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• Assessing the transferability of a crash modification factor is an example of assessing external validity. • Assessing external validity almost certainly requires understanding how a modification works. • A case study is presented developing and testing an explanation for a crash modification effect. • Guidance to others wanting to develop explanations is provided. Although the Highway Safety Manual (HSM) now provides empirical tools for predicting the safety consequences of highway engineering decisions, these tools represent the driver and vehicle conditions prevailing in the United States during the last few decades. As automated vehicles improve in capability and increase in market share these conditions will change, possibly reducing the accuracy of HSM predictions. Assessing the transferability of a crash modification factor to new situations almost certainly requires an explanation of how the modification achieves its effect, but at present there is little guidance on how such explanations might be posed and tested. This paper describes the use of micro-simulation to develop an explanation of how pedestrian hybrid beacons (PHB) modify pedestrian crash likelihood. Since the literature indicated that PHBs can affect both pedestrian and driver behavior it was necessary to include both possibilities in the model. To simulate injury severity distributions similar to those recorded in a crash database it was necessary to propose that almost all simulated drivers attempt to brake in pedestrian/vehicle encounters. Then changing the simulated fraction of careful pedestrians from between 0% and 30% to between 80% and 90% gave simulated crash modification factors similar to estimates reported in the literature. The resulting working hypothesis then is that PHBs achieve their crash reduction effect in large part by modifying pedestrian behavior. This is not so much a direct observation as it is an inference to the best explanation. That is, the support for the hypothesis comes from its ability to explain the data at hand. This hypothesis should be tested further, and additional tests are proposed. [ABSTRACT FROM AUTHOR]

Abstract: Considerable attention is focused on plans for sheltering or evacuating the population of the US national capital region in response to a regional emergency such as a terrorist attack or natural disaster. Such planning engages multiple disciplines spanning infrastructure engineering, emergency management, health care, mass communication, water and food supply, logistics, and others. Knowledge of population behaviors should influence the many dimensions of protection, prevention, response, and recovery. Of particular interest are the behaviors and needs of the resident and non-resident populations in the aftermath of a regional disaster, including those at home, at work, and traveling. The authors deployed a 30-min telephone survey to 2700 residents of the region to gain knowledge of their intended behaviors in the event of a variety of potential dirty bomb attacks. The survey provides a unique foundation for the current paper. The paper will identify and model the assumptions of population behaviors that most affect agency priorities for emergency planning including regional sheltering and evacuation following a radiological disaster such as a dirty bomb. The technical approach assessed several planning initiatives across performance criteria derived from strategic plans and applied combinations of behavioral assumptions to vary the relative importance of each criterion. The results reveal the behavioral scenarios that are most significant to the prioritization of planning initiatives and identify the highest and lowest priority initiatives across the criteria used. [Copyright &y& Elsevier]

Abstract: As automobile transportation continues to increase around the world, bicyclists, pedestrians, and motorcyclists, also known as vulnerable road users (VRUs), will become more susceptible to traffic crashes, especially in countries where traffic laws are poorly enforced. Many countries, however, are employing innovative strategies to ensure that road users can more safely navigate the urban landscape. While bicyclists and motorcyclists are important road users, this paper will focus on pedestrian crash problems and solutions. Pedestrians are most at risk in urban areas due in part to the large amount of pedestrian and vehicle activity in urban areas. With this in mind, designing safe, accessible, and comprehensive facilities for pedestrians is vital to reducing pedestrian crashes. This paper will provide some insight into the magnitude of the pedestrian crash problem around the world, and will offer some lessons learned from several countries, particularly in Europe and the U.S., for improving pedestrian safety. Beginning with pedestrian safety statistics at the global, regional, and national levels, this paper will address potential countermeasures and strategies for improving pedestrian safety from an international perspective. [Copyright &y& Elsevier]

