Total Crashes Research Articles

Evaluating Safety of Type-A Weaving Sections Using Geometric and Traffic Operational Factors

The efficiency and safety of type-A freeway weaving sections in urban areas are constrained by recurrent bottlenecks. Limited space in freeway weaving sections cause traffic congestion and crashes during peak-hours. Various factors, including length of weaving section, continuity of lanes, and number of lanes will have significant effects on the level of service and safety performance of the weaving sections. Eight years (2010-2017) of crash data in the type-A weaving sections was used in this analysis. The objective of this study aims to evaluate geometric design factors and operational factors on total crashes and each of the four crash types: rear-end, sideswipe, angle, and single-vehicle in type-A weaving sections using traditional negative binomial approach and develop crash modification factors (CMFs) to improve safety in the type-A weaving section. The results revealed that on-ramp traffic per hour, off-ramp traffic per hour, non-weaving traffic per hour, weaving ratio, length of the weaving section, direction of the freeway, width of inside shoulder, and width of outside shoulder were influencing crashes in type-A weaving sections. Furthermore, the estimated crash modification factors (CMFs) result revealed that total crashes gradually decrease as inside shoulder width increases. This implies that widening inside shoulder width have positive effects on weaving section safety. In addition, ramp metering, and advisory warning signs could improve safety in type-A weaving sections.

Segmentation Effect on the Transferability of International Safety Performance Functions for Rural Roads in Egypt

This paper examines the transferability of the Safety Performance Function (SPF) of the Highway Safety Manual (HSM) and other 10 international SPFs for total crashes on rural multi-lane divided roads in Egypt. Four segmentation approaches are assessed in the transferability of the international SPFs, namely: (1) one-kilometer segments (S1); (2) homogenous sections (S2); (3) variable segments with respect to the presence of curvatures (S3); and (4) variable segments with respect to the presence of both curvatures and U-turns (S4). The Mean Absolute Deviation (MAD), Mean Prediction Bias (MPB), Mean Absolute Percentage Error (MAPE), Pearson χ2 statistic, and Z-score parameters are used to evaluate the performance of the transferred models. The overdispersion parameter (k) for each transferred model and each segmentation approach is recalibrated using the local data by the maximum likelihood method. Before estimating the transferability calibration factor (Cr), three methods were used to adjust the local crash prediction of the transferred models, namely: (1) the HSM default crash modification factors (CMFs); (2) local CMFs; and (3) recalibrating the constant term of the transferred model. The latter method is found to outperform the first two methods. Besides, the results show that the segmentation method would affect the performance of the transferability process. Moreover, the Italian SPFs based on the S1 segmentation method outperforms the HSM and all of the investigated international SPFs for transferring their models to the Egyptian rural roads.

Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia

Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the association between crash outcomes and available explanatory variables. However, they are unable to capture the latent spatial heterogeneity accurately. Further, the vast majority of previous studies have focused on detailed spatial analysis of crashes from an aggregated viewpoint without considering the attributes of the built environment and land use. This study first uses geographic information systems (GIS) to examine crash hotspots based on two severity groups, seven prevailing crash causes, and three predominant crash types in the City of Dammam, Kingdom of Saudi Arabia (KSA). GIS-based geographically weighted regression (GWR) analysis technique was then utilized to uncover the spatial relationships of traffic collisions with population densities and relate it to the land use of each neighborhood. Results showed that Fatal and Injury (FI) crashes were mostly located in residential neighborhoods and near public facilities having low to medium population densities on highways with relatively higher speed limits. Distribution of hotspots and GWR-based analysis for crash causes showed that crashes due to “sudden lane deviation” accounted for the highest proportion of crashes that were concentrated mainly in the Central Business District (CBD) of the study area. Similarly, hotspots and GWR analysis for crash types revealed that “collisions between motor vehicles” constitute a significant proportion of the total crashes, with epicenters mostly stationed in high-density residential neighborhoods. The outcomes of this study could provide analysts and practitioners with crucial insights to understand the complex inter-relationships between traffic safety and land use. It can provide useful guidance to policymakers for better planning and effective management strategies to enhance safety at zonal levels.

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.

