Rear-end Crashes Research Articles

Analysis of following vehicle driver injury severity in rear-end collision on straight road based on LightGBM and SHAP

Rear-end accidents, as one of the common accident types, may cause serious injuries to the driver and passengers. There are mutual coupling effects among causal features of rear-end accidents, and traditional analytical methods may lead to modeling distortion due to assumption constraints. In this study, the Light Gradient Boosting Machine (LightGBM), a machine learning algorithm, is used to model the injury severity of the following vehicle driver in a rear-end crash accident on a straight road, based on the data from the China In-depth Accident Study data. The Shapley Additive Explanations (SHAP) method was used to interpret the results of the LightGBM model and to analyze the relationship between factors and driver injury severity. The results show that location familiarity, willingness to take risks, and driving time have a significant impact on the injury severity for following vehicle drivers involved in rear-end crashes. Cloudy conditions increase a driver’s risk of being involved in a fatal or injured rear-end collision. In rain, snow, hail, and foggy conditions, have a higher propensity to cause driver fatalities in the crash event. The rear-end crash of passenger cars resulted in a higher death probability of the following vehicle driver compared to sedan and truck, and female drivers are more likely to be involved in uninjured accidents compared to males. These results are informative for preventing rear-end accidents and reducing the extent of accidental injuries.

Risk assessment of rear-end crashes by incorporating vehicular heterogeneity into Bayesian hierarchical extreme value models

Extreme value theory (EVT) has been extensively used to assess road safety with traffic conflicts. However, most studies used pooled models that do not account for vehicle heterogeneity which is characterised by different static and dynamic vehicle parameters such as size, speed, acceleration, and braking capacity. This study proposes a risk assessment technique for rear-end crashes while incorporating vehicular heterogeneity. Video-based trajectory data were collected at four uncontrolled intersections, and conflicts were estimated using modified time-to-collision (MTTC). The crash risk was derived from the observed conflicts using pooled as well as Bayesian hierarchical EVT models. Unlike the pooled model, the hierarchical model revealed that crash risk varies across leader-follower pairs. Interactions that involve cars or light commercial vehicles with slow-moving vehicles are riskier. This study highlights the importance of incorporating vehicular heterogeneity in crash risk assessment. The proposed methodology can be utilised for more accurate risk assessment in heterogeneous traffic.

Disturbances and safety analysis of linear adaptive cruise control for cut-in scenarios: A theoretical framework

Although adaptive cruise control (ACC) has been widely analyzed in the car-following process, the way it reacts to cut-in maneuvers has been largely ignored. The cut-in maneuvers may trigger multiple disturbances to traffic, resulting in traffic oscillations and corresponding rear-end crashes. Hence, this study provides a theoretical analysis framework to model the disturbance evolution for ACC under cut-in scenarios. Specifically, we first derive the general ACC dynamic evolution based on the widely adopted ACC (i.e., linear feedback control) by applying Caley-Hamilton theorem. Given that, two representative cut-in scenarios are designed to comprehensively understand the impact of cut-in vehicle behavior as well as control parameters on the safety and stability of the ACC system. Enable by the interpretability nature of ACC dynamic analytical solution, necessary and sufficient conditions for overshoot and potential safety risks are derived. The proposed framework is further applied to analyze the cut-in disturbance evolution of commercial ACC systems using field-test data and calibrated control parameters, by which the probabilistic safety and stability condition is provided. Through the above efforts, the framework is instrumental in robust ACC design under cut-in scenarios.

