Crash Modification Factors Research Articles

An Alternative Regression-Based Approach to Estimate the Crash Modification Factors of Multiple Treatments Using Before-and-After Data

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%.

Estimating safety effects of adaptive signal control technology using the Empirical Bayes method

IntroductionAdaptive signal control technology (ASCT) has long been investigated for its operational benefits, but the safety impacts of this technology are still unclear. The main purpose of this study was to determine the safety effect of ASCT at urban/suburban intersections by assessing two different systems. MethodCrash data for 41 intersections from the Pennsylvania Department of Transportation (PennDOT), along with crash frequencies computed through Safety Performance Functions (SPFs), were used to perform the Empirical Bayes (E-B) method to develop crash modification factors (CMF) for ASCT. Moreover, a crash type analysis was conducted to examine the safety impact of ASCT on a regional scale and the variation of safety among type of crashes observed. ResultsThe results from this study indicated the potential of ASCT to reduce crashes since the Crash Modification Factor (CMF) values for both ASCT systems (SURTRAC and InSync) showed significant reductions in crashes. Average CMF values of 0.87 and 0.64 were observed for total and fatal and injury crash categories at a 95% confidence level, and results were consistent between systems. While a reduction in the proportion of rear end crashes was observed, the change was not determined to be statistically significant. The overall distribution of crash types did not change significantly when ASCT was deployed. Conclusion and practical applicationThe results indicate that safety benefits of ASCT were generally consistent across systems, which should aid agencies in making future deployment decisions on ASCT.

Barrier-relevant crash modification factors and average costs of crashes on arterial roads in Indiana

The objective of this study was to develop crash modification factors (CMFs) and estimate the average crash costs applicable to a wide range of road-barrier scenarios that involved three types of road barriers (concrete barriers, W-beam guardrails, and high-tension cable barriers) to produce a suitable basis for comparing barrier-oriented design alternatives and road improvements. The intention was to perform the most comprehensive and in-depth analysis allowed by the cross-sectional method and the crash data available in Indiana. To accomplish this objective and to use the available data efficiently, the effects of barrier were estimated on the frequency of barrier-relevant (BR) crashes, the types of harmful events and their occurrence during a BR crash, and the severity of BR crash outcomes. The harmful events component added depth to the analysis by connecting the crash onset with its outcome. Further improvement of the analysis was accomplished by considering the crash outcome severity of all the individuals involved in a crash and not just drivers, utilizing hospital data, and pairing the observations with and without road barriers along same or similar road segments to better control the unobserved heterogeneity.This study confirmed that the total number of BR crashes tended to be higher where medians had installed barriers, mainly due to collisions with barriers and, in some cases, with other vehicles after redirecting vehicles back to traffic. These undesirable effects of barriers were surpassed by the positive results of reducing cross-median crashes, rollover events, and collisions with roadside hazards. The average cost of a crash (unit cost) was reduced by 50% with cable barriers installed in medians wider than 50ft. A similar effect was concluded for concrete barriers and guardrails installed in medians narrower than 50ft. The studied roadside guardrails also reduced the unit cost by 20%–30%.Median cable barriers were found to be the most effective among all the studied barriers due to the smaller increase in the crash frequency caused by these barriers and the less severe injury outcomes. More specifically, the occupants of vehicles colliding with near-side cable barriers tended to have less severe injuries than occupants of vehicles entering the median from median’s farther side. The near-side cable barriers provided protection against rollover inside the median and against a potentially dangerous collision with or running over the median drain; therefore, the greatest safety benefit can be expected where cable barriers are installed at both edges of the median.The CMFs and unit crash costs for 48 road-barrier scenarios produced in this study are included in this paper.

Evaluating the safety benefit of retrofitting motorways section with barriers meeting a new EU standard: Comparison of observational before–after methodologies

Abstract The road safety barriers are today designed and installed in compliance with the European standards for Road Restraint Systems (EN 1317), which lays down common requirements for the testing and certification in all EU countries. The introduction of the European Union (EU) regulation for safety barriers, which is based on performance, has encouraged European road agencies to perform an upgrade of the old barriers installed before 2000, with the expectation that there will be safety benefits at the retrofitted sites. Due to the high cost of such treatments, a benefit-cost analysis (BCA) is often used for site selection and ranking and to justify the investment. To this aim a crash modification factor (CMF) has to be applied and errors in the estimation of benefits are directly reflected in the reliability of BCA. Despite the benefits of empirical Bayes before–after (EB–BA) analysis or similar rigorous methods are well-known in the scientific world, these approaches are not always the standard for estimating the effectiveness of safety treatments. To this aim, the differences between the EB–BA and a naive comparison of observed crashes before and after the treatment are presented in the paper. Crash modification factors for total and target crashes are estimated by performing an EB–BA based on data from a motorway in Italy. As expected the results suggest a strong safety benefit for the ran-off-road crashes by reducing the number of severe crashes (fatal and injury). The statistical significance of results obtained by the EB–BA approach show that the retrofits are still cost-effective. The comparison pointed out as selection bias effects can overestimate the safety benefit of the retrofits when a naive approach is used to estimate the CMF and how those can significantly affect a benefit-cost analysis.

