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Safety Impact of Connected Vehicles on Driver Behavior in Rural Work Zones under Foggy Weather Conditions
Abstract
Work zone safety is one of the paramount goals of the safety community. The safety concerns in work zones might be exacerbated under foggy conditions, as an exogenous factor, contributing to high driver behavior variability. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) application on driver behavior during foggy weather conditions. A work zone was simulated using VISSIM under four sequential areas, including the advance warning, transition, activity, and termination area. The effect of increased drivers' situational awareness under the impact of WZW was calibrated in VISSIM based on the results of a high-fidelity Driving Simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Modified Deceleration Rate to Avoid Crash (MDRAC), Time Exposed Time-to-collision (TET), and Time-Integrated Time-to-collision (TIT), were employed to quantify the safety performance of CVs under varying CVs Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that the increase in CV-MPR enhances the safety performance of the work zone area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR up to 60% on I-80 would reduce mean speed and standard deviation of speed at each of the work zone areas, leading to more speed harmonization and minimizing crash risk in work zones.
... Many researchers conducted studies and tests of connected vehicle technology based on simulations (Adomah et al., 2021;Kang et al., 2018;Njobelo et al., 2018;Yang and Oguchi, 2020), but this method ignores the human factors. Meanwhile, some studies indicate that more than 90% of traffic crashes, to some degree, are caused by human factors (Rad et al., 2016). ...
To ensure safety, it is necessary to test the connected vehicle (CV) technology before application. The goal of this study is to provide a case reference for the testing of the connected vehicle technology. The connected vehicle technology test platform is built based on the driving simulator. Taking fog zone, tunnel zone, and work zone as analysis cases, drivers were invited to participate in driving simulation experiments, related data was collected, and the impact of connected vehicle technology on driving behavior and safety was analyzed. The results of the fog zone imply that drivers have a high degree of compliance with the connected vehicle technology. However, it also increases the visual workload of drivers to a certain extent. The results of the tunnel zone indicate that the connected vehicle technology can enhance driving safety by enabling drivers to remain cautious. The results of the work zone demonstrate that the connected vehicle technology is able to promote drivers’ ability of controlling speed and lane-changing. Overall, the results show that the connected vehicle technology has a positive effect on enhancing driving behavior and safety. The research framework and the development of the connected vehicle technology test platform based on the driving simulator given in the paper are dynamic and reproducible, which provides a reference for researchers in related fields, and the case analysis in this paper enriches the research of connected vehicle technology.
... The traffic volume in I-80, during the last three decades, has increased by 65 %, whereas the heavy truck traffic volume has increased by 150 % (Wyoming Department of Transportation, 2018). This difference in the growth rate requires more profound safety analyses since, in 2014, I-80 reached 0.52 in the large truck crashes per million vehicles miles traveled, which was the first rank in the United States (Adomah et al., 2021;Gaweesh et al., , 2021Khoda Bakhshi and Ahmed, 2020a, 2020b . To mitigate these safety concerns, the United States Department of Transportation Federal Highway Administration (USDOT FHWA) selected a 402-mile of I-80 in Wyoming to pilot Connected Vehicles (CVs) technology (Gopalakrishna et al., 2015). ...
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.
Introduction:
Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data.
Method:
A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables.
Results:
72.2% of EMS run reports matched to a crash record and 69.3% of trauma registry records matched with a crash record. Females, individuals between 11 and 20 years old, and individuals involved in single vehicle or head on crashes were more likely to be present in linked data sets. Using the linked data sets, relationships between EMS response time and reported injury in the crash report, and between police-reported injury and injury severity score were examined.
Conclusion:
Linking data from other sources can greatly enhance the information available to address road safety issues, data quality issues, and more. Linking data has the potential to result in biases that must be investigated as they relate to the use-case for the data.
Practical implications:
This research resulted in a transferable heuristic approach that can be used to link data sets that are commonly collected by agencies across the world. It also provides guidance on how to check the linked data for biases and errors.
