Roadway Departure Crashes Research Articles

Crash modification factors for high friction surface treatment on horizontal curves of two-lane highways: A combined propensity scores matching and empirical Bayes before-after approach

Horizontal curves are locations that, as a result of the changing alignment, may be a contributing factor in roadway departure crashes. One low-cost countermeasure to mitigate crashes at these locations is the installation of the high friction surface treatment (HFST), which increases roadway friction and is intended to help keep drivers on the roadway when traversing a horizontal curve. This treatment has been implemented at numerous curves in Pennsylvania, but the overall safety effectiveness is not known. The purpose of this study is to estimate a suite of Crash Modification Factors (CMFs) for HFST applied to curve sections of undivided two-lane roadways. A novel combination of the empirical Bayes observational before-after study design and propensity score matching was used to estimate CMFs for multiple crash types, crash severities, and roadway settings (urban and rural). Propensity score matching was implemented to identify the most appropriate reference group to use within the empirical Bayes methodology. The results indicate that the installation of HFST is associated with a statistically significant decrease in all crash types and severities considered.

Reliability-based analysis of horizontal curve design by evaluating the impact of vehicle automation on roadway departure crashes and safety performance

Roadway departure (RwD) crashes are significant safety concerns, especially at horizontal curves. The design of these curves plays a crucial role in mitigating RwD crashes. Thus, a thorough understanding of the interaction between driver behavior, vehicle automation, and geometric design is vital. Substantive safety, which emphasizes the inherent safety in a road's design and function, serves as the foundation of our approach. Building on this, the study employs a safe system approach to investigate the performance of horizontal curves under both non-automated and partially automated conditions, using a reliability-based analysis focusing on Stopping Sight Distance as the primary driver demand. Factors including Perception-Brake Time and Take-Over Time for automated vehicles are examined. The analysis covers horizontal curves, characterized by their geometric design and crash data. Our findings highlight a shift in the performance of horizontal curves under automation, emphasizing the need to consider automation in roadway design within the safe system approach. This study demonstrates how a reliability-based analysis can guide designers in making informed decisions regarding the geometric design of horizontal curves to reduce RwD crashes. To enhance transportation safety in the era of increasing automation, ongoing exploration of the relationships between driver behavior, automation, and road design is indispensable.

Crash modification factors of rumble strips on horizontal curves of two-lane rural roads: A propensity scores potential outcomes approach

Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips.

Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition

Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

Roadway departure crashes in Vietnam: identifying the sequence of events, causes, and countermeasures by applying the crash reconstruction technique

The horizontal curve segment is where traffic crashes often occur in rural areas of Vietnam. Although many safety countermeasures have been implemented, their effectiveness still needs to improve. The main reason is that the implemented countermeasures are unfit for the crash causes. More specifically, determining crash causes and contributory factors to the severity of crashes needs to be more accurate because of the need for more critical data and specialized analytical tools. This study applies the crash reconstruction technique to simulate, reconstruct, and analyze crashes that occurred at horizontal curve segments to address the abovementioned issues. The study results have reproduced the sequence of events, crash causes, and contributory factors to the severity of roadway departure crashes of four-wheeled vehicles in rural areas. This research is a scientific foundation to help researchers choose the suitable technique to reconstruct traffic crashes and identify the appropriate solutions for enhancing traffic safety at horizontal curve segments in Vietnam.

Developing a Data-Driven Network Screening Procedure for Systemic Safety Approach

Systemic analysis is considered an important safety analysis approach that is complementary to the Highway Safety Manual hotspot analysis. The network screening step in systemic analysis is to identify sites with characteristics that are associated with specific types of severe crashes. Traditionally, determining the risk scores involves subjective criteria. This research aims to develop a data-driven approach to replace the subjective methods used in the past. To achieve the research objective, this study collected roadway and crash data from the Connecticut Department of Transportation. A data-driven crash risk factor categorization methodology is proposed to estimate accurately the performance measures indicating crash risks. Moreover, this study proposes and compares four different risk scoring matrices to identify an optimal risk scoring method that is attuned with the principles of the systemic approach to safety as well as providing additional insights on justifying the systemic safety analysis results. The proposed methodology is implemented to conduct network screening for severe roadway departure crashes and later validated using severe aggressive-driving related crashes. Risk-based network screening results indicate that risk scores derived from normalized crash over-representation provide additional emphasis on sites with low traffic volume that are associated with high severe crash counts. The highest modified crash rate was obtained using normalized crash over-representation based risk scores indicating that the proposed network screening methodology can not only identify roadway attributes that are correlated with severe crashes but also account for low-volume roadway sites with severe crashes. The validation analysis indicated proposed method is transferrable to different emphasis area related crashes.

