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
Background::Air pollution and poor ambient air quality are significantly related to multiple health risks.One associated disease is chronic obstructive pulmonary disease(COPD),a preventable disease with several contri...Background::Air pollution and poor ambient air quality are significantly related to multiple health risks.One associated disease is chronic obstructive pulmonary disease(COPD),a preventable disease with several contributing factors and one of the leading causes of morbidity/mortality locally and globally.A potentially high-risk population are traffic enforcers who are constantly exposed to air pollution.In the Philippines,the MMDA has the widest coverage in traffic management.The study determined the risk of COPD among Metro Manila Development Authority(MMDA)traffic enforcers in relation to ambient air quality level,as well as identified other factors that increase the risk of developing COPD.Methods::Fifty-two MMDA traffic enforcers deployed in PM 2.5 air quality sensor areas in Metro Manila from 2016 to 2018 were recruited through stratified sampling.The International Primary Airways Guidelines(IPAG)questionnaire was utilized to measure risk of COPD.Respiratory health and working history were obtained through questionnaires.Department of environment and natural resources provided PM 2.5 ambient air quality data which aided in the construction of the Exposure-Month Index.Ordinal logistic regression was used to examine the association of PM 2.5 together with the relevant factors and the risk of COPD.Results::We found statistically significant associations between PM 2.5 and COPD among high risk category[odds risk(OR):1.24,95%confidence interval(CI):1.07-1.44].Age(Moderate,OR:1.16,95%CI:0.98-1.38 and High,OR:10.06,95%CI:4.02-25.17)and chest pain(Moderate,OR:68.65,95%CI:1.71-2.75×103)were potential risk factors,whereas body mass index(BMI)(OR:0.05,95%CI:0.01-0.53)exhibited protective effect.Conclusions::Exposure to PM 2.5 was associated with an increased risk of COPD among high-risk category MMDA traffic enforcers.Age and chest pain were potential risk factors to risk of COPD,whereas BMI exhibited a potential protective effect.Results of this study can be used for clinical management of high-risk populations,such that of MMDA traffic enforcers.展开更多
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.
文摘Background::Air pollution and poor ambient air quality are significantly related to multiple health risks.One associated disease is chronic obstructive pulmonary disease(COPD),a preventable disease with several contributing factors and one of the leading causes of morbidity/mortality locally and globally.A potentially high-risk population are traffic enforcers who are constantly exposed to air pollution.In the Philippines,the MMDA has the widest coverage in traffic management.The study determined the risk of COPD among Metro Manila Development Authority(MMDA)traffic enforcers in relation to ambient air quality level,as well as identified other factors that increase the risk of developing COPD.Methods::Fifty-two MMDA traffic enforcers deployed in PM 2.5 air quality sensor areas in Metro Manila from 2016 to 2018 were recruited through stratified sampling.The International Primary Airways Guidelines(IPAG)questionnaire was utilized to measure risk of COPD.Respiratory health and working history were obtained through questionnaires.Department of environment and natural resources provided PM 2.5 ambient air quality data which aided in the construction of the Exposure-Month Index.Ordinal logistic regression was used to examine the association of PM 2.5 together with the relevant factors and the risk of COPD.Results::We found statistically significant associations between PM 2.5 and COPD among high risk category[odds risk(OR):1.24,95%confidence interval(CI):1.07-1.44].Age(Moderate,OR:1.16,95%CI:0.98-1.38 and High,OR:10.06,95%CI:4.02-25.17)and chest pain(Moderate,OR:68.65,95%CI:1.71-2.75×103)were potential risk factors,whereas body mass index(BMI)(OR:0.05,95%CI:0.01-0.53)exhibited protective effect.Conclusions::Exposure to PM 2.5 was associated with an increased risk of COPD among high-risk category MMDA traffic enforcers.Age and chest pain were potential risk factors to risk of COPD,whereas BMI exhibited a potential protective effect.Results of this study can be used for clinical management of high-risk populations,such that of MMDA traffic enforcers.
