Mohammed Imran Basheer Ahmed, Halah Alabdulkarem, Fatimah Alomair, Dana Aldossary, Manar Alahmari, Munira Alhumaidan, Shoog Alrassan, Atta Rahman, Mustafa Youldash, Gohar Zaman
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.
{"title":"A Deep-Learning Approach to Driver Drowsiness Detection","authors":"Mohammed Imran Basheer Ahmed, Halah Alabdulkarem, Fatimah Alomair, Dana Aldossary, Manar Alahmari, Munira Alhumaidan, Shoog Alrassan, Atta Rahman, Mustafa Youldash, Gohar Zaman","doi":"10.3390/safety9030065","DOIUrl":"https://doi.org/10.3390/safety9030065","url":null,"abstract":"Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.","PeriodicalId":36827,"journal":{"name":"Safety","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtual Reality (VR) construction safety training modules have reached a level of maturity which renders them as a serious alternative to traditional safety training modules. The purpose of this study is to investigate the usability of a particular safety training module related to “Working at heights” for blue-collar construction workers in Kuwait. A mixed study approach was applied based on a semi-quasi experimental research design, utilizing a control group/experimental group with pre-/post-test measurements, supplemented by observations. The findings indicate a statistically insignificant higher learning effectiveness of the workers exposed to the VR approach. Observations confirmed that trainees require an extended time of preparation to become familiar with moving within the virtual environment and using the related hardware. Furthermore, younger users with less work experience reported a higher usability than older users with more work experience. VR content developers are encouraged to investigate the possibilities of simplifying the virtual environment to make it more relevant for blue-collar workers, reduce the complexity of the hardware, and intensify the feeling of the consequences resulting from users’ choices. Construction companies and educational institutions training construction blue-collar workers can benefit from the VR approach to safety training if they allow sufficient time for familiarization with the virtual training module.
{"title":"Safer Working at Heights: Exploring the Usability of Virtual Reality for Construction Safety Training among Blue-Collar Workers in Kuwait","authors":"Mohamad Iyad Al-Khiami, Martin Jaeger","doi":"10.3390/safety9030063","DOIUrl":"https://doi.org/10.3390/safety9030063","url":null,"abstract":"Virtual Reality (VR) construction safety training modules have reached a level of maturity which renders them as a serious alternative to traditional safety training modules. The purpose of this study is to investigate the usability of a particular safety training module related to “Working at heights” for blue-collar construction workers in Kuwait. A mixed study approach was applied based on a semi-quasi experimental research design, utilizing a control group/experimental group with pre-/post-test measurements, supplemented by observations. The findings indicate a statistically insignificant higher learning effectiveness of the workers exposed to the VR approach. Observations confirmed that trainees require an extended time of preparation to become familiar with moving within the virtual environment and using the related hardware. Furthermore, younger users with less work experience reported a higher usability than older users with more work experience. VR content developers are encouraged to investigate the possibilities of simplifying the virtual environment to make it more relevant for blue-collar workers, reduce the complexity of the hardware, and intensify the feeling of the consequences resulting from users’ choices. Construction companies and educational institutions training construction blue-collar workers can benefit from the VR approach to safety training if they allow sufficient time for familiarization with the virtual training module.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47701678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safety indicators that eliminate variability in human interpretation. This work also showcases a class of PSPIs that are reliable and time-dependent but only work in a digital online environment: profile PSPIs. It is demonstrated that the profile PSPI opens many new opportunities for leading indicators, without the need for complex mathematics. Online PSPI analyses were performed at the Syngenta Huddersfield Manufacturing Centre, Leeds Road, West Yorkshire, United Kingdom, and shared with their international headquarters in Basel, Switzerland. The performance was determined with industry software to extract time-series data and perform the calculations. The calculations were based on decades of IoT data stored in the AVEVA Factory Historian. Non-trivial data cleansing and additional data tags were required for the creation of relevant signal conditions and composite conditions. This work demonstrates that digital methods do not require gifted data analysts to report existing PSPIs in near real-time and is well within the capabilities of chemical (safety) engineers. Current PSPIs can also be evaluated in terms of their effectiveness to allow management to make decisions that lead to corrective actions. This improves significantly on traditional PSPI processes that, when reviewed monthly, lead to untimely decisions and actions. This approach also makes it possible to review PSPIs as they develop, receiving notifications of PSPIs when they reach prescribed limits, all with the potential to recommend alternative PSPIs that are more proactive in nature.