Safety evaluation of centerline rumble strips on rural two-lane undivided highways: Application of intervention time series analysis

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

Advanced Driver Assistance System

bstract: Many people die each year in roadway departure crashes caused by driver inattention. Lane detection systems are useful in avoiding these accidents as safety is the main purpose of these systems. Such systems have the goal to detect the lane marks and to warn the driver in case the vehicle tends to depart from the lane. A lane detection system is an important element of many intelligent transport systems. Lane detection is a challenging task because of the varying road conditions that one can come across while driving. In the past few years, numerous approaches for lane detection were proposed and successfully demonstrated. In this paper, a comprehensive review of the literature in lane detection techniques is presented. The main objective of this paper is to discover the limitations of the existing lane detection methods.

Long-Term Performance and Safety Implications of High-Friction Surfacing Treatments

Although high-friction surface treatments (HFSTs) are a proven efficient countermeasure for the reduction of roadway departure crashes, their long-term pavement performance has yet not been fully understood with large-scale field monitoring. This paper presented and analyzed 3 years of monitoring results on 36 HFST sites located in 12 states in the US ranging from 0 to 11 years in treatment age. Taking the traffic directions and number of lanes at each site into consideration, a total of 65 HFST segments were measured. Onsite data were collected by a Grip Tester for pavement friction, the PaveVision3D laser imaging system for condition evaluation and high-resolution pavement images, and the AMES high-speed profiler for surface texture and roughness. The evolutions of HFST surface characteristics and field performance were evaluated. It was found that most HFST sites continued providing superior friction values compared with the neighboring nontreated surfaces. Cracking and surface delamination were observed as the major failure types on a few sites. The delaminated area exhibited higher roughness values and lower friction numbers. Subsequently, a multivariant friction prediction model was developed to assist in understanding its influencing factors and deterioration trends. Furthermore, a safety performance function was built for crash frequency estimation on HFST sites, through which the benefit of HFST in reducing roadway departure crashes could be further quantified.

Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: Association knowledge using data mining approach

Introduction: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. Data: Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with and without streetlight). Method: This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized. Results and conclusions: Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight condition, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In dark lighting condition (with and without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15–24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep), and colliding with animal(s). Practical Applications: The findings also reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.

Effectiveness of front crash prevention systems in reducing large truck real-world crash rates

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017–2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions—12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.

Exploring the influential factors of roadway departure crashes on rural two-lane highways with logit model and association rules mining

• Simultaneous presence of multiple factors results in roadway departure crashes. • Animal, no streetlight, drug, alcohol influence rural two-lane road departure. • Associated factors are – animal presence & no streetlight, male driver & 12am – 6am. • Other associations are – male driver & no passenger, use of both drug & alcohol. • Association rule mining facilitates strategic countermeasure development. Roadway departure (RwD) crashes are a major contributor of rural two-lane (R2L) highway crashes and fatalities. For targeted reduction of crashes and fatalities due to roadway departure, a thorough understanding of factors associated with RwD crashes is necessary. This study quantitatively assessed the available pre-crash factors that might influence the RwD crashes by developing a logit model comparing roadway, crash environment, and the vehicle and driver-related characteristics of 122,978 crashes that occurred in Louisiana over thirteen years. With a high prediction accuracy (81.9% area under the receiver operating characteristics curve), the model presented significant individual associations across crash characteristics with RwD crashes on R2L highways, for example – animals on roadways, snow/sleet/hail, 50–55 mph speed limit, AADT of 1,001 to 5,000 vehicles per day, drug intoxication, motorcycles, driving during 12 am to 6 am, curve radius of 501–1,000 ft., absence of streetlight, alcohol intoxication. Investigation on these top factors using association rules mining reveals findings such as – a higher likelihood of RwD crashes can be strongly associated animal presence coupled with the absence of streetlights, male drivers during the early morning (12 am to 6 am), male drivers driving with no passengers, drivers being intoxicated by both drugs and alcohol, etc. Findings from this study are expected to help highway safety specialists not only in identifying and predicting RwD crashes but also in an improved understanding of associated contributing factors leading to the application of proper countermeasures for the strategic reduction of RwD crashes.

