This study investigates the inward flux events following sawtooth crashes in the edge of HL-2A neutral beam heated plasmas.We identified three distinct types of inward fluxes with varying magnitudes and durations,each...This study investigates the inward flux events following sawtooth crashes in the edge of HL-2A neutral beam heated plasmas.We identified three distinct types of inward fluxes with varying magnitudes and durations,each associated with unique plasma parameter fluctuations.Magnetic fluctuations,particularly the disruption of magnetic surface structures caused by sawtooth crashes,may play a significant role in modulating plasma dynamics.Moreover,the crossphase term and coherence between density and velocity fluctuations were found to be key factors in these flux events,with high coherence correlating with peak inward flux.These findings enhance the understanding of fluctuation-induced transport after sawtooth crashes and have implications for plasma confinement in fusion devices.展开更多
Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes fa...Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes factors that contribute to crash occurrence based on two national datasets in the United States (CISS and NASS-CDS) for the years 2017-2022 and 2010-2015, respectively. Three taxonomies were applied to enhance understanding of the various crash contributing factors. These taxonomies were developed based on previous research and practice and involved different groupings of human factors, vehicle factors, and roadway and environmental factors. Statistics for grouping the different types of factors and statistics for specific factors are provided. The results indicate that human factors are present in over 95% of crashes, roadway and environmental factors are present in over 45% of crashes, and vehicle factors are present in less than 2% of crashes. Regarding factors related to human error and vehicle maintenance, speeding is involved in over 25% of crashes, distraction is involved in over 20% of crashes, alcohol and drugs are involved in over 9% of crashes, and vehicle maintenance is involved in approximately 0.45% of crashes. Approximately 4.4% of crashes involve a driver who “looked but did not see.” Weather is involved in over 13% of crashes. Conclusions: The findings indicate that, consistent with previous research, human factors or human error are present in around 95% of crashes. Infrastructure and environmental factors contribute to about 45% of crashes. Vehicle factors contribute to only 1.67% - 1.71% of crashes. The results from this study could potentially be used to inform future safety management and improvement activities, including policy-making, regulation development, safe systems and systemic safety approaches to safety management, and other engineering, education, emergency response, enforcement, evaluation, and encouragement activities. The findings could also be used in the development of future Driver Assistance Technologies (DAT) systems and in enhancing existing technologies.展开更多
Within-Visual-Range(WVR)air combat is a highly dynamic and uncertain domain where effective strategies require intelligent and adaptive decision-making.Traditional approaches,including rule-based methods and conventio...Within-Visual-Range(WVR)air combat is a highly dynamic and uncertain domain where effective strategies require intelligent and adaptive decision-making.Traditional approaches,including rule-based methods and conventional Reinforcement Learning(RL)algorithms,often focus on maximizing engagement outcomes through direct combat superiority.However,these methods overlook alternative tactics,such as inducing adversaries to crash,which can achieve decisive victories with lower risk and cost.This study proposes Alpha Crash,a novel distributional-rein forcement-learning-based agent specifically designed to defeat opponents by leveraging crash induction strategies.The approach integrates an improved QR-DQN framework to address uncertainties and adversarial tactics,incorporating advanced pilot experience into its reward functions.Extensive simulations reveal Alpha Crash's robust performance,achieving a 91.2%win rate across diverse scenarios by effectively guiding opponents into critical errors.Visualization and altitude analyses illustrate the agent's three-stage crash induction strategies that exploit adversaries'vulnerabilities.These findings underscore Alpha Crash's potential to enhance autonomous decision-making and strategic innovation in real-world air combat applications.展开更多
This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Acc...This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Accounting Research Database(CSMAR)for empirical analysis.By examining the impact of CSR performance on stock price crash risk,this study identifies key relationships and further investigates the moderating role of media promotion and communication as an intermediary to explore the transmission mechanisms and influence between the two.The empirical results indicate that CSR performance is significantly negatively correlated with stock price crash risk,suggesting that strong CSR performance can effectively reduce the likelihood of a stock price crash.Furthermore,additional analysis reveals that media plays a moderating role in the relationship between CSR performance and stock price crash risk.This study aims to contribute to the understanding of the formation mechanisms and analytical paradigms of factors influencing stock price crash risk while providing theoretical support and reference value for risk prevention strategies.展开更多
