Safety Effect of Missouri's Strategic Highway Safety Plan (original) (raw)
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Accident Analysis & Prevention, 2010
This paper develops a step-by-step methodology for the application of Full Bayes (FB) approach for beforeand-after analysis of road safety countermeasures. As part of this methodology, it studies the posterior prediction capability of Bayesian approaches and their use in crash reduction factor (CRF) estimation. A collection of candidate models are developed to investigate the impacts of different countermeasures on road safety when limited data are available. The candidate models include traditional, random effects, non-hierarchical and hierarchical Poisson-Gamma and Poisson-Lognormal (P-LN) distributions. The use of random effects and hierarchical model structures allows treatment of the data in a time-series crosssection panel, and deal with the spatial and temporal effects in the data. Next, the proposed FB estimation methodology is applied to urban roads in New Jersey to investigate the impacts of different treatment measures on the safety of "urban collectors and arterial roads" with speed limits less than 45 mph. The treatment types include (1) increase in lane width, (2) installation of median barriers, (3) vertical and horizontal improvements in the road alignment; and (4) installation of guide rails. The safety performance functions developed via different model structures show that random effects hierarchical P-LN models with informative hyper-priors perform better compared with other model structures for each treatment type. The individual CRF values are also found to be consistent across the road sections, with all showing a decrease in crash rates after the specific treatment except guide rail installation treatment. The highest decrease in the crash rate is observed after the improvement in vertical and horizontal alignment followed by increase in lane width and installation of median barriers. Overall statistical analyses of the results obtained from different candidate models show that when limited data are available, P-LN model structure combined with higher levels of hierarchy and informative priors may reduce the biases in model parameters resulting in more robust estimates.
2010
Recent research suggests that traditional safety evaluation methods may be inadequate in accurately determining the effectiveness of roadway safety measures. In recent years, advanced statistical methods are being utilized in traffic safety studies to more accurately determine the effectiveness of roadway safety measures. These methods, particularly Bayesian statistical techniques, have the capabilities to account for the shortcomings of traditional methods. Hierarchical Bayesian modeling is a powerful tool that more fully identifies a given problem than a simpler model could. This report explains the process wherein a hierarchical Bayesian model is developed as a tool to analyze the effectiveness of two types of road safety measures: raised medians and cable barrier. Several sites where these safety measures have been implemented in the last 10 years were evaluated using available crash data. The results of this study show that the installation of a raised median is an effective te...
Analyzing the Effectiveness of Safety Measures Using Bayesian Methods
Recent research has shown that traditional safety evaluation methods have been inadequate in accurately determining the effectiveness of roadway safety measures. In recent years, advanced statistical methods have been utilized in traffic safety studies to more accurately determine the effectiveness of roadway safety measures. These methods, particularly hierarchical Bayesian statistical techniques, have the capabilities to account for the shortcomings of traditional methods. Hierarchical Bayesian modeling is a powerful tool for expressing rich statistical models that more fully reflect a given problem than a simpler model could. This paper uses a hierarchical Bayesian model to analyze the effectiveness of two types of road safety measures: raised medians and cable barriers. Several sites where these safety measures have been implemented in the last 10 years were evaluated using available crash data. This study analyzes the effectiveness of raised medians and cable barriers of roadway safety by determining the effect each has on crash frequency and severity at selected locations. The results of this study show that the installation of a raised median is an effective technique to reduce the overall crash frequency and severity on Utah roadways. The analysis of cable barriers showed that cable barriers were effective in decreasing cross-median crashes and crash severity.
ESTIMATING BENEFITS FROM SPECIFIC HIGHWAY SAFETY IMPROVEMENTS
In the past thirty years, highway fatality rates have declined steadily because, most notably, of dramatic changes in motor vehicle design, passage of laws making seat belt use mandatory and driving while intoxicated a criminal offense, and educating the public through focused advertising campaigns. However, the practice of highway design has changed little.
Analysis of highway crash data by Negative Binomial and Poisson regression models
This study evaluates the influence of roadway, weather and acci-dents conditions, and type of traffic control on accident severity (number of persons killed) using Negative Binomial and Poisson regression models. Information on accident severity and roadway and weather conditions was obtained from the Michigan Department of Transportation Accident Database. Negative Binomial (NB) and Poisson regression models were deployed to measure the association between accident severity and roadway, weather and accidents conditions. NB regression model results presented that monthly, daily, hourly and weekday variations are not statistically significant on accident severity (number of persons killed). However, Poisson regression results were the reverse with respect to these variables. Type of traffic control was also found to be not statistically significant. Number of vehicles involved, crash type (overturn, rear-end, side-swipe, head-on, hit object, and so on), injury types (A, B, C), number...
Accident Analysis & Prevention, 2004
This paper presents an analyses of data from the Highway Safety Information System (HSIS) for the State of Illinois. Our analyses focuses on whether various changes in road network infrastructure can be associated with changes in road fatalities and reported accidents. We also evaluate models that control for demographic changes. County-level time-series data is used and fixed effect negative binomial models are estimated. Results cannot confirm the hypothesis that changes in road infrastructure have been beneficial for safety. Increases in the number of lanes and increased lane widths appear to increase traffic-related fatalities.
Highway accident severities and the mixed logit model: An exploratory empirical analysis
Accident Analysis & Prevention, 2008
Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.
Accident Analysis & Prevention, 2017
This study aims at contributing to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and nonmotorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we convert the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulate a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model is estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model is also estimated and compared with the joint model. The result indicates that the joint model provides better data fit and can identify more significant variables. Subsequently, a novel joint screening method is suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes are identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. It is expected that the joint model and screening method can help decision makers, transportation officials, and community planners to make more efficient treatments to proactively improve pedestrian and bicyclist safety.
Assessing the Safety Impacts of Increased Speed Limits on Kansas Freeways
Journal of Transportation Technologies
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 crosssectional 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.