为明确不同严重程度的危险货物道路运输事故致因因素,运用故障树(Fault Tree Analysis,FTA)与贝叶斯网络(Baysian Network,BN)相结合的方法进行分析,以避免传统故障树方法的局限性。首先以事故发生为顶事件,驾驶员因素、环境因素、道路...为明确不同严重程度的危险货物道路运输事故致因因素,运用故障树(Fault Tree Analysis,FTA)与贝叶斯网络(Baysian Network,BN)相结合的方法进行分析,以避免传统故障树方法的局限性。首先以事故发生为顶事件,驾驶员因素、环境因素、道路因素、车辆因素、管理因素和危险货物类别因素为中间事件,事故致因因素为基本事件,构建故障树模型;其次将故障树模型映射为贝叶斯网络结构,并利用案例数据进行参数学习;最后对危险货物道路运输事故进行诊断推理和因果推理,找出各方面最敏感因素。结果表明:仅财产损失事故、受伤事故、死亡或中毒事故最敏感的基本事件分别为罐体破裂、腐蚀性物质和车速控制不当;仅财产损失事故、受伤事故、死亡或中毒事故中贡献度最大的中间事件分别为车辆因素、危险货物类别因素和驾驶员因素。研究结果为制定有针对性且有效的危险货物道路运输事故预防和严重程度缓解措施提供了理论依据。展开更多
Florida has the highest number of motorcycle fatalities in the United States and contains the second largest population of registered motorcycles. The COVID-19 pandemic influenced the roads, traffic, and driving behav...Florida has the highest number of motorcycle fatalities in the United States and contains the second largest population of registered motorcycles. The COVID-19 pandemic influenced the roads, traffic, and driving behavior in the continental United States. Motorcycle crashes decreased during the COVID-19 years (2020 and 2021) while the fatality rates increased. The purpose of this study is to 1) investigate motorcycle crashes before and during the Pandemic period to understand the impacts on motorcycle safety and contributing factors to the crash severity levels;2) develop the crash predictive model for different degrees of severity in motorcycle crashes in Florida. Florida statewide crash data were collected. T tests have been conducted to compare the contributing factors between two periods. The injury severities are significantly different among all five levels between those during normal period and the Pandemic period. A crash predictive model has been developed to determine the facts to injury severity levels for motorcycle crashes. A total of eight variables are found to significantly increase the injury severity levels for motorcycle crashes during the Pandemic period.展开更多
The objective of the current study was to evaluate traffic and geometric features and their influences on the safety performance of roundabouts by developing suitable crash modification factors(CMFs).The cross-section...The objective of the current study was to evaluate traffic and geometric features and their influences on the safety performance of roundabouts by developing suitable crash modification factors(CMFs).The cross-sectional method can be applied as an alternative method to estimate the CMFs when before-and-after studies are impractical to apply,e.g.,lack of data from the period after implementing treatments.To accomplish the study objective,CMFs were derived from generalised linear models(GLMs),i.e.,negative binomial(NB)regression,using data collected on regional roundabouts in Toowoomba City,Australia.Six years of crash data from 49 roundabouts included all recorded crashes as well as traffic and geometric features for the entire roundabouts.Several candidate models were developed using the GLMs.Five models were selected based on statistical significance,goodness-of-fit(GOF)measures,and cumulative residual(CURE)analysis.The results show that increasing the number of entry lanes,entry width,entry radius,traffic volume,circulatory roadway width,weaving width,and speed limit have positive effects on roundabout safety.On the other hand,increasing the number of legs,number of exit lanes,exit width,exit radius,weaving length,central island diameters,and presence of fixed object on a central island have negative effects on roundabout safety.The study shows that quantifying the risk factors can support road safety stakeholders to identify safety improvements at roundabouts more effectively and efficiently.展开更多
Truck crash occurrence causes extensive damage to lives and property. Truck crashes on downgrades exacerbate these costs due to the likelihood of a runaway being involved.Highway agencies have continuously sought engi...Truck crash occurrence causes extensive damage to lives and property. Truck crashes on downgrades exacerbate these costs due to the likelihood of a runaway being involved.Highway agencies have continuously sought engineering measures to reduce the incidence of such crashes. However, most past studies on truck crashes have focused on level roadway sections of highways without considering the effects of downgrades. The difference in geometric characteristics of downgrades and the mechanics of truck operations on such sections mean different factors may be at play in contrast to level roadway sections.This paper investigated the factors influencing truck crashes on downgrades; an attempt to fill in some of the research gaps. An empirical analysis of factors affecting truck crashes on two-lane downgrade roadways in Wyoming was carried out using a binary logistic regression technique. After calibrating the model, the effect of each significant variable was determined using theoretical concepts established in previous studies and engineering intuition. Crash factors including driver gender and age, weather, lighting and road conditions, number of crest curves, crash type, number of driveways, day of week and posted speed limit were found to be significant. The results of the study offer new understandings into how the identified factors influence truck crashes on downgrades.展开更多