Effects of speed difference on injury severity of freeway rear-end crashes: Insights from correlated joint random parameters bivariate probit models and temporal instability

Rear-end crashes particularly on freeways are the most frequent type of collisions causing many injuries, damage and congestion. This paper investigates the impact of varying speed differences between following and leading vehicles on injury severity in two-vehicle rear-end crashes. It develops three groups of correlated joint random parameters bivariate probit models with heterogeneity in means. The rear-end crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods before, during, and after the COVID-19 pandemic. The study considers two potential injury severity outcomes: no injury and injury/fatality, for both drivers involved in these crashes. The findings indicate that a range of variables, including driver, vehicle, roadway, environmental, crash, and temporal attributes, significantly influence the injury severity outcomes for drivers in both following and leading vehicles. Demonstrating superior goodness-of-fit, the proposed approach sheds light on interactive unobserved heterogeneity, captured through heterogeneity in means and significant correlations among random parameters. The study observes critical influences on the injury severity outcomes of both drivers, with significant factors such as gender, age, vehicle type, weather conditions, lighting, and time of day. Furthermore, the results substantiate the heightened risk outcomes associated with greater speed differences and the period of the COVID-19 pandemic. These findings yield further insights into the risk mechanisms of two-vehicle rear-end crashes and offer guidance for the development of effective safety countermeasures.

Are front crash prevention systems less effective at preventing rear-end crashes where trucks and motorcycles are struck?

Objective Automatic emergency braking (AEB) and forward collision warning (FCW) are effective at preventing rear-end crashes, but they may perform better in some rear-end crash scenarios than others. The goal of this study was to estimate the effects of front crash prevention systems equipped to passenger vehicles in crashes where another passenger vehicle, a medium/heavy truck, or a motorcycle is struck and compare effectiveness by struck vehicle type. Methods More than 160,000 two-vehicle rear-end crashes were identified where a passenger vehicle with or without FCW and AEB was the striking vehicle and another passenger vehicle, medium/heavy truck, or motorcycle was the struck vehicle. Poisson regression was used to estimate the effect of front crash prevention by struck vehicle type on rear-end crash rates per registered vehicle year, accounting for the state and year of the crash and the make, model year, class, and engine type of the striking vehicle. Results Front crash prevention was associated with a 53% reduction in rear-end crash rates when striking another passenger vehicle, which was significantly larger than the reductions of 38% when striking a medium/heavy truck and 41% when striking a motorcycle. Reductions in rear-end injury crash rates when striking a passenger vehicle also were larger than when striking a medium/heavy truck and when striking a motorcycle. Discussion If all passenger vehicles were equipped with FCW and AEB that were as effective in crashes striking a truck or motorcycle as they are in crashes with another passenger vehicle, over 5,500 additional crashes with medium/heavy trucks and 500 with motorcycles could potentially be prevented annually in the United States above what would be expected from current front crash prevention systems. Extending front crash prevention testing in consumer information programs to include motorcycle and truck targets could encourage auto manufacturers to improve performance in these crash scenarios.

Prioritizing rear-end crash explanatory factors for injury severity level using deep learning and global sensitivity analysis

Traffic accidents are usually unique events with unpredictable geographical and temporal dimensions; thus, accident injury severity level (INJ-SL) analysis presents formidable categorization and data stability problems. Classical statistical models are limited in their ability to correctly model INJ-SL, whilst sophisticated machine learning approaches do not appear to have any equations to prioritize/analyze multiple contributing factors to forecast accidents accompanying INJ-SLs. In addition, the intercorrelations between the input variables may render the conclusions of a formal sensitivity analysis incorrectly. Rear-end collisions are the most common form of traffic accidents; consequently, their linked INJ-SL requires more research. This paper provides a complex technique based on a deep learning paradigm paired with different indicators of Global Sensitivity Analysis to address all of these concerns. Unlike existing neural network designs, this technique presents a deep residual neural network structure that employs residual shortcuts (i.e., connections). The connections enable the DRNNs to sidestep a few levels of the deep network architecture, evading the regular training with high accuracy issues. Using the trained DRNNs model, a Latin Hypercube sampling simulation was undertaken to determine each explanatory component's influence on the resulting INJ-SL. The latest available data from 2011 to 2018 is used to assess all rear-end collisions in North Carolina. A comparison was made between the performance of two different schemes of data categorization using a set of global sensitivity metrics. It was determined that the devised technique overcame the data heterogeneity problems to achieve an accuracy of 87%. In addition, the proposed sensitivity analysis identified the most relevant factors associated with INJ-SL rear-end collisions.

Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation

Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation

Assessing driver behavior in work zones: A discretized duration approach to predict speeding

Higher speeds in work zones have been linked to an increased likelihood of crashes and more severe crash outcomes. To enhance safety, speed limits are often reduced in work zones, aiming to create a steady flow of traffic and safer traffic operations such as merging and flagging. However, this speed reduction can also lead to abrupt speed changes, resulting from sudden braking or acceleration, increasing the risk of crashes. This disruption in speed and flow results increases the likelihood of rear-end crashes. Ensuring driver compliance with the reduced speed limits and traffic flow operations is challenging as work zones may cause frustration and lead to more instances of speeding. Therefore, proactively predicting speeding events in work zones can be crucial for the safety of both workers and road users, as it enables the implementation of speed enforcement measures to maintain and improve driver compliance in advance. In this study, we employ the duration-based prediction framework to forecast speeding occurrences in work zones. The model is used to identify significant predictors of speeding including visibility, number of lanes, posted speed limit, segment length, coefficient of variation in speed, and travel time index. Among these variables, the number of lanes, posted speed limit, and coefficient of variation of speed are positively associated with speeding. On the other hand, visibility, segment length, and travel time index are negatively associated with speeding. Results show the model’s predictive accuracy is higher for speeding events with shorter durations between consecutive occurrences. The model predicted speeding within 61% of the actual epoch when speeding events within 5 h of one another were considered for validation. This indicates that the model is more effective for road segments and work zones where speeding occurs more frequently. The prediction framework can be a great asset for agencies to improve work zone safety in real-time by enabling them to proactively implement effective work zone enforcement measures to control speeding and to stay prepared, preventing potential hazards.

Exploring differences in injury severity between occupant groups involved in fatal rear-end crashes: a correlated random parameter logit model with mean heterogeneity

ABSTRACT Rear-end crashes are one of the most common crash types. Passenger cars involved in rear-end crashes frequently produce severe outcomes. However, no study investigated the differences in the injury severity of occupant groups when cars are involved as following and leading vehicles in rear-end crashes. Therefore, the focus of this investigation is to compare the key factors affecting the injury severity between the front- and rear-car occupant groups in rear-end crashes. First, data is extracted from the Fatality Analysis Reporting System (FARS) for two types of rear-end crashes, including passenger cars as rear-end and rear-ended vehicles. Significant injury severity difference between front- and rear-car occupant groups is found by conducting likelihood ratio test. Moreover, the front- and rear-car occupant groups are modeled by the correlated random parameter logit model with heterogeneity in means (CRPLHM) and the random parameter logit model with heterogeneity in means (RPLHM), respectively. This study provides an insightful knowledge of mechanism of occupant injury severity in rear-end crashes.

Development of a Restraint System for Rear-Facing Car Seats

In self-driving vehicles, passengers can set their seats in an unconventional seating position, such as rear-facing. Sitting in such an orientation can increase the risk of whiplash in the head-and-neck system in a frontal impact, as frontal crashes usually have higher severities compared with rear-end crashes. This paper shows that a forward-facing front seat optimised for rear-impact protection needs to be redesigned to be used as a rear-facing seat. In the second and main part of this paper, a restraint system for rear-facing car seats is developed, and frontal impact simulations with 64 km/h of delta-V are used to evaluate its performance. The designed seating system comprises two rigid torso plates, a fixed recliner and an energy absorber under the seat pan. Without using the developed restraint system, the 50th percentile male human model is exposed to neck shear forces exceeding 600 N. With the developed restraint system, neck shear forces are less than 350 N in frontal impacts with 64 km/h of delta-V. Apart from whiplash, the risk of head, chest, lower extremity and lower back injuries are also evaluated. The results confirm that the developed restraint system successfully protects the occupant since all assessment criteria values are lower than the injury assessment reference values.