{"title":"Online Process Safety Performance Indicators Using Big Data: How a PSPI Looks Different from a Data Perspective","authors":"Paul Singh, C. van Gulijk, Neil Sunderland","doi":"10.3390/safety9030062","DOIUrl":"https://doi.org/10.3390/safety9030062","url":null,"abstract":"This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safety indicators that eliminate variability in human interpretation. This work also showcases a class of PSPIs that are reliable and time-dependent but only work in a digital online environment: profile PSPIs. It is demonstrated that the profile PSPI opens many new opportunities for leading indicators, without the need for complex mathematics. Online PSPI analyses were performed at the Syngenta Huddersfield Manufacturing Centre, Leeds Road, West Yorkshire, United Kingdom, and shared with their international headquarters in Basel, Switzerland. The performance was determined with industry software to extract time-series data and perform the calculations. The calculations were based on decades of IoT data stored in the AVEVA Factory Historian. Non-trivial data cleansing and additional data tags were required for the creation of relevant signal conditions and composite conditions. This work demonstrates that digital methods do not require gifted data analysts to report existing PSPIs in near real-time and is well within the capabilities of chemical (safety) engineers. Current PSPIs can also be evaluated in terms of their effectiveness to allow management to make decisions that lead to corrective actions. This improves significantly on traditional PSPI processes that, when reviewed monthly, lead to untimely decisions and actions. This approach also makes it possible to review PSPIs as they develop, receiving notifications of PSPIs when they reach prescribed limits, all with the potential to recommend alternative PSPIs that are more proactive in nature.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44073439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rakesh Gangadharaiah, J. Brooks, Patrick J. Rosopa, Haotian Su, Lisa Boor, Ashley Edgar, Kristin Kolodge, Yunyi Jia
Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.
{"title":"The Development of the Pooled Rideshare Acceptance Model (PRAM)","authors":"Rakesh Gangadharaiah, J. Brooks, Patrick J. Rosopa, Haotian Su, Lisa Boor, Ashley Edgar, Kristin Kolodge, Yunyi Jia","doi":"10.3390/safety9030061","DOIUrl":"https://doi.org/10.3390/safety9030061","url":null,"abstract":"Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44046283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.
{"title":"Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context","authors":"Ahmed Jaber, B. Csonka","doi":"10.3390/safety9030060","DOIUrl":"https://doi.org/10.3390/safety9030060","url":null,"abstract":"This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42205754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Permanent Daylight Savings Time (DST) may improve road safety by providing more daylight in the evening but could merely shift risk to morning commutes or increase risk due to fatigue and circadian misalignment. Methods: To identify how potential daylight exposure and fatigue risk could differ between permanent DST versus permanent Standard Time (ST) or current time arrangements (CTA), generic work and school schedules in five United States cities were modeled in SAFTE-FAST biomathematical modeling software. Commute data were categorized by morning (0700–0900) and evening (1600–1800) rush hours. Results: Percent darkness was greater under DST compared with ST for the total waking day (t = 2.59, p = 0.03) and sleep periods (t = 2.46, p = 0.045). Waketimes occurred before sunrise 63 ± 41% percent of the time under DST compared with CTA (42 ± 37%) or ST (33 ± 38%; F(2,74) = 76.37; p < 0.001). Percent darkness was greater during morning (16 ± 31%) and lower during evening rush hour (0 ± 0%) in DST compared with either CTA (morning: 7 ± 23%; evening: 7 ± 14%) or ST (morning: 7 ± 23%; evening: 7 ± 15%). Discussion: Morning rush hour overlaps with students’ commutes and shift workers’ reverse commutes, which may increase traffic congestion and risk compared with evening rush hour. Switching to permanent DST may be more disruptive than either switching to ST or keeping CTA without noticeable benefit to fatigue or potential daylight exposure.