Connected Vehicle (CV) notifications enhance drivers' situational awareness, where, depending on traffic flow characteristics, CV drivers might require sufficient distance and time to adapt their driving behavior accordingly. Hereof, in addition to CVs Market Penetration Rate (MPR) and traffic flow characteristics, humans are still in the safety loop. Hence, there might be a human-related unobserved heterogeneity affecting the safety performance of CVs. A conflation of microsimulation and advanced statistical modeling was followed to account for this unobserved heterogeneity by investigating the random effect of varying delivery distances, specifically provided for the Work Zone Warning (WZW) application. CVs behaviors were calibrated based on the results of driving simulator experiments. A typical WZ was simulated under VISSIM using three levels of low, moderate, and high traffic volumes. Five levels of CV-MPR received the WZW on four different settings of delivery distances, including 1500, 3000, 4500, and 6000 ft, upstream of the Transition Area. Accordingly, 60 scenarios were simulated, and the number of critical Time-to-Collision (TTC) was considered as the response count variable per scenario. Four hierarchical Negative Binomial regressions under Bayesian inference were conducted on 60 observations over four WZ areas, where the delivery distance was treated as a random factor. Analysis of the Intraclass Correlation Coefficient showed that the number of observed critical TTCs is nested within the delivery distances. It was ascertained, at any CV-MPR, when the WZW delivery distance increases, the number of critical TTCs decreases that indicates the same safety performance at higher CV-MPR can be achieved even at lower MPR if the WZW distancing increases. Besides, the optimal Road Side Unit (RSU) placement for WZW was found to be 4500 ft upstream of the Transition Area regardless of traffic volume and CV-MPR. Although this study was restricted to the CV-WZW application, the introduced delivery distance for CV notifications should be regarded in future studies as a contributing factor to the safety assessment of other CV applications.
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.
In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mixed logit model was used to determine statistically significant crash severity contributing factors and their effects. Significant factors in both models included work-zone-specific characteristics and crash-specific characteristics, where environmental characteristics were only significant in the daytime model. In addition, results from parameter transferability test provided evidence that daytime and nighttime crashes need to be modeled separately. Further, to explore the nonlinear relationship between crash severity levels and time-of-day, as well as compare the effects of variables to that of the logit model and assess prediction performance, a Support Vector Machines (SVM) model trained by Cuckoo Search (CS) algorithm was utilized. Opening the SVM black-box, a variable impact analysis was also performed. In addition to the characteristics identified in the logit models, the SVM models also included the impacts of vehicle-level characteristics. The variable impact analysis illustrated that the termination area of the work zone is most critical for both daytime and nighttime crashes, as this location has the highest increase in severe injury likelihood. In summary, results of this study demonstrate that work zone crashes need to be modeled separately by time-of-day with a high level of confidence. Furthermore, results show that the CS-SVM models provide better prediction performance compared to the SVM and logit models.
In this research, we propose novel mathematical models and algorithms for optimizing connected and automated vehicles’ (CAVs) trajectories at freeway weaving segments assuming 100 percent CAV market penetration. The proposed system receives vehicle arrival information and generates optimal trajectories that are relayed to the CAVs. The algorithm simulates vehicle arrivals and develops optimal vehicle trajectories on a 2-lane weaving section (one mainline and one auxiliary lane). We test the optimization algorithm under a variety of demand scenarios and using real-world arrival data. Results suggest that the proposed algorithm increases the average travel speed and capacity by 12 to 16% and up to 11%, respectively when compared to HCM estimates. Also, our case study shows the algorithm increases the average speed by 17%, 30%, and 38% for minimum time headways of 1.7 s, 1.4 s, and 1 s, respectively, compared to conventional vehicles.
In this study, emergency medical services times, along with other crash-related explanatory variables, have been used to investigate influential factors on injury severity. To overcome the complexity of emergency medical services times impact on crash outcome, the interaction effects of EMS times and injury location on the body were also investigated in a separate model. This study utilized the linked data of police-reported crash data and emergency medical services runs, including 2192 crash injuries that transferred to hospital. A random-effects ordered probit approach was implemented to identify effective factors on crash injury severity. Three models of (1) crash-related variables, (2) crash-related and emergency medical services times and (3) crash-related, emergency medical services times and interaction effects of EMS times and injury location on the body were developed. Although the outcome could not find the impact of faster emergency medical services times on injury severity in the second model, in the third model, faster response time and slower on-scene time were associated with decreasing the severity of entire-body injuries. We discuss why this may be the case.
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