Measuring Road Safety Using Surrogate Events provides researchers and practitioners with the tools they need to quickly and effectively measure traffic safety. Traditional crash-based safety analyses are being undermined by today's growing use of intelligent vehicular and road safety technologies. Crash surrogates--or near misses--can be more effectively used to measure the future risk of crashes. Measuring Road Safety Using Surrogate Events advances the idea of using these near-crash techniques to deliver quicker and more adequate measurements of safety. The book explores the relationships between traffic conflicts and crashes using an extrapolation of observed events rather than post-crash data, which is significantly slower to obtain. It delivers sound estimation methods based on rigorous scientific principles, offering compelling new tools to better equip researchers to understand road safety and its factors. Consolidates the latest thinking from disparate places into one resource Establishes a consistent use of key terms, definitions, and concepts to help codify this emerging field Numerous application-oriented case studies throughout Learning aids include chapter objectives, glossary, and links to data used in examples

This book increases the level of knowledge on road safety contexts, issues and challenges; shares what can currently be done to address the variety of issues; and points to what needs to be done to make further gains in road safety.

"Traffic safety may be analyzed with the use of surrogate safety measures, measures of safety that do not incorporate collision data but rather rely on the concept of traffic conflicts. Use of these measures provides several benefits over use of more traditional analysis methods with historical crash data. Surrogate measures eliminate the need to wait for crashes to occur to conduct a safety analysis. The amount of time required for enough crash data to accumulate can be significant, delaying safety analyses. Similarly, these measures allow for safety analysis to be conducted prior to crashes occurring, potentially calling attention to hazardous areas which may be altered to prevent crashes. In addition to these benefits, traffic conflicts occur much more frequently than collisions, generating many more data points which in turn make statistical methods of analysis more effective. Evaluating surrogate safety measures for a particular transportation network is most effectively done with the use of traffic microsimulation or with connected vehicle data. Traffic microsimulation (such as the use of PTV VISSIM) will generate kinematic data that may then be used for computation of surrogate safety measures. A significant amount of research has been done on this topic, resulting in the establishment of algorithms for calculation of several different surrogate measures and validation of these measures. Kinematic data from connected vehicles has also been used for the calculation of surrogate safety measures. One data point collected by connected vehicles is harsh braking events which could serve as a surrogate safety measure. Because drivers usually brake more gently if given the opportunity to do so, harsh braking events indicate that a traffic conflict has occurred or is about to occur. Such events take away the driver’s opportunity to brake gently. This research establishes statistical models which relate harsh braking events to crashes on intersections and segments in Salt Lake City, Utah. The findings indicate that harsh braking events have the effect of reducing expected crashes because they represent traffic conflicts which were remedied through the use of harsh braking as an evasive action. The presence of schools and the presence of left turn lanes were also found to be statistically significant crash predictors. In addition to this research work a paper outlining the existing state of safety analysis with surrogate safety measures and evaluating the usefulness and practicality of various existing measures is presented."--Boise State University ScholarWorks.

This report presents a framework for measuring safety in automated vehicles (AVs): how to define safety for AVs, how to measure safety for AVs, and how to communicate what is learned or understood about AVs.

Sudden changes in weather conditions might have a tremendous impact on traffic operation and safety. Previous studies investigated the impact of adverse weather conditions on traffic safety and to what extent these conditions may increase crash risks on roadways. The increase in weather-related crashes has motivated researchers to study driver behavior and performance under different weather conditions. Adverse weather affects driver decisions and may result in taking improper actions while facing a crash/near-crash event in comparison with clear weather conditions. While driver behavior and performance are considered among the key contributing factors to crashes, little research have been conducted to fully understand the difference between normal driving and safety critical scenarios for developing crash prevention means. Monitoring driver behavior and performance during a safety critical event has been a challenging task for researchers due to the lack of detailed event records. Moreover, the issues associated with traditional police records of crashes have limited a comprehensive analysis of how the deviation from normal driving may lead to a culmination of crashes. In addition, one of the main reasons for the increase number of crashes on roadways is that drivers may not appropriately adapt their behaviors to compensate for adverse weather conditions. The lack of real-time trajectory-level weather information and the sporadic data collected from weather stations have limited researchers from conducting sound safety studies. This study attempts to fulfill some of the research gaps to assist transportation agencies and traffic safety researchers to improve safety and mobility. In general, the research efforts conducted in this dissertation aims to improve traffic safety in adverse weather conditions on freeways. In addition, this dissertation aims to provide practical recommendations to transportation agencies that can efficiently enhance traffic safety in Connected and Automated Vehicle (CAV) environments. The dissertation goal was achieved through utilizing different subsets of the Second Strategic Highway Research Program (SHRP2) – Naturalistic Driving Study (NDS) data. The utilization of the NDS real-time trajectory dataset would open a new horizon in traffic safety research related to connected and automated vehicles. In this study, five main research objectives, each with multiple tasks, were set to enhance traffic safety in adverse weather conditions. The first objective was to provide a better understanding of what happened before and during a near-crash event and comparing it with normal matched trips. This objective would help to develop effective countermeasures that reduce crash risks on freeways. The second objective was to detect Surrogate Measures of Safety (SMoS) on freeways by comparing environmental conditions and vehicle kinematics signatures of near-crash events to their matched normal driving trips. A time-chunking technique was used with different aggregation levels to monitor changes in vehicle kinematics on a timescale. This approach established a comparative study of parametric and non-parametric techniques to estimate near-crashes on freeways. A Binary Logistic Regression model was used as a parametric prediction model, while the Decision Tree (DT), k-Nearest Neighbors (k-NN), and Deep Learning Artificial Neural Network (ANN) were used as non-parametric prediction models. The results showed that the logistic regression model has provided an excellent fit to the input data and can predict near-crashes with an outstanding accuracy. In addition, DT and Deep Learning ANN machine learning algorithms showed higher prediction accuracy of near-crashes compared to the k-NN algorithm. The third objective was to investigate normal and risky driving condition patterns under both rainy and clear weather conditions. The fourth objective was to distinguish between normal driving and risky driving condition patterns in rainy and clear weather conditions using real-time trajectory-level datasets. To achieve the third and fourth objectives, the SHRP2 - NDS data were employed to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started one second earlier in rainy weather condition than in clear weather condition. Furthermore, risky driving patterns extended three seconds in rainy weather condition, while it was two seconds in clear weather condition.

Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials Provides examples and case studies for most models and methods Includes learning aids such as online data, examples and solutions to problems

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.