International Journal of Injury Control and Safety Promotion最新文献

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors.

Various studies have investigated the relationship between speed and accidents using different definitions of speed variation. This research considers the speed in mixed traffic as heterogeneous based on the vehicle categories. This research aims to develop a traffic safety model with speed heterogeneity as expressed in accident modification factor (AMF) index. The data types include traffic data, road volumes and geometrics from 18 roads in 8 provinces in Indonesia: Central Sulawesi, Southeast Sulawesi, South Sulawesi, West Kalimantan, Central Kalimantan, NTB, NTT and Bali. The power model is adopted to model the relationship between speed changes and the number of accidents and victims. Change in paratransit speed is significant in predicting all types of AMFs, but the effects are lower than those of the other categories. Truck speed change has the highest impact of fatalities. A 10% decrease in truck speed results in a 29.9% decrease in the number of fatalities, whilst the same 10% decrease in paratransit decreases 17.4% of fatalities. The study resulted in AMF models based on the vehicle speed heterogeneity that could be used in road safety evaluation by looking at the effects of vehicle speed changes in specific categories.

Spousal physical violence (PV) against women is considered to be major health issue in developing countries. Lifetime physical violence is a composite outcome consists of hit, kick, beat, slap and threatened with weapon, perpetrated by the husband. The study aims to examine changes in prevalence and specific risk factors for PV from 1998 to 2016 in India. This study analyzed data from a cross sectional epidemiological survey in 1998-1999, NFHS-3 (2005-2006) and NFHS-4 (2015-2016) data. There was a significant decline of about 10% (95% CI: 8.8%-11.1%) in PV. Major risk factors for change in PV were husband's use of alcohol, illiteracy and socio-economic status of the household. The Protection of Women from Domestic Violence Act may have played a role in reducing the PV. Even though there was a decline in PV, actions have to be implemented from the root level to ensure women empowerment.

Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial and temporal factors and road geometry) on crash severity for major intra-city roads in Saudi Arabia. We used a crash dataset that covers four years (Oct. 2016 - Feb. 2021) with more than 59,000 crashes. Machine learning algorithms were utilized to predict the crash severity outcome (non-fatal/fatal) for three types of roads: single, multilane, and freeway. Furthermore, features that have a strong impact on crash severity were examined. Results show that only 4 out of 16 road condition variables were found to be contributing to crash severity, namely: paints, cat eyes, fence side, and metal cable. Additionally, vacation was found to be a contributing factor to crash severity, meaning crashes that occur on vacation are more severe than non-vacation days.

To achieve an effective emergency response and road safety, this study aims to assist a semi-automated dynamic system to analyze and predict the spatial distribution and temporal pattern of road crashes. Kasur, an intermediate city of Pakistan, was selected and data including location, time and reasons of accidents for five years (2014-2018) was utilized. Radar charts, Getis-Ord Gi* statistic, Moran's I spatial auto-correlation, and time series indices were engaged to present temporal, spatial and spatial-temporal variation of accidents, using python-based tools and jupyter notebook. A dynamic user interface was created using Github and Tableau to visualize a real-time zoom-able spatiotemporal variation of accidents. The results explain that out of 12 months, October faces the peak while April sees the least of road accidents. 7am is the peak hour for accidents and the weekends record a significantly higher number of road accidents as compared to weekdays. The city core witnesses the major hotspot areas with huge cluster of accidents. The findings contribute towards a well-informed decision support system, the knowledge of spatial analytics and its application in road safety science, and the preparedness of the rescue agencies for rapid response to reduce the impacts of road accidents.

Identifying black spots effectively and accurately is a pivotal and challenging task to improve road traffic safety. A novel black spot identification model is proposed by integrating the GIS-based processing with hierarchical density-based spatial clustering of applications with noise. Additionally, the optimal clustering parameters are determined based on an internal validation indicator called the density-based clustering validation index to minimize the impact of subjectivity in parameter selection. The model is validated by collecting 3536 accident data from 1 August to 31 October 2020 in Hangzhou, China, and eventually identifies 39 black spots. The results show that: (1) The number of accidents contained in black spots account for 75% of all accidents, while the length of network in the black spots only account for 23.26% of the total road network length. (2) Compared with the conventional density-based spatial clustering of applications with noise model and K-means model, the proposed model achieves the best performance with more accidents gathered per unit road length. (3) The sample survey with 6 onsite of the identified black spots indicates that the proposed model has high recognition accuracy and recommend these sites for further investigation.

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.