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This study presented an extensive assessment of risky driving behavior through Structural Equation Modeling (SEM) technique and explored the applicability of this method in identifying contributing factors influencing drivers' risk-taking behavior in clear and adverse weather. Drivers' questionnaire responses as well as vehicle trajectories of their completed trips in clear and adverse weather were utilized from the SHRP2 Naturalistic Driving Study (NDS). Factor analyses were conducted to identify the number of unobserved "latent" variables. Subsequently, two SEM models in clear and adverse weather were developed to attain the relationships between the observed and the latent variables. "Human Factors" and "Driving Skills" were determined as exogenous latent variables in both models to investigate their impacts on an endogenous latent variable (i.e., risky driving). The results suggested that "Human Factors" was the most significant latent variable affecting drivers' risk-taking behavior in clear and adverse weather conditions. Moreover, speeding was found to have a significant impact on risky behavior in adverse weather conditions. The findings could help safety practitioners with better understanding of the influencing factors affecting risky driving to improve safety through proper enforcement and necessary training programs, particularly targeting young and inexperienced drivers. [ABSTRACT FROM AUTHOR]

Traffic crashes impose tremendous socio-economic losses on societies. To alleviate these concerns, countless traffic safety researches have shed light on the cognition of observable crash/crash severity contributing factors. Nonetheless, some influential factors might not be observable or measurable, referred to as unobserved heterogeneity, that could be accounted for by structuring random intercepts and slopes in hierarchical models. With this respect, although it is known random slopes can capture more unobserved heterogeneity, most previous studies utilized random intercepts to simplify result interpretations, indicating an inconsistency in the literature considering the hierarchical modeling specification. This study delves into the mentioned confusion within an empirical real-time clustering critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes throughout 402-miles of Interstate-80 in Wyoming. The crash dataset was conflated with real-time traffic-related and environmental contributing factors. Regarding the inclusion of random intercepts and slopes, eleven Logistic regressions were conducted. As a data-dependent matter, results depicted random slopes, compared to random intercepts, do not necessarily enhance models' out-of-sample predictive performance because they impose much more complexity on the models' structure. Besides, considering the type of unobserved heterogeneity, if random slopes are required, random intercepts should be accompanied to allow data showing their true patterns. [ABSTRACT FROM AUTHOR]

Lane departure crashes account for more than 50% of traffic fatalities in the US. Among the different roadway treatments, rumble strips was found to be cost-effective. Centerline rumbles strips (CLRS), provided on the centerline for two-lane two-way highways, alert distracted and drowsy drivers from drifting into the opposite driving lane. It is used as a countermeasure for head-on or cross-over crashes. Despite public concerns of the generated noise and perceived decrease in visibility of pavement markings, there is no doubt about the safety benefits of CLRS. During severe snowstorms, compacted snow located on the roadway could negatively influence the performance of CLRS. Higher and more frequent winter maintenance level operation might accelerate the functional restoration of CLRS. The gap in literature was approached by quantifying the effect of CLRS in reducing crashes accounting for different levels of winter maintenance operations. Wyoming highways with distinct winter maintenance levels were assessed in this study, in which safety performance functions (SPFs) were developed. Unobserved heterogeneity in between-route variance was accounted for by utilizing random-intercept negative binomial (NB) model in a Bayesian framework. Crash modification factors (CMFs) were developed using the before-after empirical Bayes (EB) method. Generally, the safety benefits of installing CLRS on the selected routes was between 25% and 68% expected crash reductions at 95% significance level. Higher level of winter maintenance was also found to be associated with higher effectiveness of CLRS. Therefore, the agencies should re-evaluate their winter maintenance plans in order to account for locations with high-risk for head-on collisions. DOI: 10.1061/JTEPBS.0000714. © 2022 American Society of Civil Engineers. [ABSTRACT FROM AUTHOR]

