In Virginia, sections of I-77 and I-64 in mountainous parts of the state have significant recurring fog events. These locations have also been the sites of several chain reaction crashes involving more than 50 vehicles during fog. These crashes were typically caused by drivers traveling too fast for the visibility conditions. To improve safety on the I-77 corridor, the Virginia Department of Transportation constructed a variable speed limit (VSL) system that posts dynamic speed limits based on the visibility condition. As of April 2016, the system was undergoing pre-deployment testing. Before the system was activated, it was important to understand existing driver speed choice behavior during low visibility conditions. It was possible that posting a VSL speed based only on the stopping sight distance (SSD) could create significant speed variance and decrease safety if drivers were driving much faster than conditions would warrant. In this study, crash, speed, and visibility data were examined at several locations on I-64 and I-77 where there were recurring fog events. The crash history for I-77 revealed that crashes during low visibility conditions were more likely to be severe and involve more than two vehicles than crashes during clear conditions. Mean speed analysis found that observed mean speeds exceeded safe speeds for all low visibility conditions and at all sites. In the worst visibility conditions, drivers often exceeded the safe speed by more than 20 mph. Standard deviation analysis found that speed variance did not increase as visibility decreased on I-77, but for several locations on I-64, the standard deviation was different during low visibility when compared to clear conditions. Models were developed to allow a better understanding of the relationship between speed and visibility. The models showed that although motorists reduce their speeds in low visibility, there is still a significant differential between observed speeds and the safe speed calculated using the SSD. The models showed that speeds for I-64 were much less sensitive to changes in visibility compared to I-77. A possible explanation for this difference is the presence of illuminated in-pavement markers on I-64. The improved delineation provided by these markers during foggy conditions may cause drivers to perceive less of a need to reduce speed during limited visibility. It is also possible that mean speeds in low visibility conditions are higher on I-64 because of the regular commuters who may be more comfortable driving during foggy conditions. The observed driver behavior from this study is being used as a basis for the VSL control algorithm that is being implemented in the field. A primary concern of the operators of the VSL system is that it will not be heeded by all motorists and thus will result in increased speed variance in foggy conditions. The developed model was used to create a VSL control algorithm to help bridge the gap between current driver behavior and safe speed. It is recommended that future VSL system deployments reflect existing driver behavior in the initial algorithms as well. After VSL activation, speed and crash data for I-77 should be analyzed to determine the operational and safety effects of the system. If the system on I-77 reduces the frequency and severity of crashes, improves speed limit compliance, and reduces speed variance, a similar system should be developed for I-64 using the current driver behavior models from this study as part of the initial algorithm.

In Virginia, sections of I-77 and I-64 in mountainous parts of the state have significant recurring fog events. These locations have also been the sites of several chain reaction crashes involving more than 50 vehicles during fog. These crashes were typically caused by drivers traveling too fast for the visibility conditions. To improve safety on the I-77 corridor, the Virginia Department of Transportation constructed a variable speed limit (VSL) system that posts dynamic speed limits based on the visibility condition. As of April 2016, the system was undergoing pre-deployment testing. Before the system was activated, it was important to understand existing driver speed choice behavior during low visibility conditions. It was possible that posting a VSL speed based only on the stopping sight distance (SSD) could create significant speed variance and decrease safety if drivers were driving much faster than conditions would warrant. In this study, crash, speed, and visibility data were examined at several locations on I-64 and I-77 where there were recurring fog events. The crash history for I-77 revealed that crashes during low visibility conditions were more likely to be severe and involve more than two vehicles than crashes during clear conditions. Mean speed analysis found that observed mean speeds exceeded safe speeds for all low visibility conditions and at all sites. In the worst visibility conditions, drivers often exceeded the safe speed by more than 20 mph. Standard deviation analysis found that speed variance did not increase as visibility decreased on I-77, but for several locations on I-64, the standard deviation was different during low visibility when compared to clear conditions. Models were developed to allow a better understanding of the relationship between speed and visibility. The models showed that although motorists reduce their speeds in low visibility, there is still a significant differential between observed speeds and the safe speed calculated using the SSD. The models showed that speeds for I-64 were much less sensitive to changes in visibility compared to I-77. A possible explanation for this difference is the presence of illuminated in-pavement markers on I-64. The improved delineation provided by these markers during foggy conditions may cause drivers to perceive less of a need to reduce speed during limited visibility. It is also possible that mean speeds in low visibility conditions are higher on I-64 because of the regular commuters who may be more comfortable driving during foggy conditions. The observed driver behavior from this study is being used as a basis for the VSL control algorithm that is being implemented in the field. A primary concern of the operators of the VSL system is that it will not be heeded by all motorists and thus will result in increased speed variance in foggy conditions. The developed model was used to create a VSL control algorithm to help bridge the gap between current driver behavior and safe speed. It is recommended that future VSL system deployments reflect existing driver behavior in the initial algorithms as well. After VSL activation, speed and crash data for I-77 should be analyzed to determine the operational and safety effects of the system. If the system on I-77 reduces the frequency and severity of crashes, improves speed limit compliance, and reduces speed variance, a similar system should be developed for I-64 using the current driver behavior models from this study as part of the initial algorithm.