A novel design method of the impact zone of a high-speed train

The design of vehicle impact zone is of great significance for passive safety protection of trains. This study introduces a novel design method of impact zone based on the concept of linear impact force, which has a linear increase of impact force with the increase of displacement. A three-dimensional dynamic model of train collision was constructed and validated. Here, the linear impact force slope of energy absorbing devices were selected as design variables, whereas the total mean acceleration along the train (TMA) and the total crash displacement (TS) were set as objectives. The multi-objective particle swarm optimisation was used to seek for an optimal design of the impact zone, where the response surface method was adopted to formulate the TMA and TS. The results indicate that the optimised design has smaller impact acceleration peak value, higher utilisation efficiency of compression stroke, and more uniform distribution of crashing displacement.

An examination of relationships between cannabis legalization and fatal motor vehicle and pedestrian-involved crashes

Objective While attention has been given to how legalization of recreational cannabis affects traffic crash rates, there was been limited research on how cannabis affects pedestrians involved in traffic crashes. This study examined the association between cannabis legalization (medical, recreational use, and recreational sales) and fatal motor vehicle crash rates (both pedestrian-involved and total fatal crashes). Methods We used crash data from the Fatality Analysis Reporting System (FARS) to calculate monthly rates of fatal motor vehicle crashes and fatal pedestrian-involved crashes per 100,000 people from 1991 to 2018. Changes in monthly crash rates in three states that had legalized cannabis (Colorado, Washington, and Oregon) were compared to matched control states using segmented regression with autoregressive terms. Results We found no significant differences in pedestrian-involved fatal motor vehicle crashes between legalized cannabis states and control states following medical or recreational cannabis legalization. Washington and Oregon saw immediate decreases in all fatal crashes (-4.15 and −6.60) following medical cannabis legalization. Colorado showed an increase in trend for all fatal crashes after recreational cannabis legalization and the beginning of sales (0.15 and 0.18 monthly fatal crashes per 100,000 people). Conclusions Overall findings do not suggest an elevated risk of total or pedestrian-involved fatal motor vehicle crashes associated with cannabis legalization.

U.S. alcohol associated traffic injuries and fatalities from 2014 to 2018.

Alcohol-impaired motor vehicle collision (MVC) fatalities comprise almost a third of total crash fatalities in the United States (U.S.). They also impose 20% of the total costs of MVCs annually. This study aims to evaluate an association between blood alcohol concentration (BAC) and number of crash injuries and fatalities from 2014 to 2018 in the U.S. Additionally, we aim to recommend solutions to reduce alcohol-impaired driving related injuries and fatalities. A retrospective analysis of National Highway Traffic Safety Administration (NHTSA) data of crash injuries, fatalities, and BAC levels (0.00g/dl, 0.01-0.07g/dl, and≥0.08g/dl) from 2014 through 2018. Descriptive statistical analysis and independent sample t-tests were conducted, with significance defined as p<.05. Compared to BAC 0.01-0.07g/dl,BAC ≥0.08g/dl resulted in significantly more injuries (6779 vs. 1357, p<.001) and fatalities (10,522 vs. 1894, p<.001). BAC level≥0.08g/dl produced significantly greater injuries and fatalities in comparison to lower BAC levels evaluated. Given the effects of alcohol-impaired driving on MVCs, the legal BAC level should be re-evaluated to protect citizens and reduce incidence of alcohol related traffic injuries and fatalities. Educational programs promoting responsible alcohol consumption need to be in place for individuals at high risk for driving under the influence.

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.

A conceptual framework to understand the role of built environment on traffic safety

IntroductionMany U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. Methods: Three years (2011–2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. Results: Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. Conclusions: This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.

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.

A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution

Real-time crash prediction plays a key role in enhancing traffic safety as well as mitigating disruptions to road users. The further improvements of predictability require the systemic analysis of crash likelihood within the driver–vehicle–environment triptych. This study presents a proactive decision support system that can predict crash events based on vehicle kinematics, driver inputs, roadway geometric features and real-time weather data. Modeling approaches that rely on Random forest, Support Vector Machine and Multilayer Perceptron machine learning techniques were applied to establish efficient crash predictions. Moreover, crash events are generally unexpected and occur rarely, thus classification results can yield deceivingly high prediction performance which are usually driven by the majority class at the expense of having poor performance on the crucial minority class. Therefore, this paper attempts to add to the current knowledge by investigating crash likelihood based on compared different data balancing techniques to improve the predictive performance through three balancing techniques: over-sampling, under-sampling and synthetic minority over-sampling (SMOTE). The highest performances have been acquired using SMOTE strategy as MLP achieved a 94.5% precision, 94.2% f1-score, 93.7% AUC and 95.3% recall, while SVM achieved a 91.5% g-mean. Furthermore, results indicated that more than 62% of total crashes have been reported in downhills and curved downhills, and 44% of all crash instances have been reported during both snow and rain weather patterns. Overall, the findings highlighted the significance of the explanatory variables associated with potential crash events and can suggest to decision-makers a safe and credible system for enhancing traffic safety.