In recent years, many departments of transportation in the US have invested more resources to enhance pedestrian safety. However, there is still a need to effectively and systematically address pedestrian safety issue...In recent years, many departments of transportation in the US have invested more resources to enhance pedestrian safety. However, there is still a need to effectively and systematically address pedestrian safety issues in low-income areas. Statistics show that pedestrians are fatally injured at disproportionately higher rates in the nation's poorer neighborhoods. Low-income areas often are sectioned by high-volume/high-speed arterials, which compound the problem. In this study, pedestrian-vehicle crash data in lowincome areas were analyzed from two aspects: how demographic factors, road environment, and land use types influence pedestrian crash occurrence(based on frequency data)and how demographic and other factors influence severity in a pedestrian crash(based on severity data). The crash frequency modeling results show that major influential variables for higher pedestrian crash frequency include four demographic factors(proportions of older adults, commuters using public transit or biking, people with low education level,and zero-car ownership), three road environmental factors(densities of traffic signals and bus stops and proportion of higher-speed roads), and three land use factors(densities of discount stores, convenience stores, and fast-food restaurants). The injury severity modeling results show that a dark-not lighted condition is the most influential variable for severe injury pedestrian crashes, and the number of impaired pedestrians and aggressive drivers also greatly increases the probability of severe injury. Based on the analysis results,this paper makes specific recommendations for both engineering countermeasures and pedestrian safety education/outreach plans that resonate with a given area's demographics to effectively improve pedestrian safety in low-income areas.展开更多
Suitable speed limit is important for providing safety for road users. Lower-than-required posted speed limits could cause the majority of drivers non-compliant and higher-than-required posted speed limits may also in...Suitable speed limit is important for providing safety for road users. Lower-than-required posted speed limits could cause the majority of drivers non-compliant and higher-than-required posted speed limits may also increase the number of crashes with related severities. The speed limit raised in Kansas from 70 mph to 75 mph on a number of freeway segments in 2011. The goal of this study is to assess the safety impacts of the freeway sections influenced by speed limit increase. Three years before and three years after speed limit increase was considered and three methods were used: 1-Empirical Bayes (EB), 2-before-and-after with comparison group, and 3-cross-sectional study. The Crash Modification Factors (CMFs) were estimated and showed 16 percent increase for total crashes according to EB method. Further, the before-and-after with comparison group method showed 27 percent increase in total crashes and 35 percent increase on fatal and injury crashes. The cross- sectional method also presented 25 percent increase on total crashes and 62 percent increase on fatal and injury crashes. It was seen that these increases were statistically significant.展开更多
This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the ...This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the explanatory variables at a 95% confidence level. The six-year crash data (2005-2010) were obtained in the State of Florida, USA and five injury level outcomes, no injury, possible injury, non-incapacitating injury, capacitating injury, and fatal injury, were considered. The no injury level was selected as the baseline category.展开更多
Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the c...Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle （W）. Methods： This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. More- over, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. Results： The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers＇ age, weight difference between vehicles, visibility condition and lane number of road signifi- cantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. Conclusion： All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers＇ age and drivers＇ original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beiiing on weekdays.展开更多
Estimating safety effectiveness of roadway improvements and countermeasures,using cross-sectional models,generally requires large amounts of data such as road geometric and traffic-related characteristics at road segm...Estimating safety effectiveness of roadway improvements and countermeasures,using cross-sectional models,generally requires large amounts of data such as road geometric and traffic-related characteristics at road segment levels.These models do not consider all confounding crash contributory factors such as driving culture and environmental conditions at the segment level due to a lack of readily available data.This may result in inaccurate models representing actual conditions at road segment levels,followed by erroneous estimations of safety effectiveness.To minimize the effect of not including such variables,this study develops a new methodology to estimate safety effectiveness of roadway countermeasures,based on generalized linear mixed models,assuming zeroinflated Poisson distribution for the response,and adjusting for spatial autocorrelation using the spatial random effect.The Bayesian approach,with Integrated Nested Laplace Approximation,was used to make inference on this model with computational efficiency.Results showed that incorporating a spatial random effect into the models provided better model fit than non-spatial models;hence,estimated safety effectiveness based on such models is more accurate.The proposed approach is a methodological advancement in traffic safety,which allows evaluation of safety effectiveness or roadway improvements when data are not readily available.展开更多