Effects of geometry and pavement friction on horizontal curve crash frequency

ABSTRACTHorizontal curves tend to be associated with a disproportionate number of severe crashes. Many treatments have been proposed to reduce traffic crashes and fatalities occurring on horizontal curves. The application of high-friction surface treatments is one among many treatments that agencies have been considering. However, relatively less research has been conducted on the safety implications of pavement friction improvements caused by these treatments. Before–after case studies have been conducted on the effectiveness of high-friction surface treatments, but pavement variables like skid number (coefficient of friction multiplied by 100) have not yet been incorporated into safety prediction methodologies. The objective of this study is to develop a crash modification factor (CMF) for skid number for all crashes, wet-weather crashes, run-off-the-road crashes, and wet-weather run-off-the-road crashes. A horizontal curve database from a southern state in the United States was used to accomplish the study objective. The skid number variable was found to be statistically significant, in addition to traditional variables such as traffic volume, curve radius, and cross-sectional widths. The CMF developed in this study can be applied to evaluate the safety performance of a curve of interest and estimate the potential safety benefit of installing a high-friction surface treatment.

A full Bayesian approach to appraise the safety effects of pedestrian countdown signals to drivers

Although they are meant for pedestrians, pedestrian countdown signals (PCSs) give cues to drivers about the length of the remaining green phase, hence affecting drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers, in the cities of Jacksonville and Gainesville, Florida, using crash modification factors (CMFs) and crash modification functions (CMFunctions). A full Bayes (FB) before-and-after with comparison group method was used to quantify the safety impacts of PCSs to drivers. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings indicated that installing PCSs result in a significant improvement of drivers’ safety, at a 95% Bayesian credible interval (BCI), for total, PDO, and rear-end collisions. The results of FI and angle crashes were not significant. The CMFunctions indicate that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Nevertheless, the CMFs on rear-end crashes are observed to decline with post-treatment time. In summary, the results suggest the usefulness of PCSs for drivers. The findings of this study may prompt a need for a broader research to investigate the need to design PCSs that will serve the purpose not only of pedestrians, but drivers as well.

Another look at the safety effects of horizontal curvature on rural two-lane highways

Crash Modification Factors (CMFs) are used to represent the effects on crashes of changes to highway design elements and are usually obtained from observational studies based on reported crashes. The design element of interest for this paper is horizontal curvature on rural 2-lane highways. The data for this study came from the Washington State database in the Highway Safety Information System (HSIS). Crash prediction models are developed for curve sections on rural 2-lane highway and the tangent sections up- and down-stream of the curve sections. Different negative binomial models were developed for segments on level grades (<3%), moderate grades (3–6%), and steep grades (>6%) to account for the confounding effects of gradient. The relationships between crashes at different traffic volumes and deflection angles are explored to illustrate how to get estimates of CMFs for increases in the minimum radius, considering the effect of increased tangent length for sharper curves, an effect that is overlooked in the Highway Safety Manual CMF, in addition to the effect of gradient. The results of that exploration indicated that even at different design speeds and deflection angles, the CMF estimates for incremental increases in radius lie within the same range, and that the crash reduction rate (CRR) is higher at segments on higher grades compared to the ones on lower grades.

Development of safety performance functions and crash modification factors for expressway ramps

The ramp is a typical section of expressway interchanges where design elements such as the grade and curvature complicatedly changes along the ramp. Dynamic changes in the design elements of ramps may seriously influence driving behaviors, resulting in more crashes on ramps than other freeway facilities. Therefore, there is a strong need to improve traffic safety of freeway ramps through in-depth studies regarding the effects of freeway design elements on freeway crashes. In this study, the crash data of 2007–2009 for expressway ramps, data associated with various ramp design elements, and the annual average daily traffic were collected. Using the data, three negative binomial regression models for the Safety Performance Functions (SPFs) were developed for individual ramp types, including direct, semi-direct, and loop types. Based on the SPFs, Crash Modification Factors (CMFs) were developed for selected design elements. For the validation of developed SPFs and CMFs, the frequency of traffic crash estimated using the SPFs and CMFs was compared with the actual frequency of traffic crashes. As a result, traffic crash prediction using the SPFs and CMFs was found out to be robust in general. The results of this study are expected to contribute to improving traffic safety on freeway ramps by quantifying the effects of diverse decision makings regarding ramp design elements on crashes.