Safety Effects of Horizontal Curve Reliability Index

Recent advancements in analytical processes have used probabilistic approaches to examine the efficacy of the point mass model (and other Green Book models) to develop reliability-based approaches for geometric design. However, there has been minimal research establishing the link between reliability measures and substantive safety (expressed through crash frequency). The objective of this paper is to use empirical data supporting the calculation of reliability index for existing horizontal curves and to estimate the relationship between reliability index and crash frequency. Other horizontal curve-related characteristics that may have an impact on crash frequency on horizontal curves for rural two-lane highways and rural freeway facilities are controlled for in the evaluation. The safety analysis showed that the wet pavement reliability index was significantly associated with crash frequency for total curve-related crashes, single-vehicle run-off-road crashes, rollover crashes, truck-related crashes, and weather-related crashes. The relationship was strongest for the reliability index in its continuous form, meaning that the effect is continuous across the range of wet pavement reliability that was observed.

Safety effects of road pavement resurfacing: A case study of city-wide scale projects in China

This study explores the influence of resurfacing projects on traffic safety. A case study of city-wide road resurfacing projects conducted from July 2014 to February 2015 in China was adopted to carry out the analysis. The crash data of two years before and after the treatment were collected. Firstly, the safety performance function, generalized linear regression, and Gaussian Process centered on Poisson regression were applied to predict the expected number of crashes in the after period if the resurfacing was not applied. Then, the observational before-after study was applied to compare the safety performance of the before and after period of resurfacing projects. Finally, crash modification factors for various target accidents in different time durations were calculated to evaluate the influence of the resurfacing projects on traffic safety. The results show that the resurfacing projects along with some minor changes, such as widen shoulders and optimized lane markings have remarkably good effects on reducing both the crashes counts and the level of severity. The degree of good effects of the resurfacing projects on different types of crashes is ranked as follows: road crashes with slight consequences > total crashes > road crashes with severe consequences > intersection crashes.

Crash modification factors for adaptive traffic signal control: An Empirical Bayes before-after study

Adaptive traffic signal control (ATSC) is a novel traffic management system that is often deployed at high-volume intersections in order to mitigate traffic congestion and improve travel time reliability. While past studies have demonstrated its operational effectiveness, relatively few have focused on safety performance. Those that have tend to suffer from limitations including small sample sizes, insufficient study designs, or the lack of consideration of potential temporal and corridor effects after ATSC installation. Furthermore, results from previous studies are mixed: while many studies point to a safety improvement, more recent studies seem to indicate that ATSC systems might increase crash frequency. In light of this, a comprehensive Empirical Bayes (EB) before-after observational study was conducted using ATSC data collected throughout Pennsylvania. Crash modification factors (CMFs) were estimated based on the following different case scenarios: crash severity levels and crash types (total, fatal and injury, rear-end, and angle crashes); intersection locations (all intersections and intersections along corridors only); and, intersection configurations (3-leg and 4-leg). Temporal trends for intersection-level CMFs were examined using annual crash data in the after period. Corridor-level CMFs were also developed to quantify changes in safety performance along corridors with ATSC installed. The results suggest that ATSC is associated with a nominal increase in total and angle crashes, and an expected decrease in fatal plus injury crashes and rear-end crashes. However, the results were not statistically significant. The safety effect estimates are similar when considering intersection locations and configurations. In addition, the temporal trend analysis indicates that the safety effectiveness does not vary annually in the after period, suggesting no obvious novelty effect associated with ATSC. Finally, the magnitude of the corridor-level CMFs are slightly lower than the intersection-level CMFs, except for rear-end crashes.