The safety impact of changes to roadway operations have been of interests in recent years with the publication of the Highway Safety Manual.One area that is in need of further study is the safety impact of traffic sig...The safety impact of changes to roadway operations have been of interests in recent years with the publication of the Highway Safety Manual.One area that is in need of further study is the safety impact of traffic signal coordination projects in urban areas.Specifically,this study seeks to identify the safety benefit from traffic signal coordination projects on major arterial roadways through urban areas using a before and after study with a comparison groups approach and a meta-analysis method.The findings suggest that traffic signal coordination could decrease total crashes by 21 percent,injury crashes by 52 percent and property-damage-only crashes by 21 percent.The results can be utilized by engineering practitioners to estimate the safety benefits for projects that seek to coordinate traffic signals along an urban corridor.Because these projects can both improve the safety of roadways while improving traffic flow,the application of these findings could be broad.展开更多
文摘为明确不同严重程度的危险货物道路运输事故致因因素,运用故障树(Fault Tree Analysis,FTA)与贝叶斯网络(Baysian Network,BN)相结合的方法进行分析,以避免传统故障树方法的局限性。首先以事故发生为顶事件,驾驶员因素、环境因素、道路因素、车辆因素、管理因素和危险货物类别因素为中间事件,事故致因因素为基本事件,构建故障树模型;其次将故障树模型映射为贝叶斯网络结构,并利用案例数据进行参数学习;最后对危险货物道路运输事故进行诊断推理和因果推理,找出各方面最敏感因素。结果表明:仅财产损失事故、受伤事故、死亡或中毒事故最敏感的基本事件分别为罐体破裂、腐蚀性物质和车速控制不当;仅财产损失事故、受伤事故、死亡或中毒事故中贡献度最大的中间事件分别为车辆因素、危险货物类别因素和驾驶员因素。研究结果为制定有针对性且有效的危险货物道路运输事故预防和严重程度缓解措施提供了理论依据。
文摘Florida has the highest number of motorcycle fatalities in the United States and contains the second largest population of registered motorcycles. The COVID-19 pandemic influenced the roads, traffic, and driving behavior in the continental United States. Motorcycle crashes decreased during the COVID-19 years (2020 and 2021) while the fatality rates increased. The purpose of this study is to 1) investigate motorcycle crashes before and during the Pandemic period to understand the impacts on motorcycle safety and contributing factors to the crash severity levels;2) develop the crash predictive model for different degrees of severity in motorcycle crashes in Florida. Florida statewide crash data were collected. T tests have been conducted to compare the contributing factors between two periods. The injury severities are significantly different among all five levels between those during normal period and the Pandemic period. A crash predictive model has been developed to determine the facts to injury severity levels for motorcycle crashes. A total of eight variables are found to significantly increase the injury severity levels for motorcycle crashes during the Pandemic period.
基金the financial support from a Tafila Technical University scholarship for carrying out his PhD study at the University of Southern Queensland。
文摘The objective of the current study was to evaluate traffic and geometric features and their influences on the safety performance of roundabouts by developing suitable crash modification factors(CMFs).The cross-sectional method can be applied as an alternative method to estimate the CMFs when before-and-after studies are impractical to apply,e.g.,lack of data from the period after implementing treatments.To accomplish the study objective,CMFs were derived from generalised linear models(GLMs),i.e.,negative binomial(NB)regression,using data collected on regional roundabouts in Toowoomba City,Australia.Six years of crash data from 49 roundabouts included all recorded crashes as well as traffic and geometric features for the entire roundabouts.Several candidate models were developed using the GLMs.Five models were selected based on statistical significance,goodness-of-fit(GOF)measures,and cumulative residual(CURE)analysis.The results show that increasing the number of entry lanes,entry width,entry radius,traffic volume,circulatory roadway width,weaving width,and speed limit have positive effects on roundabout safety.On the other hand,increasing the number of legs,number of exit lanes,exit width,exit radius,weaving length,central island diameters,and presence of fixed object on a central island have negative effects on roundabout safety.The study shows that quantifying the risk factors can support road safety stakeholders to identify safety improvements at roundabouts more effectively and efficiently.
文摘Truck crash occurrence causes extensive damage to lives and property. Truck crashes on downgrades exacerbate these costs due to the likelihood of a runaway being involved.Highway agencies have continuously sought engineering measures to reduce the incidence of such crashes. However, most past studies on truck crashes have focused on level roadway sections of highways without considering the effects of downgrades. The difference in geometric characteristics of downgrades and the mechanics of truck operations on such sections mean different factors may be at play in contrast to level roadway sections.This paper investigated the factors influencing truck crashes on downgrades; an attempt to fill in some of the research gaps. An empirical analysis of factors affecting truck crashes on two-lane downgrade roadways in Wyoming was carried out using a binary logistic regression technique. After calibrating the model, the effect of each significant variable was determined using theoretical concepts established in previous studies and engineering intuition. Crash factors including driver gender and age, weather, lighting and road conditions, number of crest curves, crash type, number of driveways, day of week and posted speed limit were found to be significant. The results of the study offer new understandings into how the identified factors influence truck crashes on downgrades.