Evaluating In-Vehicle Sound and Vibration during Incursions on Sinusoidal Rumble Strips

Rumble strips (RS) are a countermeasure used to reduce roadway-departure crashes by providing audible and haptic alerts to the driver when a vehicle is departing the roadway. This study evaluated the feasibility of using sinusoidal RS as a substitute for more traditional rounded RS. A van, a passenger car, and a heavy vehicle were equipped with sound and vibration sensors to measure the interior noise and haptic feedback of each RS design. A set of typical conditions (with interior climate control fan and radio turned on) were also tested. Data from 75 RS strikes were analyzed. Experimental results demonstrated that the rounded RS doubled interior noise for the passenger car and van (11.3 dBA, 10.0 dBA) but the sinusoidal RS also generated a clearly noticeable interior alert for the passenger car and van (5.8 dBA, 4.6 dBA). The haptic alert provided an increase over the human perception threshold of vibration for all vehicles. The sinusoidal RS interior alert was detectable and within the acceptable range, but not clearly noticeable (5 dBA) when the climate control and radio were active. Alert levels for the rounded RS were >10 dBA, doubling the amount of interior noise for all ambient factor groups (11.2–14.4 dBA).

A penalized-likelihood approach to characterizing bridge-related crashes in New Jersey

Objective A roadway departure crash is one in which a vehicle crosses an edge line, a centerline, or otherwise leaves the traveled way. These crashes that involve run-off-road and cross-median/centerline head-on collisions tend to be more severe than other crash types. According to the NHTSA Fatality Analysis Reporting System database, a total of 7,833 people perished in crashes involving fixed roadside objects in 2017, accounting for 21 percent of the total number of fatalities in the United States. Several previous studies have reported that rural bridge-related crashes result in more fatalities due to their being mostly the fixed-object crash type. As such, further in-depth investigation of this type of crash is necessary. Due to the lack of a comprehensive database that includes bridge-related crashes and bridge characteristics, identifying the key factors contributing to this type of crash is a challenging task that is addressed in this paper. Method Study team gathered and compiled five years (2011–2015) of crash data from the New Jersey crash database and the characteristics of bridges from the Long-Term Bridge Performance portal. A Firth’s penalized-likelihood logistic regression model was developed to examine the impact of explanatory variables on crash severity. Results Based on the five years (2011–2015) of crash data, significant factors (i.e., driver age, weather conditions, surface conditions, lighting conditions, speed limit, roadway characteristics, and direction of traffic) were identified that affect the severity of bridge-related crashes in Middlesex County, New Jersey. Conclusion This model is an appropriate tool for predicting the impact of all the confounding variables on the probability of bridge-related crashes while also considering the rareness of the event. Based on the obtained odds ratio, the various effects of the identified variables are discussed, and recommendations made regarding countermeasures policymakers can establish to reduce the number of these crashes in New Jersey.

Temporal stability of driver injury severity in single-vehicle roadway departure crashes: A random thresholds random parameters hierarchical ordered probit approach

This study examines contributing variables affecting the driver injury-severity in single-vehicle roadway departure crashes. To capture the threshold heterogeneity and unobserved heterogeneity, the random thresholds random parameters hierarchical ordered probit (HOPIT) approach is employed using the crash data collected from 2014 to 2017 in the State of North Carolina. Three injury severity levels are considered: severe injury, minor injury, and no injury. Attributes that potentially affect crash severity are examined, including driver, crash, roadway, and environmental characteristics. A series of likelihood ratio tests are conducted to examine the temporal stability of factors across different studied years. The marginal effects of factors in different injury-severity models are compared. Significant temporal instability is found among the studied year periods. The threshold value estimated using the random threshold random parameters HOPIT model is found to be random parameters and determined by specific explanatory variables. Additionally, the effects of some factors (e.g., alcohol, curved roadway, passenger car, SUV, and wet/water surface) on injury severity are relatively stable, while others (e.g., female driver, collector, and clear weather) present temporal unstable effects. Regarding the temporal instability, decision-makers need to treat the factors carefully to avoid developing incorrect countermeasures.