• The study investigated the transferability of real-time safety models for signalized intersections to new jurisdictions. • The real-time safety models relate rear-end conflicts at through lanes to various dynamic traffic parameters. • Two corridors in California and Atlanta States were used in the analysis as destination jurisdictions. • The models' transferability was evaluated with and without a local calibration at the new jurisdictions. • The results showed that the models are transferable and can be used for real-time safety optimization. Optimizing traffic signals in real-time for safety performance can be executable in the era of Connected Vehicles (CVs) when real-time information on vehicle positions and trajectories is available. To achieve this, real-time safety models are needed to understand how changes in signal controllers affect safety in real-time. Recently, several real-time safety models were developed for signalized intersections that relate various dynamic traffic parameters to the number of rear-end traffic conflicts at the signal cycle level. The traffic parameters included: traffic volume, maximum queue length, shock wave speed and area, and platoon ratio. For wider application of these models to other jurisdictions, the transferability of these models needs to be examined. Therefore, this paper aims to investigate the transferability of several signalized intersections real-time safety models to new jurisdictions. Two corridors of signalized intersections in California and Atlanta were used in the analysis as destination jurisdictions. Detailed vehicle trajectories for these corridors were obtained from the Next Generation Simulation (NGSIM) data. Various transferability analysis approaches were applied. The transferability of the real-time safety models was evaluated with and without a local calibration for the model parameters at the new jurisdictions. Several goodness-of-fit measures were examined to assess the ability of the developed models to predict traffic conflicts. Overall, the results showed that the real-time safety models are transferable, which confirms the validity of using them for real-time safety evaluation of signalized intersections. [ABSTRACT FROM AUTHOR]

• A highway-rail grade crossing consolidation model was created using XGBoost machine learning algorithm. The developed model contained 14 significant variables and the overall accuracy of 0.991% was obtained for it. • The significant variables included in the model were: in or near city, night thru train movement (6 pm to 6 am), day thru train movement (6 am to 6 pm), maximum timetable speed, typical minimum speed, typical maximum speed, crossbuck assemblies, total count of flashing light pair, crossing surface (main track), intersecting roadway within 500 ft., AADT, estimated percent trucks, total switching train, and average number of school buses passing over the crossing on a school day. • Based on this model, 62% of current highway-rail grade crossings in the USA should be either closed or improved. In the United States, there are approximately 212,000 highway-rail grade crossings, some of which experience vehicle-train incidents that often cause a massive financial burden, loss of life, and injury. In 2017, there were 2,108 highway-rail incidents resulting in 827 injuries and 307 fatalities nationwide. To eliminate collision risks, crossing grade separation and active alarm improvement are commonly used. Moreover, crossing closures are considered to be the most effective safety improvement program. While it may be very difficult, and in some cases impossible to close crossings, there are some incentive programs that facilitate the closure process. One of these programs is a working consolidation model that aims to determine crossing closure suitability. Using details of highway-rail grade crossings in the United States and applying an eXtreme Gradient Boosting (XGboost) algorithm, this paper proposes a data-driven consolidation model that takes into consideration a number of engineering variables. The results indicated an overall accuracy of 0.991 for the proposed model. In addition, the developed XGboost consolidation model reported the relative importance of the variables input to the model, offering an in-depth understanding of the model's behavior. Finally, for the practical implementation of the model, a simplified version containing fewer variables was developed. A sensitivity analysis was performed considering the aggregate gain and the different correlation threshold values between variables. This analysis developed a simplified model utilizing 14 variables, with aggregated gain values of 75% and a correlation threshold of 0.9 which would perform similarly to the full model. Based on this model, 62% of current highway-rail grade crossings should be closed. [ABSTRACT FROM AUTHOR]

Highlights • Safety performance of a new design called a super diverging diamond interchange (super DDI) was studied using VISSIM and SSAM. • The new Super DDI showed a high potential both in terms of traffic safety and pedestrian safety. • Lower traffic involving in each conflict point should be one of the reasons for the promising traffic safety performance of the Super DDI. • Since there is no free-flowing crossing for pedestrians in a super DDI, pedestrian performance of the super DDI is predicted to be safer than a DDI design. Abstract Most existing interchanges in the United States were built more than 50 years ago based on old design policies. Many of these designs are not consistent with current traffic and pedestrian demands anymore. Therefore, this inconsistency has caused problems regarding operation and safety. This paper models safety performance of a new design called super diverging diamond interchange (super DDI) considering VISSIM simulation and surrogate safety assessment model (SSAM). Six other interchange designs were also considered for comparing to the new super DDI design. Overall, 252 simulation scenarios were modeled in VISSIM and then tested by SSAM. Also, the same number of tests were considered to evaluate pedestrian performance of the designs considered in this study. Based on results, super DDI showed a high potential either in terms of traffic safety and pedestrian safety. In comparison to other designs, super DDI had the minimum number of simulated conflicts as well as the lowest mean speed and time to collision (TTC) of simulated conflicts. This fact shows that super DDI could perform promising in reducing crash frequency and crash severity. Reviewing the geometry of the super DDI, lower traffic involving in each conflict point should be one of the main reasons for the promising traffic safety performance of the design. Regarding pedestrian performance, super DDI got the third rank of the lowest mean pedestrian travel times. There is no free-flowing conflict between vehicles and pedestrians in a super DDI. Therefore, pedestrian paths of the super DDI are predicted to be safer than the paths in a typical DDI design. [ABSTRACT FROM AUTHOR]

