International Journal of Injury Control and Safety Promotion最新文献

{"title":"Random forest models for motorcycle accident prediction using naturalistic driving based big data.","authors":"Fatma Outay,&nbsp;Muhammad Adnan,&nbsp;Uneb Gazder,&nbsp;Syed Fazal Abbas Baqueri,&nbsp;Hammad Hussain Awan","doi":"10.1080/17457300.2022.2164310","DOIUrl":"https://doi.org/10.1080/17457300.2022.2164310","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"282-293"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9521360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Child pedestrian, drowning and burn mortality in Johannesburg.","authors":"Samed Bulbulia,&nbsp;Ashley van Niekerk,&nbsp;Lu-Anne Swart,&nbsp;Mohammed Seedat","doi":"10.1080/17457300.2022.2147193","DOIUrl":"https://doi.org/10.1080/17457300.2022.2147193","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"232-238"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9883161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi.","authors":"Deotima Mukherjee,&nbsp;K Ramachandra Rao,&nbsp;Geetam Tiwari","doi":"10.1080/17457300.2022.2109175","DOIUrl":"https://doi.org/10.1080/17457300.2022.2109175","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"185-194"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9527656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors affecting severe pedestrian crash percentages at intersections in Colorado 2006-2018.","authors":"Bruce Janson,&nbsp;Mohamed Mesbah,&nbsp;Wesley Marshall","doi":"10.1080/17457300.2022.2147273","DOIUrl":"https://doi.org/10.1080/17457300.2022.2147273","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"255-261"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9528618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating and modeling the influence of PET-types on crossing conflicts at urban unsignalized intersections in India.","authors":"Aninda Bijoy Paul,&nbsp;Ninad Gore,&nbsp;Shriniwas Arkatkar,&nbsp;Gaurang Joshi","doi":"10.1080/17457300.2022.2147194","DOIUrl":"https://doi.org/10.1080/17457300.2022.2147194","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"239-254"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9899023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study of the factors affecting driving risk perception using the Bivariate Ordered Probit model.","authors":"Sina Sahebi,&nbsp;Habibollah Nassiri,&nbsp;Hossein Naderi","doi":"10.1080/17457300.2022.2090579","DOIUrl":"https://doi.org/10.1080/17457300.2022.2090579","url":null,"abstract":"Abstract This paper aims to examine the key factors influencing driving risk perception in Iran. We conducted separate surveys for two groups of Iranian drivers, namely passenger car drivers and truck drivers. In order to assess driving risk perception, respondents were asked what they think about their Probability of Having a Road Accident (PHRA) and if they eventually have an accident as a driver, what they think about the Probability of it being Fatal or causing Severe Injury (PFSI). A Bivariate Ordered Probit model, which considers the possible correlation between PHRA and PFSI, was developed to explain the observed driving risk perception using type of vehicle, driving experience, socio-demographic information, and driving behaviour. According to the results, vehicle type, vehicle age, driving experience, sleep quality, at-fault accidents over the past three years, vehicles safety-related equipment, and education level have significant effects on driving risk perception (p-value < 0.05). In addition, this paper compares the driving risk perception of truck and passenger car drivers. The results show that truck drivers have a higher perception of PHRA and PFSI compared with passenger car drivers (p-value < 0.05). The results may convince policy-makers to consider the characteristics of the two categories of drivers when designing regulations.","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"172-184"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9528589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Professor Emeritus Dr Leif O. Svanström, MD, PhD, Karolinska Institutet, Stockholm, Sweden 30 October 1943 - 29 January 2023.","authors":"Guldbrand Skjönberg,&nbsp;Maria Isabel Gutiérrez,&nbsp;Reza Mohammadi,&nbsp;Andrés Villaveces,&nbsp;Barbara Minuzzo,&nbsp;Shrikant I Bangdiwala","doi":"10.1080/17457300.2023.2211874","DOIUrl":"https://doi.org/10.1080/17457300.2023.2211874","url":null,"abstract":"","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"321-323"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9531908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety research guided by systems theory?","authors":"Geetam Tiwari","doi":"10.1080/17457300.2023.2210424","DOIUrl":"https://doi.org/10.1080/17457300.2023.2210424","url":null,"abstract":"This volume brings a mix of studies ranging from pedestrian crashes, risk perceptions, black spot analyses, and motorcycle crashes from the area of motor vehicle injuries, to children drowning, cooking related burn injuries, and emergency response by rescue agencies. The study of pedestrian and motorcycle crashes both continue to be important in all countries in general, and specifically for low and middle income countries, where nearly 50-60% of fatalities involve pedestrians and motorcycles (World Health Organization (WHO),) 2018). The importance of robust data and methods producing strong evidence for effective interventions must be recognized by all researchers and other stakeholders. Malaya Mohanty, et al, from India, have studied road traffic crashes in a medium size city in India. The study presents the development of crash prediction models by assessing the roles of vehicles, both as impacting vehicle and victim, using historical crash data. The study compares the binary logistic regression model and the artificial neural network (ANN) method to understand the role of vehicle type in crashes. The authors have discussed the strengths and limitations of both methods, which is useful for other researchers. It has been observed that heavy vehicles and two-wheelers are prone to contribute to a large number of road accidents. While the involvement of heavy vehicles as impacting vehicles has been listed in many earlier studies, the role of two wheelers as an impacting vehicle is an important new insight, and an area where new research is required for preventive interventions. The assessment of actual traffic crash risk and the perceived traffic crash risk by different road users and their behaviours on the road has been studied by many researchers. Understanding of both risks is important since our behaviour is influenced by our perception; however, there could be many other contributory factors for actual traffic crash risk, such as road geometry, traffic mix and other factors. Sinao Sahebi et al studied the driving risk perception of drivers, whereas Ramachandra et al have analysed the actual risk to pedestrians near bus stops. Sinao Sahebi et al from Iran, studied the driving risk perception of road accidents of truck and car drivers, and their views on the possible association with resulting in fatality or severe injury. The authors developed a bivariate ordered probit model to better examine the distinction between perceived risk and objective risk. The study shows that several factors, like vehicle type and age, driving experience, and education levels, have important effects on the drivers’ risk perception, while (say) the road geometry would be an important factor in the measurement of the objective risk. K. Ramachandra Rao, et al, studied the risk assessment for pedestrians posed by the built environemnt in the vicinity of bus-stops. The present case study of Delhi city, is supported by video-graphic material and the relevant manual fie","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"153-154"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of contributory factors of fatal pedestrian crashes by mixed logit model and association rules.","authors":"Maria Rella Riccardi,&nbsp;Filomena Mauriello,&nbsp;Antonella Scarano,&nbsp;Alfonso Montella","doi":"10.1080/17457300.2022.2116647","DOIUrl":"https://doi.org/10.1080/17457300.2022.2116647","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"195-209"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9527663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The mixed-mixed multinomial logit model for identification of factors to the passengers' seatbelt use.","authors":"Mahdi Rezapour,&nbsp;Khaled Ksaibati","doi":"10.1080/17457300.2022.2164308","DOIUrl":"https://doi.org/10.1080/17457300.2022.2164308","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"30 2","pages":"262-269"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9527701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}