Road traffic accident prediction modelling: a literature review

This paper presents a comprehensive literature review on road traffic accident prediction models (APMs) and crash modification factors (CMFs). The focus is on motorways and higher ranked rural roads and the study was performed within a European road authorities' research project. The priorities for the review were determined by a questionnaire survey on model availability and needs, addressed to national road authorities in Europe and worldwide. The salient literature was reviewed and existing models were assessed in terms of theoretical approaches, model characteristics, implementation conditions, data requirements and available results. The review of CMFs focused on their background and development, the various methods for developing them and the key issues in their application. The review resulted in the development of an APM and CMF inventory that forms the basis for an online repository, with the aim of assisting the practical application of gathered experience on accident prediction.

Examination of the reliability of the crash modification factors using empirical Bayes method with resampling technique

There have been plenty of studies intended to use different methods, for example, empirical Bayes before–after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before–after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle+Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites.

Causal inference framework for generalizable safety effect estimates

This study integrates a causal inference framework to the Empirical Bayes (EB) before-after method to develop generalizable safety effect estimates (i.e., crash modification factor (CMF)). The method considers approaches to estimate the average treatment effect for the treated (ATT), average treatment effect for the untreated (ATU), and average treatment effect (ATE). The current EB method is shown to estimate ATT while ATE is what is typically desired in traffic safety research. Modifications to the current EB method to estimate ATU and ATE are provided.The method is then applied to a dataset with a “no-treatment” scenario where the treatments were: 1) randomly selected and 2) selected based on crash history. Given the “no-treatment” outcome, it is known that the CMFs should have a value of 1 in order to be considered accurate. The standard negative binomial and mixed effects negative binomial regression models were applied in the analysis. It was found that, of the two regression methods, the ATE CMFs developed using the standard negative binomial were the most accurate. Finally, potential sources of bias in the EB method are discussed.

Investigating the influence of passing relief lane sections on safety and traffic performance

As it is well known, one of the keys to improve traffic performance of two lane rural roads with high traffic volumes is to provide passing sections. However, providing a long segment in which the passing maneuver is allowed, is not always feasible, especially when suburban areas are considered. The retrofitting of some road sections by adding a passing relief lane can improve traffic performance by reducing platoons, driver delays and increasing speed. Nevertheless, the effects of this measure on safety may be controversial. With higher traffic speed, the diverging and merging conflicts may escalate and deteriorate the safety conditions of the treated sites. The present research seeks to address this dilemma by presenting an operation and safety study based on experimental data from two lane rural roads. Serious crashes with fatalities and injured people were taken into account in the estimation of the Crash Modification Factors. The Empirical Bayes before-after study was performed for a period from 2005 to 2013, with the exclusion of 2009 when road segments were retrofitted by adding a passing relief lane. Certain improvements in safety were observed for both total and target crashes. The research also encompassed traffic performance by way of investigating the changes in speed and platoon size at the beginning and at the end of the treated sections. The results have shown that the platoon reduction value depends on the length of the passing section and the share of heavy vehicles. The obtained operational results proved less beneficial than expected.

Incorporating Traffic Control and Safety Hardware Performance Functions into Risk-based Highway Safety Analysis

Traffic control and safety hardware such as traffic signs, lighting, signals, pavement markings, guardrails, barriers, and crash cushions form an important and inseparable part of highway infrastructure affecting safety performance. Significant progress has been made in recent decades to develop safety performance functions and crash modification factors for site-specific crash predictions. However, the existing models and methods lack rigorous treatments of safety impacts of time-deteriorating conditions of traffic control and safety hardware. This study introduces a refined method for computing the Safety Index (SI) as a means of crash predictions for a highway segment that incorporates traffic control and safety hardware performance functions into the analysis. The proposed method is applied in a computation experiment using five-year data on nearly two hundred rural and urban highway segments. The root-mean square error (RMSE), Chi-square, Spearman’s rank correlation, and Mann-Whitney U tests are employed for validation.

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM‐Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs.

Safety Performance of Combinations of Traffic and Roadway Cross-Sectional Design Elements at Straight and Curved Segments

AbstractThis study develops various crash modification factors (CMFs) for combinations of traffic and roadway cross-sectional elements at noncurved (i.e., straight) and curved roadway sections usin...

Time series trends of the safety effects of pavement resurfacing

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time.