背景:永久日光节约时间(DST)可以通过在晚上提供更多的日光来改善道路安全，但可能只是将风险转移到早晨通勤，或由于疲劳和昼夜节律失调而增加风险。方法:为了确定永久夏令时与永久标准时间(ST)或当前时间安排(CTA)之间的潜在日光暴露和疲劳风险差异，在SAFTE-FAST生物数学建模软件中对美国五个城市的一般工作和学校时间表进行了建模。通勤数据按早高峰时间(07:00 - 09:00)和晚高峰时间(16:00 - 18:00)分类。结果:夏令时下的黑暗百分比比夏令时下的总清醒日(t = 2.59, p = 0.03)和睡眠时间(t = 2.46, p = 0.045)更大。与CTA组(42±37%)或ST组(33±38%)相比，DST组(63±41%)的睡眠时间发生在日出前;F(2,74) = 76.37;P < 0.001)。与两种CTA相比，夏令时早晨的黑暗百分比更高(16±31%)，晚高峰时间的黑暗百分比更低(0±0%)(早晨:7±23%;晚上:7±14%)或ST(早上:7±23%;晚上:7±15%)。讨论:早高峰与学生上下班和倒班工人上下班的时间重叠，与晚高峰相比，可能会增加交通拥堵和风险。切换到永久夏时制可能比切换到夏时制或保持CTA更具破坏性，而没有明显的疲劳或潜在的日光照射益处。
{"title":"Potential Effects of Permanent Daylight Savings Time on Daylight Exposure and Risk during Commute Times across United States Cities in 2023–2024 Using a Biomathematical Model of Fatigue","authors":"J. Devine, J. Choynowski, S. Hursh","doi":"10.3390/safety9030059","DOIUrl":"https://doi.org/10.3390/safety9030059","url":null,"abstract":"Background: Permanent Daylight Savings Time (DST) may improve road safety by providing more daylight in the evening but could merely shift risk to morning commutes or increase risk due to fatigue and circadian misalignment. Methods: To identify how potential daylight exposure and fatigue risk could differ between permanent DST versus permanent Standard Time (ST) or current time arrangements (CTA), generic work and school schedules in five United States cities were modeled in SAFTE-FAST biomathematical modeling software. Commute data were categorized by morning (0700–0900) and evening (1600–1800) rush hours. Results: Percent darkness was greater under DST compared with ST for the total waking day (t = 2.59, p = 0.03) and sleep periods (t = 2.46, p = 0.045). Waketimes occurred before sunrise 63 ± 41% percent of the time under DST compared with CTA (42 ± 37%) or ST (33 ± 38%; F(2,74) = 76.37; p < 0.001). Percent darkness was greater during morning (16 ± 31%) and lower during evening rush hour (0 ± 0%) in DST compared with either CTA (morning: 7 ± 23%; evening: 7 ± 14%) or ST (morning: 7 ± 23%; evening: 7 ± 15%). Discussion: Morning rush hour overlaps with students’ commutes and shift workers’ reverse commutes, which may increase traffic congestion and risk compared with evening rush hour. Switching to permanent DST may be more disruptive than either switching to ST or keeping CTA without noticeable benefit to fatigue or potential daylight exposure.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45647583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ittirit Mohamad, R. Kasemsri, V. Ratanavaraha, Sajjakaj Jomnonkwao
Accidents pose significant obstacles to economic progress and quality of life, especially in developing countries. Thailand faces such challenges and this research seeks to assess the frequency and most common causes of road accidents that lead to fatalities. This study employed the Apriori algorithm to examine the interrelationships among factors contributing to accidents in order to inform policymaking for reducing accident rates, minimizing economic and human losses, and enhancing the effectiveness of the healthcare system. By analyzing road accident data from 2015 to 2020 in Thailand (167,820 accidents causing THB 1.13 billion in damages), this article specifically focuses on the drivers responsible for fatal highway accidents. The findings reveal several interconnected variables that heighten the likelihood of fatalities, such as male gender, exceeding speed limits, riding a motorbike, traveling on straight roads, encountering dry surface conditions, and clear weather. An association rule analysis underscores the increased risk of injury or death in traffic accidents.
{"title":"Application of the Apriori Algorithm for Traffic Crash Analysis in Thailand","authors":"Ittirit Mohamad, R. Kasemsri, V. Ratanavaraha, Sajjakaj Jomnonkwao","doi":"10.3390/safety9030058","DOIUrl":"https://doi.org/10.3390/safety9030058","url":null,"abstract":"Accidents pose significant obstacles to economic progress and quality of life, especially in developing countries. Thailand faces such challenges and this research seeks to assess the frequency and most common causes of road accidents that lead to fatalities. This study employed the Apriori algorithm to examine the interrelationships among factors contributing to accidents in order to inform policymaking for reducing accident rates, minimizing economic and human losses, and enhancing the effectiveness of the healthcare system. By analyzing road accident data from 2015 to 2020 in Thailand (167,820 accidents causing THB 1.13 billion in damages), this article specifically focuses on the drivers responsible for fatal highway accidents. The findings reveal several interconnected variables that heighten the likelihood of fatalities, such as male gender, exceeding speed limits, riding a motorbike, traveling on straight roads, encountering dry surface conditions, and clear weather. An association rule analysis underscores the increased risk of injury or death in traffic accidents.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46065946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Faisal Habib, R. Bridgelall, Diomo Motuba, Baishali Rahman
Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users’ crash data of San Francisco, the first step in the workflow involves using Ripley’s K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes.