Highlights • Big data generated by connected vehicles is harnessed for proactive safety management. • A new framework to quantify the variations in driving behavior is developed. • Driving volatility is associated with the frequency of multiple crash types. • Longitudinal volatility is highly associated with rear-end crashes. • Longitudinal/lateral volatilities are correlated with sideswipe, angle, and head-on crashes. Abstract Connected and automated vehicles have enabled researchers to use big data for development of new metrics that can enhance transportation safety. Emergence of such a big data coupled with computational power of modern computers have enabled us to obtain deeper understanding of instantaneous driving behavior by applying the concept of "driving volatility" to quantify variations in driving behavior. This paper brings in a methodology to quantify variations in vehicular movements utilizing longitudinal and lateral volatilities and proactively studies the impact of instantaneous driving behavior on type of crashes at intersections. More than 125 million Basic Safety Message data transmitted between more than 2800 connected vehicles were analyzed and integrated with historical crash and road inventory data at 167 intersections in Ann Arbor, Michigan, USA. Given that driving volatility represents the vehicular movement and control, it is expected that erratic longitudinal/lateral movements increase the risk of crash. In order to capture variations in vehicle control and movement, we quantified and used 30 measures of driving volatility by using speed, longitudinal and lateral acceleration, and yaw-rate. Rigorous statistical models including fixed parameter, random parameter, and geographically weighted Poisson regressions were developed. The results revealed that controlling for intersection geometry and traffic exposure, and accounting unobserved factors, variations in longitudinal control of the vehicle (longitudinal volatility) are highly correlated with the frequency of rear-end crashes. Intersections with high variations in longitudinal movement are prone to have higher rear-end crash rate. Referring to sideswipe and angle crashes, along with speed and longitudinal volatility, lateral volatility is substantially correlated with the frequency of crashes. When it comes to head-on crashes, speed, longitudinal and lateral acceleration volatilities are highly associated with the frequency of crashes. Intersections with high lateral volatility have higher risk of head-on collisions due to the risk of deviation from the centerline leading to head-on crash. The developed methodology and volatility measures can be used to proactively identify hotspot intersections where the frequency of crashes is low, but the longitudinal/lateral driving volatility is high. The reason that drivers exhibit higher levels of driving volatility when passing these intersections can be analyzed to come up with potential countermeasures that could reduce volatility and, consequently, crash risk. [ABSTRACT FROM AUTHOR]

Highlights • Crash severity effects of Adaptive signal control technology (ASCT) were assessed. • Multiple ASCT systems deployed across Pennsylvania and Virginia were analyzed. • Combined best fit model revealed reductions in severe plus moderate and minor injury crashes by 5.24% and 9.91%. • Combined best fit model showed a low forecast error of 0.301 and was also observed to be spatially transferable. Abstract Adaptive signal control technology (ASCT) is an intelligent transportation systems (ITS) technology that optimizes signal timings in real time to improve corridor flow. While a few past studies have examined the impact of ASCT on crash frequency, little is known about its effect on injury severity outcomes. Similarly, the impact of different types of ASCTs deployed across different states is also uncertain. This paper therefore, used ordered probit models with random parameters to estimate the injury severity outcomes resulting from ASCT deployment across Pennsylvania and Virginia. Two disparate systems deployed across the two different states were analyzed to assess whether they had similar impacts on injury severity, although signal timings are optimized using different algorithms by both systems. The estimation results revealed that both ASCT systems were associated with reductions in injury severity levels. Marginal effects showed that Type A ASCT systems reduced the propensity of severe plus moderate and minor injury crashes by 11.70% and 10.36% while type B ASCT reduced the propensity of severe plus moderate and minor injury crashes by 4.39% and 6.92%. Similarly, the ASCTs deployed across the two states were also observed to reduce injury severities. The combined best fit model also revealed a similar trend towards reductions in severe plus moderate and minor injury crashes by 5.24% and 9.91%. This model performed well on validation data with a low forecast error of 0.301 and was also observed to be spatially transferable. These results encourage the consideration of ASCT deployments at intersections with high crash severities and have practical implications for aiding agencies in making future deployment decisions about ASCT. [ABSTRACT FROM AUTHOR]