基金a FDOT research project with the contract number of BDV25-977-30
文摘In recent years, many departments of transportation in the US have invested more resources to enhance pedestrian safety. However, there is still a need to effectively and systematically address pedestrian safety issues in low-income areas. Statistics show that pedestrians are fatally injured at disproportionately higher rates in the nation's poorer neighborhoods. Low-income areas often are sectioned by high-volume/high-speed arterials, which compound the problem. In this study, pedestrian-vehicle crash data in lowincome areas were analyzed from two aspects: how demographic factors, road environment, and land use types influence pedestrian crash occurrence(based on frequency data)and how demographic and other factors influence severity in a pedestrian crash(based on severity data). The crash frequency modeling results show that major influential variables for higher pedestrian crash frequency include four demographic factors(proportions of older adults, commuters using public transit or biking, people with low education level,and zero-car ownership), three road environmental factors(densities of traffic signals and bus stops and proportion of higher-speed roads), and three land use factors(densities of discount stores, convenience stores, and fast-food restaurants). The injury severity modeling results show that a dark-not lighted condition is the most influential variable for severe injury pedestrian crashes, and the number of impaired pedestrians and aggressive drivers also greatly increases the probability of severe injury. Based on the analysis results,this paper makes specific recommendations for both engineering countermeasures and pedestrian safety education/outreach plans that resonate with a given area's demographics to effectively improve pedestrian safety in low-income areas.
文摘Suitable speed limit is important for providing safety for road users. Lower-than-required posted speed limits could cause the majority of drivers non-compliant and higher-than-required posted speed limits may also increase the number of crashes with related severities. The speed limit raised in Kansas from 70 mph to 75 mph on a number of freeway segments in 2011. The goal of this study is to assess the safety impacts of the freeway sections influenced by speed limit increase. Three years before and three years after speed limit increase was considered and three methods were used: 1-Empirical Bayes (EB), 2-before-and-after with comparison group, and 3-cross-sectional study. The Crash Modification Factors (CMFs) were estimated and showed 16 percent increase for total crashes according to EB method. Further, the before-and-after with comparison group method showed 27 percent increase in total crashes and 35 percent increase on fatal and injury crashes. The cross- sectional method also presented 25 percent increase on total crashes and 62 percent increase on fatal and injury crashes. It was seen that these increases were statistically significant.
文摘This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the explanatory variables at a 95% confidence level. The six-year crash data (2005-2010) were obtained in the State of Florida, USA and five injury level outcomes, no injury, possible injury, non-incapacitating injury, capacitating injury, and fatal injury, were considered. The no injury level was selected as the baseline category.
文摘Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle （W）. Methods： This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. More- over, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. Results： The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers＇ age, weight difference between vehicles, visibility condition and lane number of road signifi- cantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. Conclusion： All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers＇ age and drivers＇ original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beiiing on weekdays.
文摘Estimating safety effectiveness of roadway improvements and countermeasures,using cross-sectional models,generally requires large amounts of data such as road geometric and traffic-related characteristics at road segment levels.These models do not consider all confounding crash contributory factors such as driving culture and environmental conditions at the segment level due to a lack of readily available data.This may result in inaccurate models representing actual conditions at road segment levels,followed by erroneous estimations of safety effectiveness.To minimize the effect of not including such variables,this study develops a new methodology to estimate safety effectiveness of roadway countermeasures,based on generalized linear mixed models,assuming zeroinflated Poisson distribution for the response,and adjusting for spatial autocorrelation using the spatial random effect.The Bayesian approach,with Integrated Nested Laplace Approximation,was used to make inference on this model with computational efficiency.Results showed that incorporating a spatial random effect into the models provided better model fit than non-spatial models;hence,estimated safety effectiveness based on such models is more accurate.The proposed approach is a methodological advancement in traffic safety,which allows evaluation of safety effectiveness or roadway improvements when data are not readily available.
文摘The safety impact of changes to roadway operations have been of interests in recent years with the publication of the Highway Safety Manual.One area that is in need of further study is the safety impact of traffic signal coordination projects in urban areas.Specifically,this study seeks to identify the safety benefit from traffic signal coordination projects on major arterial roadways through urban areas using a before and after study with a comparison groups approach and a meta-analysis method.The findings suggest that traffic signal coordination could decrease total crashes by 21 percent,injury crashes by 52 percent and property-damage-only crashes by 21 percent.The results can be utilized by engineering practitioners to estimate the safety benefits for projects that seek to coordinate traffic signals along an urban corridor.Because these projects can both improve the safety of roadways while improving traffic flow,the application of these findings could be broad.