• Developing a realistic microsimulation model requires adjusting driving behavior parameters. • This study updated lane change models in various weather using trajectory-level SHRP2 NDS dataset. • Representative key lane change parameters were extracted from an automated algorithm. • The demonstrated methodology could be used to develop weather-specific microsimulation models. Introduction: Adverse weather has a considerable negative impact on safety and mobility of transportation networks. Microsimulation models are one of the potential tools that could be used to evaluate the safety and operational impacts of adverse weather. The development of a realistic microsimulation model requires the adjustment of driving behavior parameters with disaggregate trajectory-level data. This study presented a novel approach to update and adjust lane change model parameters for the development of realistic microsimulation models in different weather conditions by leveraging the trajectory-level data from SHRP2 Naturalistic Driving Study (NDS). Method: Representative key lane change parameters in various weather conditions were extracted from an automatic identification algorithm. These lane change parameters were used to develop microsimulation models in VISSIM in an attempt to assess the safety and operational impacts of adverse weather on a freeway weaving segment. Results: The evaluation of safety impacts of adverse weather with regard to three Surrogate Measures of Safety (SMoS) namely Time-to-Collision (TTC), Post Encroachment Time (PET), and Deceleration Rate to Avoid Collision (DRAC) suggested that extreme adverse weather (including heavy rain, heavy snow, and heavy fog) produced a higher total number of simulated conflicts compared to clear weather. The operational analysis results revealed that adjusted parameters in most of the adverse weather produced lower average speeds with higher total travel times and total delays than clear weather. Conclusions: The outcomes of safety and operational assessments for the adjusted parameters showed that the development of microsimulation models should be based on weather-specific, rather than default parameters. Practical Applications: The methodology presented in this study could be adopted by transportation agencies to develop weather-specific microsimulation models. Moreover, the demonstrated approach could be used to evaluate different Connected Vehicle (CV) applications related to lane change in terms of safety and operations in microsimulation platforms. [ABSTRACT FROM AUTHOR]

This article investigates the safety effectiveness of weather-based variable speed limit (VSL) systems on a rural mountainous interstate in Wyoming using a before-after study with Empirical Bayes (EB). VSL is considered one of the important countermeasures that help in reducing crashes by regulating and minimizing the variability for the motorists' operating speed. Although the multiple benefits result from the VSL, studies evaluating its safety effectiveness using Highway Safety Manual (HSM) methodologies are scarce in literature. Interstate 80 (I-80) in Wyoming, which has a total length of 402 miles, is known by its challenging roadway geometry, adverse weather, and high truck traffic. It comprises four weather-based VSL corridors totaling 147 miles. Full SPFs were developed to account for the variability in weather, traffic, roadway geometry, and implemented countermeasures. Negative binomial (NB) and multivariate adaptive regression splines (MARS) were used to develop the SPFs. Six years in the before period and 5 years in the after period were utilized to develop the crash modification factors (CMFs). Results showed that the MARS model outperformed the NB model. Moreover, the VSL showed a significant reduction in all the investigated crash types. The safety effectiveness of VSL for the examined severity levels ranged between 15% to 29% and 12% to 34% using NB and MARS, respectively. [ABSTRACT FROM AUTHOR]

• Multilevel modeling was utilized to capture regional heterogeneity. • Effect of driver's locality-related factors on driver speeding behavior was evaluated. • The geographical location accounted for about 7.7% of the variability in speeding. Disparities in traffic safety among different regions and driver populations have been investigated in previous studies. These studies found significant differences in crashes between population groups and geographical locations with various socio-economic characteristics. However, driver-behavioral factors, specifically speeding behavior, which is a critical aspect of traffic safety, have received less attention in case of analyzing the impact of local characteristics on driver-behavioral choices that might increase the risk of crashes. The impact of local characteristics on various driver behaviors is getting even more important as the data from the connected and automated vehicles as well as similar second-by-second trajectory level data from naturalistic driving studies are becoming more available. In fact, neglecting mentioned impact might lead to erroneous inferences due to the disparities in socioeconomic characteristics in different regions. Therefore, this paper, for the first time, utilized multilevel logistic regression modeling approach to evaluate the effect of driver's locality-related factors on driver speeding behavior using naturalistic driving data collected from the SHRP2 project in six US states. The results showed that the geographical location of drivers accounted for about 7.7% of the variability in the likelihood of a driver driving over the posted speed, regardless of other explanatory variables. The methodology and the results from this study can pave the road for future human factor studies utilizing trajectory-level data from different geographical locations to reduce the heterogeneity and increase the transferability of the results without introducing a bias in inferences. [ABSTRACT FROM AUTHOR]