Investigating the impact of influential factors on crash types for autonomous vehicles at intersections

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings.

Characteristics of Rear-End Collisions: A Comparison between Automated Driving System-Involved Crashes and Advanced Driving Assistance System-Involved Crashes

With the increasing number of vehicles equipped with automated driving systems (ADS) being tested on public roads and the expanding market share of vehicles equipped with advanced driving assistance systems (ADAS), the number of ADS- or ADAS-involved crashes increases. Thus, it is necessary to investigate the distribution of ADS- and ADAS-involved crashes and the factors leading to them. The rear-end collision has been found to dominate ADS-involved crashes. However, no research has explored the conditions when ADS-involved rear-end collisions are more likely to happen and no research has investigated ADAS-involved rear-end crashes. Based on 130 ADS-involved crashes and 84 ADAS-involved crashes extracted from a dataset collected by the National Highway Traffic Safety Administration (NHTSA) between July 2021 and May 2022, this study explored the crash patterns, especially rear-end crashes, of ADS- and ADAS-controlled vehicles. Results show that rear-end collisions dominate both ADS- and ADAS-involved crashes, especially ADAS-involved crashes. The type of both ADS-involved and ADAS-involved crashes was affected by the speed of the ego-vehicle relative to the posted speed limit. Further, the type of ADS-involved crash was affected by the pre-crash movement of the crash partner, while the type of ADAS-involved crash was further associated with the road type. The findings can provide insights into the design of ADAS and ADS control algorithms, the external human-machine interface design of the vehicles with ADS or ADAS, and the training program of human road users to improve traffic safety in mixed traffic.

Spatiotemporal Evaluation of Injury Severity of Expressway Rear-End Crashes in China: Insights Using Random Parameters Approaches

Rear-end crashes have become an urgent problem in China, resulting in severe casualties and massive property damage. This study aims, first, to discover the determinants affecting injury severity in expressway rear-end crashes and to examine the corresponding changes over time and space, and, second, to identify the sources of unobserved heterogeneity shifts, which could have substantial implications for efficient and effective crash prevention. Using three-year crash data (2017 -2019) from two expressways with speed limits of 120 km/h and 100 km/h (G2 and G25) in Jiangsu and Guangdong provinces in China, four random parameter logit model (RP-LM) approaches were used in this study to analyze the contributing factors: fixed parameter LM, RP-LM, RP-LM with heterogeneity in means, and RP-LM with heterogeneity in means and variances. With three injury severity consequences (severe injury, minor injury, and no injury), the characteristics of the driver, vehicle, roadway, environment, and others were proposed as possible determinants. The temporal and spatial stability were then explored using transferability tests. The marginal effects were computed to explore further potential heterogeneity and spatiotemporal variations. The estimated results indicate the superiority of the proposed model over its base counterparts, with very good [Formula: see text] values all over 0.64. Saturday, early morning, and winter indicators were identified as significant random parameters, and several variables were observed to manifest heterogeneity in means and variances. Speeding behavior, early morning, and heavy truck indicators increased the likelihood of minor and severe injury. These findings could significantly improve expressway safety related to rear-end crashes.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV’s automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV’s operational design domain and the development and optimization of the AV’s ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

Analysis of the Effects of Highway Geometric Design Features on the Frequency of Truck-Involved Rear-End Crashes Using the Random Effect Zero-Inflated Negative Binomial Regression Model

Statistical data indicate that trucks are more prone to rear-end crashes, making this an area of concern. The objective of this study is to create a model that analyzes the factors influencing the frequency of rear-end crashes involving trucks (TIRC). To achieve this, researchers identified the most appropriate model as Spatial Zero-Inflated Negative Binomial Regression (SZINB). This model takes into account spatial correlation, which plays a significant role in the occurrences of TIRC on different road segments supervised by each highway ward. The estimation of parameters in the SZINB model has led to key findings that shed light on the factors contributing to a higher likelihood of TIRC. These findings include the increased probability of TIRC on curved roads compared to straight ones, roads that feature open middle islands, six lanes per direction, a slope, right-of-way shoulder width, pavement type, lane width, and a post speed limit. Based on these key findings, this study developed policy recommendations and sample measures aimed at reducing the frequency of TIRC. Implementing measures such as improving the road design on curved sections, optimizing middle islands, and enhancing traffic management on wider roads can help mitigate the risk of crashes involving trucks.