{"title":"Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users","authors":"Muhammad Faisal Habib, R. Bridgelall, Diomo Motuba, Baishali Rahman","doi":"10.3390/safety9030057","DOIUrl":"https://doi.org/10.3390/safety9030057","url":null,"abstract":"Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users’ crash data of San Francisco, the first step in the workflow involves using Ripley’s K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43149387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emma D’Antoine, Janis Jansz, Ahmed Barifcani, Sherrilyn Shaw-Mills, Mark Harris, Christopher Lagat
The offshore oil and gas working environment is an inherently dangerous one, with risks posed to physical safety on a daily basis. One neglected field of research is the added psychosocial stressors present in this environment. This research examined the experiences of offshore oil and gas workers through one-on-one online interviews which were recorded and transcribed. Transcripts were analyzed through the qualitative software NVivo, which generated themes and patterns for the responses given to questions that were developed through a focus group. The results of the analysis showed that multiple psychosocial stressors are present in this population, such as fear of speaking up, unsatisfactory company-provided facilities, work–life interference, work status, micromanaging, gender harassment and bullying. In addition, interviews identified that production and time pressures, along with fatigue, can influence accidents and mistakes. Climate factors also cause discomfort. However, these are managed according to best practices by organizations. Due to the timing of the study, COVID-19 was a significant stressor for some, but not all, employees. In conclusion, offshore oil and gas workers face multiple stressors in a dangerous environment that may lead to devastating consequences.
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Pub Date : 2023-08-15DOI: 10.20944/preprints202307.0279.v1
Emma D’Antoine, J. Jansz, A. Barifcani, Sherrilyn Shaw-Mills, Mark A. Harris, C. Lagat
The offshore oil and gas working environment is an inherently dangerous one, with risks posed to physical safety on a daily basis. One neglected field of research is the added psychosocial stressors present in this environment. This research examined the experiences of offshore oil and gas workers through one-on-one online interviews which were recorded and transcribed. Transcripts were analyzed through the qualitative software NVivo, which generated themes and patterns for the responses given to questions that were developed through a focus group. The results of the analysis showed that multiple psychosocial stressors are present in this population, such as fear of speaking up, unsatisfactory company-provided facilities, work–life interference, work status, micromanaging, gender harassment and bullying. In addition, interviews identified that production and time pressures, along with fatigue, can influence accidents and mistakes. Climate factors also cause discomfort. However, these are managed according to best practices by organizations. Due to the timing of the study, COVID-19 was a significant stressor for some, but not all, employees. In conclusion, offshore oil and gas workers face multiple stressors in a dangerous environment that may lead to devastating consequences.
{"title":"Psychosocial Safety and Health Hazards and Their Impacts on Offshore Oil and Gas Workers","authors":"Emma D’Antoine, J. Jansz, A. Barifcani, Sherrilyn Shaw-Mills, Mark A. Harris, C. Lagat","doi":"10.20944/preprints202307.0279.v1","DOIUrl":"https://doi.org/10.20944/preprints202307.0279.v1","url":null,"abstract":"The offshore oil and gas working environment is an inherently dangerous one, with risks posed to physical safety on a daily basis. One neglected field of research is the added psychosocial stressors present in this environment. This research examined the experiences of offshore oil and gas workers through one-on-one online interviews which were recorded and transcribed. Transcripts were analyzed through the qualitative software NVivo, which generated themes and patterns for the responses given to questions that were developed through a focus group. The results of the analysis showed that multiple psychosocial stressors are present in this population, such as fear of speaking up, unsatisfactory company-provided facilities, work–life interference, work status, micromanaging, gender harassment and bullying. In addition, interviews identified that production and time pressures, along with fatigue, can influence accidents and mistakes. Climate factors also cause discomfort. However, these are managed according to best practices by organizations. Due to the timing of the study, COVID-19 was a significant stressor for some, but not all, employees. In conclusion, offshore oil and gas workers face multiple stressors in a dangerous environment that may lead to devastating consequences.","PeriodicalId":36827,"journal":{"name":"Safety","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47047072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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