Rule-based safety prediction models for rural two-lane run-off-road crashes

• Analyzed rural two-lane run-off-road crashes in Louisiana. • Applied both statistical and machine learning models to determine the best-fit model. • Cubist provides the highest accuracies with rules-based regression models. • Rules-based models are capable in addressing localized issues by improving prediction accuracies. During 2015–2017, the yearly average of fatalities caused by roadway departure (RwD) crashes was 19,233 in the U.S. Roadway departure crashes, or crashes in which a vehicle crosses an edge line, centerline, or otherwise leaves the traveled way, account for 52 percent of all traffic fatalities in the U.S. during this time. A majority of RwD crashes are run-off-road (ROR) crashes; these are crashes that result in a vehicle crossing the edge line on either side of the roadway. In this study, the research team analyzed seven years (2010–2016) of rural two-lane ROR crash data from Louisiana to better comprehend ROR crashes in refining safety predictions for rural two-lane highways. Statistical model (negative binomial model) and three separate machine learning models (random forest, support vector machine, and Cubist) were applied to determine the best fit models. Overall, Cubist is characterized by a better performance in estimating ROR crashes on rural two-lane roadways. The Cubist approach introduced rules-based safety performance functions (SPFs) for total and fatal and injury crashes. This approach will be beneficial for the safety practitioners in tackling localized issues in crash data with an emphasis on prediction accuracy.

Empirical Bayes before-after evaluation of horizontal curve warning pavement markings on two-lane rural highways in Pennsylvania

Roadway departure crashes contribute to a large proportion of fatal and injury crashes in the United States. These crash types are more likely to occur along horizontal curve sections of a roadway. Countermeasures that prevent vehicles from departing the roadway is one method to mitigate roadway departure crashes. Pennsylvania has deployed on-pavement horizontal curve warning markings in advance of horizontal curves on two-lane rural highways as a roadway departure crash reduction strategy. This study used an Empirical Bayes (EB) before-after study design to evaluate the safety effects of the horizontal curve warning pavement markings. A total of 263 treatment sites and more than 21,000 reference sites were included in the evaluation. Crash modification factors were developed for total, fatal plus injury, run-off-road, nighttime, nighttime run-off-road, and nighttime fatal plus injury crashes. The point estimates for each of these crashes ranged from 0.65 to 0.77 – the results were statistically significant for total and fatal plus injury crashes at the 95th-percentile confidence level.

Analysis of Single-Vehicle Roadway Departure Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity

Roadway departure crashes are one of the core emphasis areas in Strategic Highway Safety Plans (SHSP). These crashes, especially on rural roads, lead to a disproportionately higher number of fatalities and serious injuries. The focus of this study is to identify and quantify the factors affecting injury-severity outcomes for single-vehicle roadway departure (SV-RwD) crashes on rural curved segments in Minnesota. The crash data are extracted from the Highway Safety Information System (HSIS) from 2010 to 2014. This study applied a mixed logit with heterogeneity in means and variances approach to model driver-injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, traffic, environmental conditions, or any combination of these attributes. This analysis adds value to the growing body of literature because it uncovers some unobserved heterogeneity in the form the attributes specific to driver-injury severities in contrast to the standard mixed logit approach. The model results indicate that there is a complex interaction of driver characteristics and actions (male drivers, aged below 30 years of age, and unsafe speed), roadway and traffic characteristics (two-lane undivided road, county roadways, and low traffic volume), environmental conditions (adverse weather, cloudy weather, dark conditions, and dry surface conditions), and vehicle characteristics (vehicle type—sport utility vehicle involved in rollover crashes). The results also provide some evidence of the effectiveness of a highway curve safety improvement program implemented in one of the Minnesota Department of Transportation (DOT) districts.

Evaluation of Sequential Dynamic Chevron Warning Systems on Rural Two-Lane Curves

Roadway departure crashes are a significant safety concern. The majority of these crashes occur on rural two-lane roadways, with a disproportionate number occurring on horizontal curves. The average crash rate for horizontal curves is about three times that of other highway segments. Curve-related crashes involve a number of roadway and driver causative factors, with speed being a preeminent factor. Implementing safety countermeasures on rural horizontal curves to address these crash types can improve the safety performance. Chevron alignment signs provide additional emphasis and guidance for drivers negotiating curves. To further emphasize the curve, some agencies have started using a sequential dynamic chevron warning system (SDCWS) which uses light emitting diode (LED) lights within each chevron sign to provide sequential lighted guidance through the curve. The research team evaluated 18 rural horizontal two-lane curves where a SDCWS had been implemented. Reference sites with similar characteristics were selected and included in the study. Models were developed using an empirical Bayes methodology for non-intersection (total) crashes and injury crashes. Additional countermeasures were present at some of the sites and were included in the model. Using these data, the study developed crash modification factors (CMFs) for SDCWS with a resulting CMF of 0.34 for total crashes (non-intersection) and 0.49 for injury crashes.