Assessment of the impact of lane width on arterial crashes

ABSTRACTAs agencies consider strategies for meeting the livability goals (e.g., public health, mobility, and access) of communities, they often repurpose the existing right-of-way of arterials to meet traffic operation or safety goals. Agencies need more definitive guidance on when this repurposing of existing right-of-way can be done without negatively affecting safety. Researchers around the world have considered the safety efficacy of lane width, but none defines lane width design recommendations of urban arterials. This study uses treed regression analysis on crash data collected from four cities of Nebraska to develop predictive models for identifying the crash modification factors of different lane widths. The analysis from five treed regression models indicates that narrower lanes increase the likelihood and the frequency of injury and noninjury crashes on high-volume segments; however more specifically, the results also show that narrower lane widths can be adopted for segments with lower volumes, a sufficient number of through lanes, and lower speed limits without degrading safety. Based on these crash modification factors, the research team creates design recommendations for arterial lane widths using combinations of speed limit, average annual daily traffic per lane, and number of through lanes.

Observations on the Use of Crash Modification Factor-Corrected Crash Prediction Models to Identify Sites with Promise

The Highway Safety Manual (HSM) provides guidance on the application of crash prediction models for network screening, evaluation of alternative designs, and evaluation of implemented safety improvements. A variety of models are available. They may be classified as network screening level (or simple models), project level [or crash modification factor (CMF)–corrected models], and evaluation-level models. The use of crash prediction models to identify sites with promise of safety improvement is often based on assessing the difference between the expected number of crashes ( Ne) at a site and the predicted number of crashes ( Np) for similar sites within the population. A large difference between Ne and Np may denote a safety problem and be used to identify and rank sites with promise. The HSM indicates that CMF-corrected models may be used for network screening purposes. However, issues arise in the analysis regarding the definition of similar sites. When CMF-corrected models are used, the definition of similar sites changes with changes in the CMF adjustments particular to each site. Use of ( Ne − Np) for identifying and ranking sites with promise does not work well when the analysis uses a CMF-corrected estimate of Np. A large combined CMF will increase Np and thereby decrease the value ( Ne − Np). But a large combined CMF may point to a site with promise. This paper examines this phenomenon in detail, reviews a case study, and suggests that simple models may be preferable for identifying sites with promise.

Alternative Approach for Combining Multiple Crash Modification Factors Using Adjustment Function and Analytic Hierarchy Process

The safety effects of multiple treatments have recently emerged as an important issue of validation for the Highway Safety Manual procedures to improve performance of the predictive process. To estimate the combined safety effects of multiple crash modification factors (CMFs) more reliably, several combining approaches have been suggested. However, several critical issues still exist for the combining methods, such as overestimation, region-specific method, and a nonscientific approach. Therefore, this study suggests a novel adjustment method to combine multiple CMFs and to enhance the reliability of combining the safety effects of multiple treatments. Various combinations of CMFs for single and multiple treatments were estimated or obtained from previous studies and used for an exploratory analysis. Moreover, an alternative combining approach with the development of an adjustment function was suggested through comparison with the existing combining methods by using a multicriterion decision-making process. The results show that the proposed alternative combining method provides better estimates than the existing methods and can account for different roadway types and severity levels. Thus, it can be recommended that the safety effects of multiple treatments be estimated with the proposed new combining approach to overcome the overestimation issue and produce results that are more reliable.

Safety Effects of Low-Cost Systemic Safety Improvements at Signalized and Stop-Controlled Intersections

Packages of intersection treatments—including signing, pavement marking, and signal enhancements—were installed at many signalized and stop-controlled intersections in South Carolina. This study evaluated the overall safety-effectiveness of the concurrent implementation of these systemic low-cost treatments as part of the FHWA Evaluations of Low Cost Safety Improvements Pooled Fund. The data set included both urban and rural three-leg and four-leg intersections with two or four lanes on the major road. The study employed an empirical Bayes before-and-after analysis. The aggregate results indicate reductions for all crash types analyzed in this study. For signalized intersections, the crash modification factors (CMFs) are 0.955, 0.893, 0.974, 0.883, and 0.969 for total, fatal and injury, rear-end, right-angle, and nighttime crashes, respectively. The CMFs for fatal and injury and right-angle crashes are statistically significant at the 95% confidence level and the CMF for total crashes is statistically significant at the 90% confidence level. For stop-controlled intersections, the CMFs are 0.917, 0.899, 0.933, 0.941, and 0.853 for total, fatal and injury, rear-end, right-angle, and nighttime crashes, respectively. All CMFs for stop-controlled intersections are statistically significant at the 95% confidence level. An economic analysis shows that systemic, low-cost intersection treatments are cost-effective with conservative benefit–cost ratio estimates of 4.1 for total crashes at signalized intersections and 12.4 for total crashes at stop-controlled intersections.