Looking back in the rearview: Insights into Queensland’s rear-end crashes

Objective Rear-end crashes cost the most out of any crash type to the Compulsory Third Party insurance scheme of Queensland. Rear-end crashes are also one of the most common types of crashes, peaking as the most common type of crash in Queensland in 2019. This study investigated the environmental, driver, and vehicle characteristics associated with rear-end collisions in Queensland, Australia. Methods A totle of 367,230 crashes in the Queensland crash database between 2001 and 2021 were used to identify crash trends in Queensland, particularly related to rear-end crashes. For crashes between 2015 and 2021, additional information was gathered and allowed further analysis of factors contributing to rear-end crashes using the quasi-induced exposure method. Two binary classification tree models were used. Model 1 investigated the environmental characteristics that predicted a crash being a rear-end or not. Model 2 investigated the controller and vehicle characteristics that predict a rear-end crash-involved vehicle to be either the striking (at-fault) or struck (not-at-fault) vehicle. Results Model 1 indicated that rear-end crashes were overrepresented in urban areas and major cities and where speed limits exceeded 50 km/h. Model 2 indicated that occupants of struck vehicles (not at-fault) were more likely to be severely injured than the striking (at-fault) vehicles in rear-end crashes. License type and vehicle type also influenced the frequency and severity of rear-end crashes. Conclusion Rear-end crashes can be unjust in that the not-at-fault party is typically more severely injured. Results from the current study suggest that keeping speed limits no higher than 50 km/h could help reduce the severity of rear-end crashes. Increased enforcement of safe following distances could also assist in preventing road trauma as a result of rear-end crashes.

Analyzing driver injury severity in two-vehicle rear-end crashes considering leading-following configurations based on passenger car and light truck involvement

Rear-end crash is a major type of traffic crashes leading to a large number of injuries and fatalities each year, and passenger cars and light trucks are two main vehicle types in rear-end crashes on US roadways. Passenger cars and light trucks are different in size, vehicle mass and driver’s vision. It is necessary to investigate the driver injury outcome patterns in rear-end crashes between passenger cars and light trucks considering crash configurations regarding the leading and following vehicle types. This study employs latent class multinomial logit (MNL) model to examine the risk factors on driver injury severity along with heterogeneity in variable effects presented by the cluster pattern in two-vehicle rear-end crashes involving passenger cars and light trucks, considering four crash configuration types, i.e., a passenger car struck by a passenger car, a light truck struck by a light truck, a passenger car struck by a light truck, and a light truck struck by a passenger car as exploratory variables. A model with two latent classes, which indicates the heterogeneity in variable effects among all the observations, is found to best fit the 7-year crash dataset from Washington State. The pseudo-elasticities are calculated to quantify the marginal effects of the contributing factors. The risk factors curve and sloping road condition, driver without seatbelt, and driver age of 65 and above increase driver fatality and serious injury risk greatly, and these three factors contribute from different latent classes. The crash configuration of a passenger car struck by a light truck is found to be one of class characteristics factors, which indicates that the heterogeneity exists between these two vehicle types. This factor is also a risk factor of injury. Furthermore, the leading vehicle is found to be much more vulnerable and closely related to injury, especially when it is in the crash of a passenger car struck by a light truck. The latent classes discovered give theoretical evidence of how to appropriately select subset data for further model construction for practical interest of serious injury prevention. The risk factors and their influence on injury severity provide beneficial insights on developing relevant countermeasures and strategies for injury severity mitigation on rear-end crashes involving passenger cars and light trucks.