Highlights • The bivariate framework examines geometry effects on safety across the road classes. • Three crash frequency models were estimated by crash severity level and road class. • The sensitivity of crash frequency to each geometric factor was investigated. • Results can help highway engineers to evaluate current design standards and policy. Abstract This paper examined the accident risk factors associated with highway traffic and roadway design, for each of three highway classes in the United States using a bivariate modeling framework involving two levels of accident severity. With regard to the highest class (Interstates), the results suggest that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume and average vertical grade, but less sensitive to the inside shoulder width and the median width. For US Roads, it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume, outside shoulder width, pavement condition, and median width but less sensitive to the average vertical grade. For the relatively lowest-class roads (State Roads), it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to the traffic volume, lane width, outside shoulder width, and pavement condition. Compared to the relatively lower-class highways, accidents at higher-class highways are more sensitive to: changes in traffic volume, average vertical grade, median width, inside shoulder width, and the pavement condition (no-casualty accidents only); but less sensitive to changes in lane width, pavement condition (casualty accidents only), and the outside shoulder width. This variation in sensitivity across the different road classes could be attributed to the differences in road geometry standards across the road classes, as the results seem to support the hypothesis that these standards strongly influence accident occurrence. It is hoped that the developed bivariate negative binomial models can help highway engineers to evaluate their current design standards and policy, and to assess the safety consequences of changes in these standards in each road class. [ABSTRACT FROM AUTHOR]

Abstract: This paper assesses group differences in severe and fatal road-traffic accidents by using a unique database that merges road-traffic records with the Israeli census data. The database traces, over a period of 9 years, a group of drivers that comprises 20% of the Israeli population and explores the probability of their being involved in an accident. This unique database enables the investigation of drivers’ socioeconomic and demographic characteristics, while controlling for a variety of variables, such as estimated daily distance traveled and license type. Testing a previously published theoretical paper on the social bases of accidents, the findings expose significant group differences in estimated probabilities of being involved in severe and fatal accidents. For example, estimated probabilities of accident involvement are higher for males than for females, for non-Jewish drivers than for Jewish, and for drivers whose origins are in Africa and Asia than in America and Europe. Furthermore, the higher one''s education and socioeconomic status, the lower is the probability of accident-involvement. The implications of the findings for developing road-safety programs and suggestions for future research are discussed. [Copyright &y& Elsevier]

Abstract: Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes. [Copyright &y& Elsevier]

This paper presents costs per US motor vehicle crash victim differentiated into many more diagnostic categories than prior estimates. These unit costs, which include the first keyed to the 1990 edition of Abbreviated Injury Scale (AIS) threat-to-life severity scores, are reported by body part, whether a fracture/dislocation was involved, and the maximum AIS score among the victim’s injuries. This level of detail allows for a more accurate estimation of the social costs of motor vehicle crashes. It also allows for reliable analyses of interventions targeting narrow ranges of injuries. The paper updates the medical care data underlying the US crash costs from 1979 to 1986 to the mid 1990s and improves on prior productivity cost estimates. In addition to presenting the latest generation of crash victim costs, this paper analyzes the effects of applying injury costs classified by AIS code from the 1985 edition to injury incidence data coded with the 1990 edition of AIS. This long-standing practice results in inaccurate cost-benefit analyses that typically overestimate benefits. This problem is more acute when old published costs adjusted for inflation are used rather than the recent costs. [Copyright &y& Elsevier]

In the United States, passenger vehicles are shifting from a fleet populated primarily by cars to a fleet dominated by light trucks and vans (LTVs). Because light trucks are heavier, stiffer, and geometrically more blunt than passenger cars, they pose a dramatically different type of threat to pedestrians. This paper investigates the effect of striking vehicle type on pedestrian fatalities and injuries. The analysis incorporates three major sources of data, the Fatality Analysis Reporting System (FARS), the General Estimates System (GES), and the Pedestrian Crash Data Study (PCDS). The paper presents and compares pedestrian impact risk factors for sport utility vehicles, pickup trucks, vans, and cars as developed from analyses of US accident statistics. Pedestrians are found to have a two to three times greater likelihood of dying when struck by an LTV than when struck by a car. Examination of pedestrian injury distributions reveals that, given an impact speed, the probability of serious head and thoracic injury is substantially greater when the striking vehicle is an LTV rather than a car. [Copyright &y& Elsevier]