Exploration of Hazardous Material Truck Crashes on Wyoming’s Interstate Roads using a Novel Hamiltonian Monte Carlo Markov Chain Bayesian Inference

Crash severity of a hazardous material (HAZMAT) transporting truck increases manyfold compared with normal truck crash because of the possible exposure to dangerous substances. Crashes which involve a HAZMAT truck might result in a catastrophic incident causing horrendous damage to individuals involved in the crash. In-transit HAZMAT crashes in Wyoming caused a total damage of $3.1 million from 2015 to 2018. HAZMAT crashes on interstate roads represented 22% of the total HAZMAT crashes causing a total damage of $2.2 million, representing 71% of the cost of total damage. Previous studies in Wyoming investigated all vehicle crashes, including large truck crashes, but none has analyzed HAZMAT-related crashes or accounted for its type as a contributing factor. This study fills the gap by analyzing crash injury severity of HAZMAT-related crashes on all interstate freeways in Wyoming. Furthermore, the study introduces the No-U-Turn (NUT) Hamiltonian Monte Carlo (HMC) method of hierarchical Bayesian analysis into HAZMAT crash injury severity analysis. In recent developments, NUT HMC has been proven to be the most efficient Markov Chain Monte Carlo (MCMC) sampling method. The results showed that 30% of the unobserved heterogeneity arises from variation in summer and winter crashes which justifies the use of hierarchical model. Among the other covariates investigated, the population-averaged effects showed that number of trucks involved, hit-and-run crashes, animal-vehicle crashes, work-zone-related crashes, collision type, percentage of females involved, drivers’ drug/alcohol use, seat-belt use, crash location, roadway curves, and surface conditions significantly impact HAZMAT crash injury severity.

Segmentation effect on developing safety performance functions for rural arterial roads in Egypt

This paper presents the development of safety performance functions (SPFs) for major rural arterial roads in Egypt. Four segmentation methods were used for the SPFs development: (1) constant section length of one kilometer (S1), because crash data are reported for every kilometer; (2) homogenous sections (S2), following the highway safety manual (HSM) recommendation; (3) variable sections with respect to the presence of curvatures (S3); and (4) variable sections with respect to the presence of both curvatures and U-turns (S4). The generalized linear modeling technique was used for SPFs development using the stepwise procedure, with/without considering time effect (i.e. year-to-year variation). The Akaike information criterion, Pearson product-moment correlation coefficient, and mean prediction bias along with the cumulative residual (CURE) plots are used to evaluate the prediction accuracy of the proposed models. The segmentation method was found to affect the prediction accuracy of the calibrated SPFs. For total crashes, the calibrated SPF model based on the S3 segmentation method with the time-trend model form is superior to SPFs calibrated based on other segmentation methods. While S1 and S4 segmentation methods without the time-trend model form produced the best SPFs for “fatal and injury” crashes and property damage only crashes, respectively. In addition, the results showed that by increasing each of the pavement, shoulder, and median widths, the probability of crash occurrence is likely to decrease. In addition, the presences of either horizontal curves and/or accesses are most likely to reduce the probability of crash occurrence.

Applying a joint model of crash count and crash severity to identify road segments with high risk of fatal and serious injury crashes

Both crash count and severity are thought to quantify crash risk at defined transport network locations (e.g. intersections, a particulate section of highway, etc.). Crash count is a measure of the likelihood of occurring a potential harmful event, whereas crash severity is a measure of the societal impact and harm to the society. As the majority of safety improvement programs are focused on preventing fatal and serious injury crashes, identification of high-risk sites—or blackspots—should ideally account for both severity and frequency of crashes. Past research efforts to incorporate crash severity into the identification of high-risk sites include multivariate crash count models, equivalent property damage only models and two-stage mixed models. These models, however, often require suitable distributional assumptions for computational efficiency, neglect the ordinal nature of crash severity, and are inadequate for capturing unobserved heterogeneity arising from possible correlations between crash counts of different severity levels. These limitations can ultimately lead to inefficient allocation of resources and misidentification of sites with high risk of fatal and serious injury crashes. Moreover, the implication of these models in blackspot identification is an important, unanswered question.While a joint econometric model of crash count and crash severity has the flexibility to account for the limitations mentioned previously, its ability to identify high-risk sites also needs to be examined. This study aims to fill this research gap by employing the joint model for blackspot identification. Using data from state-controlled roads in Queensland, Australia, a new risk score is developed based on predicted crash counts by severity, weighted by the cost ratio of severity levels. This weighted risk score is then used for identifying road segments with high risk of fatal and injury crashes. Results show that the joint model of crash count and crash severity has substantially improved prediction accuracy compared to the traditional count models. The correlation between crash counts of different severity levels captures the unobserved heterogeneity caused by the extra-variation in total crash counts and moderates the parameters in the joint model. In comparison with the traditional approaches, the proposed weighted risk score approach with the joint model of crash count and crash severity leads to the identification of a higher number of fatal and serious injury crashes in the top ranked sites flagged for safety improvements.