Mitigation of signalized intersection collision risks with trajectory based dynamic dilemma zone protection

Dilemma zone is one of the major factors causing red-light violations, right-angled and rear-end crashes at signalized intersections. In this paper, a dilemma zone protection system is introduced, which employs a dynamic vehicular trajectory optimization approach to guide vehicles approaching a signalized intersection. Unlike conventional methods that aim to eliminate dilemma zones, this system adjusts the speed profiles of individual vehicles to shift the distribution of dilemma zones and prevent vehicles from becoming trapped. Extensive simulated experiments were conducted to test and validate the proposed system for both individual vehicles and platoons. Results demonstrate that the system offers superior protection for individual vehicles, with full coverage across various settings of initial speeds and distances to the stop line. In the traffic environment with realistic platooning settings, the proposed system significantly reduces the number of vehicles in the dilemma zone, resulting in improved operational and safety benefits such as reduced risks of hazardous maneuvers and savings in vehicular delay.

Effects of Advanced Driver Assistance Systems on Impact Velocity and Injury Severity: An Exploration of Data from the Crash Investigation Sampling System

This paper presents an exploratory analysis of advanced driver assistance systems (ADAS) features and safety outcomes using National Highway Transportation Safety Administration’s Crash Investigation Sampling System (CISS) database. In this database, we found improved safety outcomes associated with the presence of three ADAS features: lane departure warning, forward collision warning, and blind spot detection. In particular, vehicles with ADAS features were less likely to be involved in fatal and severe crashes than vehicles of a similar age and body style. Additionally, vehicles with ADAS features had a lower rate of change in velocity pre- and postcrash, a key proxy for injury severity. Vehicles with ADAS were also less likely to be involved in head-on and rear-end crashes than similar vehicles without ADAS and were less likely to experience a severe outcome than vehicles without ADAS when involved in sideswipe collisions. Despite these promising findings, however, small sample sizes precluded robust within-category and multivariate analysis. More robust data are urgently needed to disentangle the safety effects of ADAS features from those related to confounding variables such as vehicle age and body type for vehicle occupants, pedestrians, and bicyclists. We conclude the paper with recommendations for improvements to the CISS database that could support future investigations. We hope this work provides a baseline for scholars and practitioners to deepen our understanding of the effects of ADAS features on transportation safety.

Multivariate Approach to Peak-Period Models for Crash Types and Severities in Variable Speed Systems on Freeways

According to current research, safety performance functions (SPFs) based on traditional annual average daily traffic are less suitable for addressing crash risk in traffic-management strategies that typically operate for short time intervals (e.g., peak period). Variable speed limit (VSL)/variable advisory speed (VAS) is a traffic-management strategy which is deployed to achieve speed harmonization typically during the peak hours of weekdays. However, peak-period SPFs for VSL/VAS-implemented freeways considering different crash types and severity levels are still unexplored. Therefore, in this study the multivariate Poisson lognormal (MVPLN) method was applied to estimate the different crash-type and severity-level models. The results showed that by addressing correlations among different crash types and severity levels, MVPLN models outperformed individual univariate Poisson lognormal crash-type and -severity models. From the developed models, several traffic- and roadway-related attributes—such as volume, average speed, standard deviation of speed, segment type (merge, diverge, and weaving), higher number of lanes, and so forth—could be identified as the potential crash-risk factors. Also, the proposed models were successful in capturing the spatial effects of segments (differences between segments downstream and upstream of the location of interest) on crash frequency. Further, the implementation of VSL/VAS strategy was found to be effective in reducing rear-end crashes in the crash-type model as well as property-damage-only (PDO) and C (non-incapacitating) crash severity levels in the crash-severity model. Policymakers and practitioners could benefit from this study in incorporating safety aspects into developing VSL/VAS algorithms.