Using data on Chinese non-financial listed firms covering 2009 to 2022,we explore the effect of supply chain transparency on stock price crash risk.Two proxies for supply chain transparency are constructed using the n...Using data on Chinese non-financial listed firms covering 2009 to 2022,we explore the effect of supply chain transparency on stock price crash risk.Two proxies for supply chain transparency are constructed using the number of supply chain partners’names and the proportion of their transactions disclosed in annual reports.The results reveal that enhancing supply chain transparency can decrease crash risk,specifically by mitigating tax avoidance and earnings management.Moreover,the analysis suggests that this risk-reduction effect is more prominent in companies where managers are more incentivized to hide negative information and investors possess superior abilities to acquire information.Interestingly,supplier transparency is more influential in mitigating crash risk than customer transparency.These findings emphasize the significance of supply chain transparency in managing financial risk.展开更多
Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extr...Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.展开更多
In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge ...In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge gap with a robust examination of car crash occurrence dynamics and the influencing variables in the Greater Melbourne area,Australia.We employed a comprehensive multi-model machine learning and geospatial analytics approach,unveiling the complicated interactions intrinsic to vehicular incidents.By harnessing Random Forest with SHAP(Shapley Additive Explanations),GLR(Generalized Linear Regression),and GWR(Geographically Weighted Regression),our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities.Using the Random Forest model,essential factors were emphasized,and with the aid of SHAP,we accessed the interaction of these factors.To complement our methodology,we incorporated hexagonalized geographic units,refining the granularity of crash density evaluations.In our multi-model study of car crash dynamics in Greater Melbourne,road geometry emerged as a key factor,with intersections showing a significant positive correlation with crashes.The average land surface temperature had variable significance across scales.Socio-economically,regions with a higher proportion of childless populations were identified as more prone to accidents.Public transit usage displayed a strong positive association with crashes,especially in densely populated areas.The convergence of insights from both Generalized Linear Regression and Random Forest’s SHAP values offered a comprehensive understanding of underlying patterns,pinpointing high-risk zones and influential determinants.These findings offer pivotal insights for targeted safety interventions in Greater Melbourne,Australia.展开更多
Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried ...Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.展开更多
This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential p...This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.展开更多
Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. Thi...Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.展开更多
In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.Howe...In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.展开更多
基金support of these experiments.This work was supported by the National Natural Science Foundation of China(12405268,12175227,11875255,12375226,and 11975231)the National Magnetic Confinement Fusion Science Program of China(2022YFE03060003,2022YFE03100004)+1 种基金the Fundamental Research Funds for the Central Universities(WK2140000016)the China Postdoctoral Science Foundation(2022M723066).
文摘This study investigates the inward flux events following sawtooth crashes in the edge of HL-2A neutral beam heated plasmas.We identified three distinct types of inward fluxes with varying magnitudes and durations,each associated with unique plasma parameter fluctuations.Magnetic fluctuations,particularly the disruption of magnetic surface structures caused by sawtooth crashes,may play a significant role in modulating plasma dynamics.Moreover,the crossphase term and coherence between density and velocity fluctuations were found to be key factors in these flux events,with high coherence correlating with peak inward flux.These findings enhance the understanding of fluctuation-induced transport after sawtooth crashes and have implications for plasma confinement in fusion devices.
文摘Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes factors that contribute to crash occurrence based on two national datasets in the United States (CISS and NASS-CDS) for the years 2017-2022 and 2010-2015, respectively. Three taxonomies were applied to enhance understanding of the various crash contributing factors. These taxonomies were developed based on previous research and practice and involved different groupings of human factors, vehicle factors, and roadway and environmental factors. Statistics for grouping the different types of factors and statistics for specific factors are provided. The results indicate that human factors are present in over 95% of crashes, roadway and environmental factors are present in over 45% of crashes, and vehicle factors are present in less than 2% of crashes. Regarding factors related to human error and vehicle maintenance, speeding is involved in over 25% of crashes, distraction is involved in over 20% of crashes, alcohol and drugs are involved in over 9% of crashes, and vehicle maintenance is involved in approximately 0.45% of crashes. Approximately 4.4% of crashes involve a driver who “looked but did not see.” Weather is involved in over 13% of crashes. Conclusions: The findings indicate that, consistent with previous research, human factors or human error are present in around 95% of crashes. Infrastructure and environmental factors contribute to about 45% of crashes. Vehicle factors contribute to only 1.67% - 1.71% of crashes. The results from this study could potentially be used to inform future safety management and improvement activities, including policy-making, regulation development, safe systems and systemic safety approaches to safety management, and other engineering, education, emergency response, enforcement, evaluation, and encouragement activities. The findings could also be used in the development of future Driver Assistance Technologies (DAT) systems and in enhancing existing technologies.