Highlights • This study serves as a baseline analysis to quantify the impact of connected vehicle technologies. • Safety performance functions (SPFs) for 402-mile rural mountainous interstate were developed. • Multivariate adaptive regression splines (MARS) and negative binomial (NB) were compared. • Transferability analysis was performed to examine the need to calibrate the jurisdiction-specific SPFs to be used for the whole corridor. • SPFs were not transferable within the same corridor. SPFs must be calibrated individually for VSL vs non-VSL corridors. Abstract Interstate 80 in Wyoming is one of the busiest freight corridors that is characterized with harsh winter conditions and challenging mountainous roadway sections. The fatality rates in Wyoming are always typically higher than the national level. The 402-mile I-80 corridor in Wyoming was selected by the USDOT FHWA for piloting connected vehicle technology to improve the safety and mobility of heavy trucks. To accurately quantify the effectiveness of the pilot, evaluation of the pre-deployment safety performance is essential. Unlike other studies, the full 402-mile of I-80 corridor passing through Wyoming was investigated as a requirement of the USDOT. Homogeneous segmentation was used to divide the corridor based on horizontal and vertical roadway characteristics. A transferability analysis was conducted to investigate whether a short portion of the corridor would be representative of the whole 402-miles of I-80. Results showed that the whole 402 miles should be considered in the analysis due to the radical changes throughout the corridor. Several SPFs were developed using three models; negative binomial (NB) model, spatial autoregressive (SAR) model, and non-parametric multivariate adaptive regression splines (MARS). Comparisons were performed for the developed models. Crash prediction models for total crashes and Fatal and Injury (F + I) crashes in addition to truck crashes were calibrated utilizing five years of crash data from 2012 to 2016. The results obtained from the three statistical approaches showed that MARS model provided a better model fit compared to NB and SAR models, given the lower AIC values for the developed models. Yet, SAR models showed the significant spatial dependency between the neighbor roadway segments. Additionally, the NB model showed its superiority on SAR when the spatial correlation was not significant. Parametric and non-parametric techniques should be interchangeably used in developing SPFs according to the modeling needs. [ABSTRACT FROM AUTHOR]

Transportation agencies across the nation are continuously evaluating the safety and operation of their rural two-lane highways. The three most common countermeasures are to do nothing, add passing lanes to target sections, or to upgrade to four-lane. The goal of this article is to update the current body of knowledge available to agencies on the process of implementing countermeasures and realistically evaluating their safety effectiveness. The Wyoming Department of Transportation (WYDOT) constructed nine passing-lane segments on a 26-mile stretch of rural two-lane highway. Preliminary analysis using 7-year crash data showed that the safety benefit was negligible at best. However, utilizing Empirical Bayes and Wyoming-specific safety performance functions, it was shown that the basic approach of comparing crash rates underestimated fatal and injury (F+I), and lane-departure crashes and that the passing lanes had a significant safety benefit. Furthermore, the authors looked at what would have happened from a safety and cost perspective had no countermeasure been implemented and then also if the road had been upgraded to four-lane undivided instead of just adding passing lanes. It was shown that passing lanes are the best option from a safety and cost perspective especially when agencies have constrained budgets. [ABSTRACT FROM PUBLISHER]

• Use of PRISMA approach for comprehensive review of naturalistic driving studies. • NDSs have provided numerous insightful findings in area of traffic safety and operation. • Most studied topic using NDS data is driver behavior. • NDSs could be effectively leveraged to perfect the behavior cloning. The state of practice of investigating traffic safety and operation is primarily based on traditional data sources, such as spot sensors, loop detectors, and historical crash data. Recently, researchers have utilized transportation data from instrumented vehicles, driving simulators, and microsimulation modeling. However, these data sources might not represent the actual driving environment at a trajectory level and might introduce bias due to their experimental control. The shortcomings of these data sources can be overcome via Naturalistic Driving Studies (NDSs) considering the fact that NDS provides detailed real-time driving data that would help investigate the safety and operational impacts of human behavior along with other factors related to weather, traffic, and roadway geometry in a naturalistic setting. With the enormous potential of the NDS data, this study leveraged the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) approach to shortlist the most relevant naturalistic studies out of 2304 initial studies around the world with a focus on traffic safety and operation over the past fifteen years (2005–2020). A total of 117 studies were systematically reviewed, which were grouped into seven relevant topics, including driver behavior and performance, crash/near-crash causation, driver distraction, pedestrian/bicycle safety, intersection/traffic signal related studies, detection and prediction using NDSs data, based on their frequency of appearance in the keywords of these studies. The proper deployment of Connected and Autonomous Vehicles (CAV) require an appropriate level of human behavior integration, especially at the intimal stages where both CAV and human-driven vehicles will interact and share the same roadways in a mixed traffic environment. In order to integrate the heterogeneous nature of human behavior through behavior cloning approach, real-time trajectory-level NDS data is essential. The insights from this study revealed that NDSs could be effectively leveraged to perfect the behavior cloning to facilitate rapid and safe implementation of CAV. [ABSTRACT FROM AUTHOR]

