Before-and-after methods have been effectively used in the road safety studies to estimate Crash Modification Factors (CMFs) of individual treatments as well as the multiple treatments on roadways. Since the common pr...Before-and-after methods have been effectively used in the road safety studies to estimate Crash Modification Factors (CMFs) of individual treatments as well as the multiple treatments on roadways. Since the common practice is to apply multiple treatments on road segments, it is important to have a method to estimate CMFs of individual treatment so that the effect of each treatment towards improving the road safety can be identified. Even though there are methods introduced by researchers to combine multiple CMFs or to isolate the safety effectiveness of individual treatment from CMFs developed for multiple treatments, those methods have to be tested before using them. This study considered two multiple treatments namely 1) Safety edge with lane widening 2) Adding 2 ft paved shoulders with shoulder rumble strips and/or asphalt resurfacing. The objectives of this research are to propose a regression-based method to estimate individual CMFs estimate CMFs using before-and-after Empirical Bayes method and compare the results. The results showed that having large sample size gives accurate predictions with smaller standard error and p-values of the considered treatments. Also, results obtained from regression method are similar to the EB method even though the values are not exactly the same. Finally, it was seen that the safety edge treatment reduces crashes by 15% - 25% and adding 2 ft shoulders with rumble strips reduces crashes by 25% - 49%.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
Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed ...Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.展开更多
Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash pre...Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash prediction models for various types of highway facilities that are often referred to as safety performance functions(SPFs).Freeway facility SPFs in the HSM were developed using data gathered from the states of California,Maine,and Washington.When applying these HSM-default SPFs to a local jurisdiction,the HSM recommends calibration of HSM-default SPFs or development of jurisdiction-specific SPFs to improve the accuracy of crash predictions.This study first calibrated the HSM-default freeway SPFs and for further accuracy and comparison purposes calibration functions were developed using Kansas freeway data.The performance of calibrated HSM-default SPFs was then compared with developed calibration functions concerning the accuracy in crash prediction.Freeway facility calibration dataset included521 freeway segments,351 entrance-related speed-change lanes,and 366 exit-related speed-change lanes.Cumulative residual plots and several other goodness-of-fit measures were used to assess the quality of calibrated HSM-default SPFs and calibration functions.Calibration functions fitted better compared to calibrated HSM-default SPFs for Kansas freeway data.The methodology used in this study could be beneficial and practiced to any jurisdiction.Calibration functions could be used as an alternative to jurisdiction-specific SPFs or a replacement for HSM-default SPFs,which are frequently used in comparing alternatives,in calculating economic benefits of project improvements,and in estimating economic effectiveness of crash reduction in highway safety-related decision making.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
文摘Before-and-after methods have been effectively used in the road safety studies to estimate Crash Modification Factors (CMFs) of individual treatments as well as the multiple treatments on roadways. Since the common practice is to apply multiple treatments on road segments, it is important to have a method to estimate CMFs of individual treatment so that the effect of each treatment towards improving the road safety can be identified. Even though there are methods introduced by researchers to combine multiple CMFs or to isolate the safety effectiveness of individual treatment from CMFs developed for multiple treatments, those methods have to be tested before using them. This study considered two multiple treatments namely 1) Safety edge with lane widening 2) Adding 2 ft paved shoulders with shoulder rumble strips and/or asphalt resurfacing. The objectives of this research are to propose a regression-based method to estimate individual CMFs estimate CMFs using before-and-after Empirical Bayes method and compare the results. The results showed that having large sample size gives accurate predictions with smaller standard error and p-values of the considered treatments. Also, results obtained from regression method are similar to the EB method even though the values are not exactly the same. Finally, it was seen that the safety edge treatment reduces crashes by 15% - 25% and adding 2 ft shoulders with rumble strips reduces crashes by 25% - 49%.
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Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.
基金supported by the Kansas Department of Transportation(KDOT)under the K-TRAN program.
文摘Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash prediction models for various types of highway facilities that are often referred to as safety performance functions(SPFs).Freeway facility SPFs in the HSM were developed using data gathered from the states of California,Maine,and Washington.When applying these HSM-default SPFs to a local jurisdiction,the HSM recommends calibration of HSM-default SPFs or development of jurisdiction-specific SPFs to improve the accuracy of crash predictions.This study first calibrated the HSM-default freeway SPFs and for further accuracy and comparison purposes calibration functions were developed using Kansas freeway data.The performance of calibrated HSM-default SPFs was then compared with developed calibration functions concerning the accuracy in crash prediction.Freeway facility calibration dataset included521 freeway segments,351 entrance-related speed-change lanes,and 366 exit-related speed-change lanes.Cumulative residual plots and several other goodness-of-fit measures were used to assess the quality of calibrated HSM-default SPFs and calibration functions.Calibration functions fitted better compared to calibrated HSM-default SPFs for Kansas freeway data.The methodology used in this study could be beneficial and practiced to any jurisdiction.Calibration functions could be used as an alternative to jurisdiction-specific SPFs or a replacement for HSM-default SPFs,which are frequently used in comparing alternatives,in calculating economic benefits of project improvements,and in estimating economic effectiveness of crash reduction in highway safety-related decision making.
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.