Assessing the safety impacts of raising the speed limit on Michigan freeways using the multilevel mixed-effects negative binomial model

Objectives: The main objective of this study was to assess the change in crash patterns associated with speed limit changes from 55 mph to 70 mph that occurred on some of Michigan freeway segments between year 2005 and year 2010.Method: Many of the statistical methods used in the past to evaluate the safety impacts of raising the speed limit on freeways lack the ability to address one or more critical issues inherent in count data, such as omitted-sample bias, over-dispersion and regression-to-the-mean bias. This study used multilevel mixed-effects negative binomial regression to address these limitations, with an additional advantage of controlling for intra-cluster correlation of crashes on each freeway corridor and segments nested in the same corridor. Changes in the crash patterns between the year 2000 and year 2015 were investigated on test sites that had a change of speed limit from 55 mph to 70 mph, relative to control sites where the speed limit was maintained at 55 mph.Results: The inclusion of random effects improved the model’s ability in explaining observed crash variations on the selected freeway segments, as indicated by test statistics such as the log-likelihood ratio test, the Akaike information criterion and Bayesian information criterion (BIC)values. Further, random effects improved the significant speed limit change fixed effects during model calibration. The final mixed-effects model indicated a significant increase in fatal and injury crashes (FI), total crashes (KABCO) and road departure crashes by 11.9 percent, 21.0 percent and 23.3 percent, respectively, on freeway segments where the speed limit was raised from 55 mph to 70 mph. The increase in road departure crashes was more pronounced on curved freeway segments with the raised speed limit compared to straight segments with no speed limit changes.Conclusions: The 15 mph increase in the speed limit on Michigan freeways had a significant association with the increase in fatal and injury crashes, total crashes and road departure crashes. The elevated crash risks associated with the speed limit increase suggest that further studies are needed to understand changes in drivers’ behaviors following a speed limit increase.

Red light camera interventions for reducing traffic violations and traffic crashes: A systematic review.

Red light camera interventions for reducing traffic violations and traffic crashes: A systematic review.

Safety evaluation of drowsy driving advisory system: Alabama case study

Introduction: The present study discusses roles, characteristics, and safety assessment of a drowsy driving advisory (DDA) system, implemented on rural interstates of Alabama. The DDA system is an engineering countermeasure designed to reduce the likelihood of drowsy driving crashes. It consists of a series of roadside signs with warning and advisory messages for drowsy drivers. The DDA system was implemented upstream of rural rest areas based on a comprehensive crash analysis. Method: A post-implementation study was conducted three years after the DDA system implementation to assess its safety effects. An empirical bayes (EB) method along with predictive methods of the Highway Safety Manual was used in the safety assessment. To overcome the underreported issue of drowsy driving crashes in the crash analysis, the present study used a concept called, expanded definition of drowsy driving (EDD) crashes. Result: The analysis found that the DDA system could reduce total and EDD crashes by 64% and 49%, respectively. It is important to note that such huge crash reduction effects are due to a combined effect of both rest areas and the DDA system, not because of a single treatment. The safety effect of a rest area itself, without considering the effect of the DDA system, was also investigated. Results show that total and EDD crashes would increase about 12–45% and 5–33%, respectively if there is no presence of a rest area. Conclusion: Our findings conclude that the DDA system could significantly reduce both total and drowsy driving crashes when it cooperates with a rest area facility. Practical Application: The findings also provide the guidance of using the DDA system on high-speed roads as a safety countermeasure of drowsy driving crashes. Readers can find details of the DDA system used in this study with its layout, dimension, and roadside safety messages.