• Drivers' behavior varies under different traffic and environmental conditions. • Extreme drivers' volatility is highly correlated with traffic critical safety events. • Characterizing Kinematic-based Surrogate Measure of Safety (K-SMoS) on Basic Safety Messages could measure drivers' behavior in Connected Vehicle (CV) environments. • Extreme Value Theorem incorporates proper statistical techniques for exploring unusual extreme behaviors. • Extreme value analysis of K-SMoS could spatiotemporally detect traffic crashes in CV environments. Connected Vehicles (CVs) technology has provided large-scale driving database embedded in Basic Safety Messages (BSMs). This valuable data source can shed more light on tracking individual driving behaviors to detect crashes. This study delves into the possibility of detecting crashes using BSMs in controlled high-fidelity driving simulator experiments. To this end, two driving simulator scenarios were designed to simulate Run-off-Road (ROR), and Rear-End (RE) crashes. Twenty-four professional truck drivers were recruited to drive the scenarios. In each scenario, crash and non-crash cases were identified from vehicles' trajectories, resulting in four study cases. Drivers' behaviors were quantified by characterizing two Kinematic-based Surrogate Measures of Safety (K-SMoS), namely Absolute value of Derivative of Instantaneous Acceleration (ADInstAccel) and Absolute value of Derivative of Steering (ADSteering). Extreme defensive driving volatilities under crash and non-crash cases were modeled by extreme value analysis of K-SMoS and fitting their associated Generalized Extreme Value (GEV) distributions under Bayesian inference. Accordingly, for each K-SMoS, the crash detection was formulated as a binary classification between two K-SMoS GEV continuous distributions under crash and corresponding non-crash conditions. Qualitative uncertainty analysis of joint posterior density distributions of GEVs' parameters revealed a higher uncertainty of extreme driving behaviors in crash conditions. Regardless, notable relative increases in the central tendency of extreme K-SMoS in crash compared to non-crash conditions were found, implying the possibility of crash detection by tracking extreme drivers' behaviors using trajectory-level observations. This visual inference was affirmed by the result of binary classification of GEV distributions associated with K-SMoS. Depending on the crash type and K-SMoS, 71% to 81% accuracy in crash detection was obtained, where ADSteering outperformed ADInstAccel in terms of the discriminative ability. Besides, using sensitivity–specificity analysis, the optimal threshold of 1.24 (rad/s) and 1.31 (m/s3), respectively, for ADSteering and ADInstAccel, were identified to detect crashes. These findings can potentially enhance CVs' automation level in spatiotemporally identifying crash-prone conditions to disseminate distress notifications. Furthermore, the introduced methodology can be a complementary one to what has been followed in the crash detection domain. [ABSTRACT FROM AUTHOR]

• This study is established to develop a consistent and effective snowy weather detection system that can provide drivers with real-time weather information while driving in regions prone to adverse weather conditions. • This study introduces new data fusion assemblies reduced from the SHRP2 NDS datasets by using the time series data and video records. • The study recommends using the one-minute time segmentation in traffic applications related to weather detection thanks to its high accuracy compared to the one-mile distance segmentations. • Results show that using texture images parameters, collected using a systematic dash-camera and using a feature extraction methodology, can help in detecting snowy weather with an accuracy between 76% to 82%. • The new data fusion between external sensors data and texture image parameters were useful to overcome data privacy and non-accessibility of the CANbus data. Introduction: This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips. Method: In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data. Results: Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data. Practical Applications: Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways. [ABSTRACT FROM AUTHOR]

