperial Japan. More than fifteen thousand American airmen and, in some cases, women burned, crashed, or fell to their deaths in stateside training accidents during the war-their lives and stories shuffled away in piles of Air Force bureaucracy. The forgotten story of Getaway Gertie was originally inspired by summer evenings around the campfire on the shores of Lake Ontario, where parts of the plane have washed up. Building on those campfire tales, Wilber deftly connects myth with fact and memory with historicity. The result is a vivid portrait of the forgotten soldier of the home front and a new take on the meaning of wartime sacrifice as the last survivors of the Greatest Generation pass away.

This study investigates the relationship between weather conditions and two-wheeler road crashes in Uttar Pradesh, India, which experiences diverse climatic conditions. A novel framework, the Weather-Influenced Clustering and Random Sampling (WICRS) model, is proposed for Relative Accident (crash) Risk (RAR) analysis. Initially, a preliminary analysis of crash data based on location, human, and environmental factors provides insights into contributing factors. Building on these findings, the WICRS model categorizes weather patterns using highly randomized sampling-based clustering, a departure from traditional matched pair analysis (MPA). The study also conducts a stratified RAR analysis, considering variables such as gender, road type, and time of day. The effectiveness of the WICRS model is validated by comparing its impact with MPA, specifically examining risk analysis for wet and non-wet days. The dataset includes over 954,000 two-wheeler crash incidents, combined with historical weather data over six years. The findings highlight the significance of weather conditions in two-wheeler crashes and support the use of the WICRS model for detailed RAR analysis and road safety policy formulation.