• Introduces a crash sequence analysis procedure of data processing, sequence encoding, sequence comparison, and clustering. • Compares two event encoding schemes and five optimal-matching-based dissimilarity measures for crash sequence comparison and clustering. • Analyzes single-vehicle crash sequences of events occurred on United States interstate highways in 2016–2018. • Sequence clustering results are evaluated by comparing with a benchmark crash categorization. • Results show that generally, the transition-rate-based localized optimal matching dissimilarity measure and consolidated event encoding scheme generate sequence clustering results closest to the benchmark. Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016 to 2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop crash modification factors (CMFs) and estimate the average crash costs applicable to a wide range of road-barrier scenarios that involved three types of road barriers (concrete barriers, W-beam guardrails, and high-tension cable barriers) to produce a suitable basis for comparing barrier-oriented design alternatives and road improvements. The intention was to perform the most comprehensive and in-depth analysis allowed by the cross-sectional method and the crash data available in Indiana. To accomplish this objective and to use the available data efficiently, the effects of barrier were estimated on the frequency of barrier-relevant (BR) crashes, the types of harmful events and their occurrence during a BR crash, and the severity of BR crash outcomes. The harmful events component added depth to the analysis by connecting the crash onset with its outcome. Further improvement of the analysis was accomplished by considering the crash outcome severity of all the individuals involved in a crash and not just drivers, utilizing hospital data, and pairing the observations with and without road barriers along same or similar road segments to better control the unobserved heterogeneity. This study confirmed that the total number of BR crashes tended to be higher where medians had installed barriers, mainly due to collisions with barriers and, in some cases, with other vehicles after redirecting vehicles back to traffic. These undesirable effects of barriers were surpassed by the positive results of reducing cross-median crashes, rollover events, and collisions with roadside hazards. The average cost of a crash (unit cost) was reduced by 50% with cable barriers installed in medians wider than 50 ft. A similar effect was concluded for concrete barriers and guardrails installed in medians narrower than 50 ft. The studied roadside guardrails also reduced the unit cost by 20%–30%. Median cable barriers were found to be the most effective among all the studied barriers due to the smaller increase in the crash frequency caused by these barriers and the less severe injury outcomes. More specifically, the occupants of vehicles colliding with near-side cable barriers tended to have less severe injuries than occupants of vehicles entering the median from median’s farther side. The near-side cable barriers provided protection against rollover inside the median and against a potentially dangerous collision with or running over the median drain; therefore, the greatest safety benefit can be expected where cable barriers are installed at both edges of the median. The CMFs and unit crash costs for 48 road-barrier scenarios produced in this study are included in this paper. [ABSTRACT FROM AUTHOR]

Despite similarities to the US in terms of transportation, land use, and culture, Australia kills 5.3 people per 100,000 population on the roads each year, as compared to the US rate of 12.4. Similar trends hold when accounting for distance driven and the number of registered cars. This paper seeks to understand what is behind the road safety disparities between these two countries. The results suggest that a number of inter-related factors seem to play a role in the better road safety outcomes of Australia as compared to the US. This includes Australia’s strategies related to seat belt usage and impaired driving as well as their efforts to help curb vehicle speeds and reduce exposure. Design-related differences include a much greater reliance on roundabouts and narrower street cross-sections as well as guidelines that encourage self-enforcing roads. Policy-related differences include stronger and more extensive enforcement programs, restrictive licensing programs, and higher driving costs. Combined with a more urban population and multimodal infrastructure, Australia tends to discourage driving mileage and exposure while encouraging safer modes of transportation such as transit, at least more so than in most of the US. Australia also enacted their version of Vision Zero – called the Safe System Approach – more than a decade before similar policies began cropping up in US cities. While it is difficult to attribute recent road safety successes to any specific policy, Australia continues to expand their lead on the US in terms of safety outcomes and is a road safety example worthy of consideration. [ABSTRACT FROM AUTHOR]

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance. [ABSTRACT FROM AUTHOR]

