International Journal of Injury Control and Safety Promotion最新文献

Motorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data. These features are mobility-based features, acceleration event-based features, aggressive overtaking event-based features and motorcyclists socio-economic features. Additionally, the relative importance of features is also determined which shows that aggressive overtaking event-based features have more impact on motorcycle accidents as compared to other categories of features. The developed model is useful in identifying risky motorcyclists and implementing safety measures focused towards them.

The study examined the extent, demographics and risks for child pedestrian, burns and drowning mortality in Johannesburg. Information on the demographics, scene and temporal circumstances for childhood injury deaths from 2000 to 2010 was gleaned from the National Injury Mortality Surveillance System. Descriptive statistical methods were used. The study recorded 756 pedestrian (8.7/100,000), 439 drowning (5.1/100,000), and 399 burn injury deaths (4.6/100,000) among children aged 0-14 years. Male children were the main victims, with male-to-female ratios of 2.3 for drowning, 1.7 for pedestrian and 1.2 for burn mortality. The pattern of child mortality differed across age groups with older children recording higher rates for pedestrian deaths and younger children higher rates for the non-traffic deaths. Pedestrian and burn mortality especially affected black children, while drowning affected both black and white children. The time, day and month of greatest injury mortality varied by injury cause, with e.g. pedestrian mortality common in afternoons and evenings, weekends, and dispersed across the year although increasing towards year end. The study highlighted the salience of differentiating risks for childhood injuries by discrete external cause for purposes of informing prevention responses.

Pedestrian safety is a serious concern in the developing nations of the world. It is evident from the past studies that built-environment characteristics near bus-stops, play a crucial role on the frequency and overall share of pedestrian deaths and injuries in road traffic crashes. The present study aims to identify critical built-environment features around vulnerable bus-stops in an Indian city and evaluate the odds of risk that prevails on the safety of pedestrians near bus stops. Hotspot analysis was conducted to finalise 177 bus stop sites within high-crash clusters in the study area. Built-environment attributes considered were based on sidewalk, crosswalk and bus stop conditions near such vulnerable locations. This study includes a video graphic and manual field survey conducted during the day and night-time. Logistic regression was applied to estimate the impact of built environment features on pedestrian crashes. Width and disability friendliness of sidewalks, presence of bus bays and on-street parking have significant impacts on pedestrian fatalities at locations with a higher share of pedestrian fatalities during the day. On the other hand, presence of zebra crossings at junctions, proper bus stop lighting and high sidewalks reduce the odds of pedestrian crashes at night near bus stops.

This article investigates factors associated with fatal and severe injury pedestrian crash percentages at intersections in Colorado. Many published studies associate road and traveler characteristics with the frequency or severity of pedestrian crashes without reference to specific locations. The objective of this study is to determine whether road and traveler characteristics, aggregated by intersection, partly explain differences in severe crash percentages at intersections. From 2006 to 2018, there were a total of 17,047 reported crashes involving pedestrians and motor vehicles in all of Colorado. This study analyzes 3,015 of these crashes that had the GPS coordinates needed to identify their locations at intersections and included the information needed to identify the pedestrian outcomes of the crash. The results of logistic and linear regressions found that lighting condition, vehicle speed, turning movement of vehicle, vehicle type, pedestrian age, and driver or pedestrian impairment by drugs or alcohol were most associated with severe crash percentages at intersections. These findings identify crash characteristics at intersections with higher severe crash proportions that can potentially be addressed to improve safety.

Un-signalized intersections in India witnessed the maximum number of crashes and fatalities in 2019. The nature of the crash investigation is still largely reactive, where the need for accurate and reliable crash data for effective safety diagnosis is pivotal. In India, crash records are unscientific, and critical details are missing. Therefore, a proactive approach using surrogate safety measures is more promising and prudent in analyzing traffic safety. The present study investigates and models crossing conflicts at un-signalized intersections under mixed traffic conditions. Traffic video data for 14 un-signalized intersections (eight un-signalized three-legged intersections and six un-signalized four-legged intersections) were collected under normal weather conditions. The crossing conflicts were identified and characterized as critical and noncritical conflicts based on the values of post-encroachment time (PET). Conflicts with PET values between -1 s and 1 s were identified as critical conflicts. The observation revealed the existence of both positive and negative PET values. The investigation revealed that crossing conflicts with negative PET values are riskier and more unsafe than conflicts with positive ones. Therefore, the crossing conflicts with positive and negative PETs were modeled separately. The positive and negative PET-based critical crossing conflicts are modeled as a function of traffic flow and intersection geometry-related characteristics using truncated negative binomial regression under a full Bayesian modeling framework. K-fold cross-validation with fivefold was employed to calibrate the model, and RMSE was used to find the best model. The modeling results revealed that the volume and traffic composition of the offending and conflicting stream and intersection geometry significantly influence the number of positive and negative PET-based critical crossing conflicts. The developed models can interest engineers and safety experts to analyze traffic safety and identify critical intersections in urban road networks.

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

A better understanding of the underlying factors to the choice of seatbelt use could contribute to the policy solutions, which consequently enhance the rate of seatbelt usage. To achieve that goal, it is important to obtain unbiased and reliable results by employing a valid statistical technique. In this paper, the latent class (LC) model was extended to account for unobserved heterogeneity across parameters within the same class. The random parameter latent class, or mixed-mixed (MM) model, is an extension of the mixed and LC models by adding another layer to the LC model, with an objective of accounting for heterogeneity within a same class. The results indicated that although the LC model outperformed the mixed model, the standard LC model did not account for the whole heterogeneity in the dataset and adding an extra layer for changing the parameter across the observations result in an improvement in a model fit. The results indicated that seatbelt status of the driver, vehicle type, day of a week, and driver gender are some of factors impacting whether or not passengers would wear their seatbelts. It was also observed that accounting for day of a week, drivers' gender, and type of vehicle heterogeneities in the second layer of the MM model result in a better fit, compared with the LC technique. The results of this study expand our understanding about factors to the choice of seatbelt use while capturing extra heterogeneity of the front-seat passengers' choice of seatbelt use. This is one of the earliest studies implemented the technique in the context of the traffic safety, with individual-specific observations.