• Hierarchical modeling can account for unobserved heterogeneity. • Considering one random factor at level-2 of data structure hinders exploring the crossed effects of other random factors on the response variable. • Utilizing the concept of Cross-Classified Random Effects Modeling (CCREM) can account for the crossed effects of different random factors. • The maximum probability of critical crashes arises on dry pavements and steep downgrades. • Real-time interventions should be applied based on both traffic-related predictors and various combinations of environmental conditions. Traditional hierarchical modeling has been proposed to account for unobserved heterogeneity in the crash analysis. Previous studies investigated the grouping of individual observations between different clusters by considering a single random factor at level-2 of data structure. This approach, however, hinders exploring the possible crossed effects of additional random factors at the level-2 of data hierarchy on the response variable. The current study aims to expand the previous attempts by introducing the concept of Cross-Classified Random Effects Modeling (CCREM) and utilizing crossed random intercepts to account for the crossed effects of two random factors. Aligned with the Connected Vehicle Pilot Deployment Program on Interstate-80 (I-80), this paper intends to cluster critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile I-80 in Wyoming during the first five months of 2017. Aggregated environmental conditions were conflated with disaggregated real-time traffic observations. Concerning road surface conditions and longitudinal grade categories, four Logistic Regression models were calibrated under Bayesian Inference. Model-1 considered these two factors as fixed parameters; however, in each of Model-2 and Model-3, one of these factors was treated as a random intercept. Model-4 considered both factors as random intercepts and investigated their crossed effect on the critical crash probability. Model-4 outperformed the others and showed that the maximum probability of critical crashes arises on dry pavements and steep downgrades. In contrast, the combined effect of wet pavements and less steep downgrades is associated with the minimum risk of critical crashes. It was revealed that the probability of critical crashes varies at any given value of real-time traffic-related predictors according to different combinations of longitudinal grade and road surface conditions. This finding indicates an essential need for Active Traffic Management to timely apply interventions not only based on real-time traffic-related predictors but also according to various combinations of environmental conditions. [ABSTRACT FROM AUTHOR]

• Slippery horizontal curves are contributed to the high risk of curve-related crashes. • Drivers' behavior is an influential factor contributing to 93% of crashes. • Connected Vehicles (CV) notifications enhance driver's situational awareness. • Drivers adapt their driving behavior based on CV notifications on horizontal curves. • A less likelihood of Run-off-Road crashes can be obtained in the CV environment. Although drivers can adequately adjust their operating speed according to the road curvature, they show a lack of recognition regarding the pavement friction conditions. In this regard, inappropriate speed selection on Horizontal Curves (HCs) with reduced surface friction can lead to a remarkable rate of run-off-road, sideswipe, head-on, and rollover crashes, especially on rural highways. Aligned with the Connected Vehicle (CV) Pilot Program on Interstate-80 in Wyoming, this study scrutinizes how CV advisory/warning messages can enhance traffic safety on slippery HCs. To this aim, a roadway consists of two HCs with regular and slippery pavement conditions was designed in a high-fidelity driving simulator experiment. A total of 24 professional truck drivers were recruited to drive the simulated roadway under CV and non-CV environments. In the CV scenario, drivers were informed about the pavement conditions and the advisory speeds before entering HCs. In contrast, no messages were given to non-CV drivers. Truck drivers' behaviors in both scenarios were quantified using four Kinematic-based Surrogate Measures of Safety (K-SMoS), including deviation from the pathway, instantaneous acceleration, lateral speed, and steering angle. CVs' trajectories were statistically compared to non-CVs in terms of the central tendency and dispersion using the Wilcoxon Signed-Rank Test (WSRT) and Median Absolute Deviation (MAD), respectively. The results of WSRT depicted, under the effect of CV advisory/warning messages and throughout the slippery HC, the central tendency of four K-SMoS could be shifted toward zero by 23% up to 99%. This shifting is associated with a significant safety enhancement that potentially can reduce the likelihood of curve-related crashes on slippery HCs. It was revealed that the variation in drivers' behavior on the slippery HC could be minimized in the CV environment, where 54% up to 95% reduction in the dispersions of four K-SMoS were observed, leading to more certainty in drivers' behavior. [ABSTRACT FROM AUTHOR]