Analysis of the causes underlying runway incursions is fundamental for the development of effective mitigation measures. However, there are significant weaknesses in the current methods to model these factors. This paper proposes a structured framework for modelling causal factors and their relationship to severity, which includes a description of the airport surface system architecture, establishment of terminological definitions, the determination and collection of appropriate data, the analysis of occurrences for severity and causes, and the execution of a statistical analysis framework. It is implemented in the context of U.S. airports, enabling the identification of a number of priority interventions, including the need for better investigation and causal factor capture, recommendations for airfield design, operating scenarios and technologies, and better training for human operators in the system. The framework is recommended for the analysis of runway incursions to support safety improvements and the methodology is transferable to other areas of aviation safety risk analysis. [ABSTRACT FROM AUTHOR]

Abstract: Motor vehicle crashes result in millions of injuries and thousands of deaths each year in the United States. While most crash research datasets use Abbreviated Injury Scale (AIS) codes to identify injuries, most hospital datasets use the International Classification of Diseases, version 9 (ICD-9) codes. The objective of this research was to establish a one-to-one mapping between AIS and ICD-9 codes for use with motor vehicle crash injury research. This paper presents results from investigating different mapping approaches using the most common AIS 2+ injuries from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS). The mapping approaches were generated from the National Trauma Data Bank (NTDB) (428,637 code pairs), ICDMAP (2500 code pairs), and the Crash Injury Research and Engineering Network (CIREN) (4125 code pairs). Each approach may pair given AIS code with more than one ICD-9 code (mean number of pairs per AIS code: NTDB=211, ICDMAP=7, CIREN=5), and some of the potential pairs are unrelated. The mappings were evaluated using two comparative metrics coupled with qualitative inspection by an expert physician. Based on the number of false mappings and correct pairs, the best mapping was derived from CIREN. AIS and ICD-9 codes in CIREN are both manually coded, leading to more proper mappings between the two. Using the mapping presented herein, data from crash and hospital datasets can be used together to better understand and prevent motor vehicle crash injuries in the future. [Copyright &y& Elsevier]

Abstract: The speed limit of 55mph (88km/h) is typically used on rural highways in the U.S. For locations where curbs are installed along these roadways, some transportation agencies have suggested the use of a lower 45mph (72km/h) speed limit because, according to AASHTO, running into curbs at high speeds may cause significant vehicular damage and even severe injuries. However, it has also been argued that lowering the speed limit after the installation of curbs may cause confusion in drivers, who do not perceive the risk associated with the newly installed curbs and tend to operate their vehicles at the same speed as before. To better understand driver behavior on rural highways before and after curb installation, and with different speed limits, researchers at the University of Tennessee conducted a series of experiments in two-lane and four-lane highways on a high-fidelity driving simulator. This paper mainly presents the findings from the four-lane study, and compares the results from the previous two-lane study. The scenario matrix consists of several dimensions including posted speed limit (45 and 55mph, or 72 and 88km/h), curb installation, lateral clearance between the edge of travel lane and the curb (2ft, 6ft, and no-curb, or 0.6m, 1.8m, and no-curb), weather (clear and fog), traffic conditions in the next lane (1400veh/h and 400veh/h), etc. For each subject under different experimental scenarios, detailed driving parameters, such as driving speed and vehicle position in the travel lane, were recorded and analyzed subsequently. Results of the study suggest that driver behaviors are influenced by the various factors in a complex and interrelated manner. It is likely that drivers do not perceive the risk from the curb in determining their speed on four-lane rural highways. However, it is found that curbs may provide certain guidance to drivers, especially in selecting lane position. Compared to the previous research in two-lane conditions, it is found that drivers are more likely to choose driving speeds according to posted speed limits, rather than lane configurations. It is also found that the relative speed between driver''s vehicle and ambient traffic or curbs is an important factor determining drivers’ perception of risk and thus their driving behavior. The influence of subjective effects of these factors to their driving behavior is also observed in the study. [Copyright &y& Elsevier]

Abstract: Animal–Vehicle Collisions (AVCs) have been a major safety problem in the United States over the past decades. Counter measures against AVCs are urgently needed for traffic safety and wildlife conservation. To better understand the AVCs, a variety of data analysis and statistical modeling techniques have been developed. However, these existing models seldom take human factors and animal attributes into account. This paper presents a new probability model which explicitly formulates the interactions between animals and drivers to better capture the relationship among drivers’ and animals’ attributes, roadway and environmental factors, and AVCs. Findings of this study show that speed limit, rural versus urban, and presence of white-tailed deer habitat have an increasing effect on AVC risk, whereas male animals, high truck percentage, and large number of lanes put a decreasing effect on AVC probability. [Copyright &y& Elsevier]