Vehicle crashes remain the leading cause of accidental death and injuries in the United States, claiming tens of thousands of lives and injuring millions of people each year. Many of these crashes occur during nighttime, where a variety of modifiers affect the risk of a crash, primarily through the reduction of object visibility. Furthermore, many of these modifiers also affect the nighttime mobility of older drivers, who avoid driving during the nighttime. Thus, a two-fold need exists for new technologies that enhance night visibility. Two separate studies were completed as part of this research. Study 1 served as a baseline by evaluating visual performance during nighttime driving under clear weather conditions. Visual performance was evaluated in terms of the detection and recognition distances obtained when different vision enhancement systems were used at the Smart Road testing facility. Study 2, also using detection and recognition distances, compared the visual performance of drivers during low visibility conditions (i.e., due to rain) to the risk perception of driving during nighttime under low visibility conditions. These comparisons were made as a function of various vision enhancement systems. The age of the driver and the characteristics of the object presented (e.g., contrast, motion) were variables of interest in both studies.

In the United States, traffic crashes claim the lives of 30,000 people every year and is the leading cause of death for 5-24 year olds. Driver error is the leading factor in over 90 percent of motor vehicle crashes, with the roadway and the vehicle each only accounting for about 2 percent of crashes. In the United States, nearly a third of fatal crashes are due to speeding and therefore, a critical step in improving traffic safety is research aimed to reduce speeding, such as crash data analysis, outreach campaigns, targeted enforcement, and understanding speed selection. In this dissertation, a multi-faceted approach was taken to improve roadway safety by examining the speeding-related crash designation, improving speed limit setting practices, and understanding the causes of speeding. Multiple experiments were conducted under this overarching goal. These experiments included an analysis of speeding-related crashes in Massachusetts, a naturalistic driving study, and a driving simulator study which investigated the causes of speeding. Collectively, the findings from these experiments can expand upon existing speed prediction models, improve crash data influence speed limit setting practices, guide speed management programs such as speed enforcement, and be used in public safety outreach campaigns.

Distracted driving is among the leading causes of motor vehicle crashes worldwide, though the magnitude of this problem is difficult to quantify given the limitations of police-reported crash data. A more promising approach is to evaluate the impacts of distraction in real-world driving events. To that end, this study leverages data from the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) to gain important insights into the risks posed by driver distraction. The objectives of this study are to assess the risk of crash and near-crash events under different contextual environments based upon whether the driver was engaged in any secondary (i.e., non-driving related) tasks. The research also compares speed profiles of distracted drivers in low-speed and high-speed environments, providing important insights into how driver behavior changes based upon the type and intensity of distraction. The first analysis uses the standard SHRP 2 data to compare the differences between near-crash risks on limited access freeways and two-lane highways. Mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the risks were more pronounced for those distractions that were a combination of cognitive, visual, and manual distractions (for e.g., cell phone texting). While the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. The second analysis uses a subset of the NDS data that were focused on naturalistic engagement in secondary tasks (NEST). The NEST data were used to assess how the type and duration of distraction impacted the likelihood of crash and near-crash events. Separate comparisons were made between crashes and near-crashes with "normal" baseline driving events. The results show the duration of distraction to be a strong predictor of both crash and near-crash risk and were found to have similar relationships with crashes and near-crashes. The risks were highest for those secondary tasks that introduce a combination of visual and manual distractions that provides evidence that distractions requiring higher levels of engagement have more pronounced impacts on safety. The third and final analysis uses NEST data to analyze how driver speed selection varies based upon the types of secondary tasks that a driver is engaged in. Comparisons are made as to differences between high-speed and low-speed environments. Two-way random effects linear regression models were estimated for both speed regimes while controlling for driver, roadway, and traffic characteristics. In general, engagement in all tasks was found to decrease speeds in high-speed environments, while the effects were mixed in low-speed settings. These changes in speeds are much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, handheld cellphone talking was associated with highest speed changes in both environments followed by reaching/manipulating an object and holding an object. Ultimately, the results of this study provides further motivation for more aggressive legislation and enforcement against distracted driving. This can be achieved by enforcing strict laws and fines, graduated licensing process, public campaigns, modified infrastructure (rumble strips and tactile lane marking), and other such measures.

Speeding is the number one road safety problem in a large number of OECD/ECMT countries. It is responsible for around one third of the current, unacceptably high levels of road fatalities. Speeding has an impact not only on accidents but also on the ...

There are approximately 4,000 fatalities in crashes involving trucks and buses in the United States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these crashes might have involved fatigued drivers. The stresses associated with their particular jobs (irregular schedules, etc.) and the lifestyle that many truck and bus drivers lead, puts them at substantial risk for insufficient sleep and for developing short- and long-term health problems. Commercial Motor Vehicle Driver Fatigue, Long-Term Health and Highway Safety assesses the state of knowledge about the relationship of such factors as hours of driving, hours on duty, and periods of rest to the fatigue experienced by truck and bus drivers while driving and the implications for the safe operation of their vehicles. This report evaluates the relationship of these factors to drivers' health over the longer term, and identifies improvements in data and research methods that can lead to better understanding in both areas.