基金supported by the National Key R&D Program of China(No.2021YFB3300602)。
文摘Within-Visual-Range(WVR)air combat is a highly dynamic and uncertain domain where effective strategies require intelligent and adaptive decision-making.Traditional approaches,including rule-based methods and conventional Reinforcement Learning(RL)algorithms,often focus on maximizing engagement outcomes through direct combat superiority.However,these methods overlook alternative tactics,such as inducing adversaries to crash,which can achieve decisive victories with lower risk and cost.This study proposes Alpha Crash,a novel distributional-rein forcement-learning-based agent specifically designed to defeat opponents by leveraging crash induction strategies.The approach integrates an improved QR-DQN framework to address uncertainties and adversarial tactics,incorporating advanced pilot experience into its reward functions.Extensive simulations reveal Alpha Crash's robust performance,achieving a 91.2%win rate across diverse scenarios by effectively guiding opponents into critical errors.Visualization and altitude analyses illustrate the agent's three-stage crash induction strategies that exploit adversaries'vulnerabilities.These findings underscore Alpha Crash's potential to enhance autonomous decision-making and strategic innovation in real-world air combat applications.
基金R&D Program of Beijing Municipal Education Commission(Grant No.SM202210005007)。
文摘This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Accounting Research Database(CSMAR)for empirical analysis.By examining the impact of CSR performance on stock price crash risk,this study identifies key relationships and further investigates the moderating role of media promotion and communication as an intermediary to explore the transmission mechanisms and influence between the two.The empirical results indicate that CSR performance is significantly negatively correlated with stock price crash risk,suggesting that strong CSR performance can effectively reduce the likelihood of a stock price crash.Furthermore,additional analysis reveals that media plays a moderating role in the relationship between CSR performance and stock price crash risk.This study aims to contribute to the understanding of the formation mechanisms and analytical paradigms of factors influencing stock price crash risk while providing theoretical support and reference value for risk prevention strategies.
基金supported by the National Social Science Foundation Key Project of China for financial support through Grant No:22AJL004.
文摘Using data on Chinese non-financial listed firms covering 2009 to 2022,we explore the effect of supply chain transparency on stock price crash risk.Two proxies for supply chain transparency are constructed using the number of supply chain partners’names and the proportion of their transactions disclosed in annual reports.The results reveal that enhancing supply chain transparency can decrease crash risk,specifically by mitigating tax avoidance and earnings management.Moreover,the analysis suggests that this risk-reduction effect is more prominent in companies where managers are more incentivized to hide negative information and investors possess superior abilities to acquire information.Interestingly,supplier transparency is more influential in mitigating crash risk than customer transparency.These findings emphasize the significance of supply chain transparency in managing financial risk.
文摘Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.
基金Linking Health,Place and Urban Planning through the Australian Urban Observatory by Ian Potter Foundation,Australia.
文摘In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge gap with a robust examination of car crash occurrence dynamics and the influencing variables in the Greater Melbourne area,Australia.We employed a comprehensive multi-model machine learning and geospatial analytics approach,unveiling the complicated interactions intrinsic to vehicular incidents.By harnessing Random Forest with SHAP(Shapley Additive Explanations),GLR(Generalized Linear Regression),and GWR(Geographically Weighted Regression),our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities.Using the Random Forest model,essential factors were emphasized,and with the aid of SHAP,we accessed the interaction of these factors.To complement our methodology,we incorporated hexagonalized geographic units,refining the granularity of crash density evaluations.In our multi-model study of car crash dynamics in Greater Melbourne,road geometry emerged as a key factor,with intersections showing a significant positive correlation with crashes.The average land surface temperature had variable significance across scales.Socio-economically,regions with a higher proportion of childless populations were identified as more prone to accidents.Public transit usage displayed a strong positive association with crashes,especially in densely populated areas.The convergence of insights from both Generalized Linear Regression and Random Forest’s SHAP values offered a comprehensive understanding of underlying patterns,pinpointing high-risk zones and influential determinants.These findings offer pivotal insights for targeted safety interventions in Greater Melbourne,Australia.
基金supported by the National Natural Science Foundation of China[grant numbers 42101449,42090012 and 61825103]the Natural Science Foundation of Hubei Province,China[grant numbers 2022CFB773 and 2020CFA001]+2 种基金the Key Research and Development Program of Hubei Province,China[grant number 2022BAA048]the Chutian Scholar Program of Hubei Provincethe Yellow Crane Talent Scheme.
文摘Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.
文摘This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
文摘Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.
文摘In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.