Abstract: The effectiveness of safety inspections of ships has been analysed from various angles, but until now, relatively little attention has been given to translate risk reduction into incident cost savings. This paper provides a monetary quantification of the cost savings that can be attributed to port state control inspections and industry vetting inspections. The dataset consists of more than half a million ship arrivals between 2002 and 2007 and contains inspections of port state authorities in the USA and Australia and of three industry vetting regimes. The effect of inspections in reducing the risk of total loss accidents is estimated by means of duration models, in terms of the gained probability of survival. The monetary benefit of port state control inspections is estimated to range, on average, from about 70 to 190 thousand dollars, with median values ranging from about 20 to 45 thousand dollars. Industry inspections have even higher benefits, especially for tankers. The savings are in general higher for older and larger vessels, and also for vessels with undefined flag and unknown classification society. As inspection costs are relatively low in comparison to potential cost savings, the results underline the importance of determining ships with relatively high risk of total loss. [Copyright &y& Elsevier]

Abstract: Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and power of each model fit. This study confirms that falls count distributions are over-dispersed, but not dispersed due to excess zero counts or heterogeneous population. Accordingly, the P model generally provided the poorest fit to all datasets. The fit improved significantly with NB and both zero-inflated models. The fit was also improved with the NB model, compared to finite mixtures of both P and NB regression models. Although there was little difference in fit between NB and ZINB models, in the interests of parsimony it is recommended that future studies involving modelling of falls count data routinely use the NB models in preference to the P or ZINB or finite mixture distribution. The fact that these conclusions apply across four separate datasets from four different samples of older people participating in studies of different methodology, adds strength to this general guiding principle. [Copyright &y& Elsevier]

Abstract: The objective of the study on which the paper is based was to explore the application of fully Bayesian methods for before–after road safety studies. Several variations of the methodology were evaluated with a simulated dataset in which hypothetical treatments with no safety effect were randomly assigned to high accident locations to mimic the common site selection process in road jurisdictions. It was confirmed that the fully Bayesian method by estimating no safety effect can account for the regression-to-the-mean that results from this biased site selection process. The fully Bayesian method was then applied to California rural intersection data to evaluate the safety effect of conversion from stop to signalized control. The results were then compared with those from the empirical Bayesian method, currently the accepted approach for conducting unbiased before–after evaluations. This comparison was generally favorable in that FB can provide similar results as EB. [Copyright &y& Elsevier]

Abstract: Crash rates are used to establish the relative safety of various variables of concern such as driver classes, vehicle types and roadway components. Appropriate exposure data for estimating crash rates is critical but crash databases do not contain information on driver or vehicle exposure. The quasi-induced exposure method, which uses not-at-fault driver/vehicle data as an exposure metric, is a technique used in order to overcome this problem. The basic assumption made here is that not-at-fault drivers represent the total population in question. This paper examines the validity of this assumption using the Kentucky crash database to define two samples of not-at-fault drivers. One sample included only not-at-fault drivers selected from the first two vehicles in a multi-vehicle crash (two or more vehicles involved) while the other included the not-at-fault drivers from multi-vehicle crashes with more than two vehicles involved and excluding the first two drivers. The assumption is that the randomness of the involvement of drivers in the second sample is more reasonable than the drivers in the first two vehicles involved in crashes. The results indicate that these two samples are similar; there is no statistical evidence demonstrating that both samples represent two different populations in the maneuvers and other variables/factors examined here; and they are representative simple random samples of the driver population with respect to the distribution of the driver age when there is no reasonable doubt about investigating officers’ judgments. Thus, estimating relative crash propensities for any given driver type by using the quasi-induced exposure approach will yield reasonable estimates of exposure. [Copyright &y& Elsevier]

• The conventional approaches of evaluating traffic laws produce unreliable results. • The contiguous-counties design yields robust estimates of various traffic laws. • DUI penalties that restrict future vehicle use reduce traffic fatalities. • Open container and zero-tolerance laws reduce alcohol-induced fatal crashes. In the United States, about 28 lives are lost daily in motor vehicle accidents that involve an alcohol-impaired driver. While most states have enacted various traffic laws to address this phenomenon, little consensus exists on the causal impact of these laws in reducing alcohol-induced fatalities. This paper exploits quasi-random variation in state-level laws to estimate the causal effect of alcohol-related traffic laws on the frequency of fatal accidents. This is identified from the discontinuities in traffic laws among contiguous counties that are separated by a shared state border. We present robust evidence that the conventional approaches that are typically utilized in the literature may erroneously estimate the effectiveness of several alcohol-related laws. [ABSTRACT FROM AUTHOR]

• The paper explores the safety effects of intelligent connected vehicles by penetration rate. • Results from previous 139 analyses are consolidated using meta-analysis. • Safety is enhanced by 4% with the penetration of 10%, and by 43% with 90% MPR. • A case study in the United States is conducted for evaluating crash reduction. • About 13% of crashes can be reduced by intelligent connected vehicles in 2035. As the era of intelligent connected vehicles (ICVs) is approaching, a number of studies have investigated the potential benefits of ICVs, including the safety effects. Although previous studies agree that ICVs would significantly improve traffic safety, its quantified safety effects at different stages are still being debated. This study aims to estimate the ICVs' safety effects by market penetration rate (MPR) adopting a meta-analysis approach. The results from the meta-analysis indicate that the number of conflicts is exponentially reduced as the MPR goes up. For example, compared to the environment without ICVs, 4.2% and 17.4% of conflicts would decrease at the MPR of 10% and 50%, respectively. The effects are more obvious at higher MPR—43.4% of conflicts are expected to decrease at the MPR of 90%. From the case study in the United States based on the meta-analysis, it is expected that the MPR would reach 17–20% in the near future (2025) and 40–48% in 2035. The anticipated reduction in the number of fatal crashes would be 5% and 13%, in 2025 and 2035, respectively. The findings from this study will be useful for both public and private sectors to establish strategic plans to promote ICVs and identify their benefits at different MPRs. [ABSTRACT FROM AUTHOR]

Abstract: This paper documents the application of the Conway–Maxwell–Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model. [Copyright &y& Elsevier]

Abstract: This paper examines rates of self-reported driving under the influence (DUI) of alcohol and 12-month and lifetime DUI arrest rates among Mexican Americans, Puerto Ricans, Cuban Americans and South/Central Americans in the U.S. population. Using a multistage cluster sample design, a total of 5224 individuals 18 years of age and older were selected from the household population in five metropolitan areas of the U.S.: Miami, New York, Philadelphia, Houston and Los Angeles. The survey weighted response rate was 76%. Among men, 21% of Mexican Americans, 19.9% of South/Central Americans, 11.6% of Puerto Ricans and 6.9% of Cuban Americans reported DUI. Rates were lower among women, ranging from 9.7% for Mexican Americans to 1.3% for Cuban Americans. Mexican American men had the highest 12-month arrest rate (1.6%) and the highest lifetime arrest rate (11.2%). Drinkers who reported DUI were heavier drinkers than those not reporting DUI according to a variety of indicators. However, most DUI incidents involved non-alcohol-dependent drivers. Mexican Americans and South Central/Americans, men, younger drivers, those with less than high school education, those with higher income and higher alcohol consumption were more likely to report DUI and DUI arrests. These findings show that Hispanic national groups in the U.S. are diverse regarding drinking and DUI-related experiences. [Copyright &y& Elsevier]

Abstract: Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)—which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters—while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming. [Copyright &y& Elsevier]

Abstract: Few studies have evaluated the impact of workplace substance abuse prevention programs on occupational injury, despite this being a justification for these programs. This paper estimates the effectiveness and benefit-cost ratio of a peer-based substance abuse prevention program at a U.S. transportation company, implemented in phases from 1988 to 1990. The program focuses on changing workplace attitudes toward on-the-job substance use in addition to training workers to recognize and intervene with coworkers who have a problem. The program was strengthened by federally mandated random drug and alcohol testing (implemented, respectively, in 1990 and 1994). With time-series analysis, we analyzed the association of monthly injury rates and costs with phased program implementation, controlling for industry injury trend. The combination of the peer-based program and testing was associated with an approximate one-third reduction in injury rate, avoiding an estimated $48 million in employer costs in 1999. That year, the peer-based program cost the company $35 and testing cost another $35 per employee. The program avoided an estimated $1850 in employer injury costs per employee in 1999, corresponding to a benefit-cost ratio of 26:1. The findings suggest that peer-based programs buttressed by random testing can be cost-effective in the workplace. [Copyright &y& Elsevier]

Abstract: This paper presents a synthesis of evidence from studies that have evaluated the impacts of economic deregulation on transport safety. Most of these studies refer to aviation or road transport. Very few studies deal with deregulation of rail transport. There are no studies of maritime transport, which has never been regulated the same way as other modes of transport. The review includes studies that have attempted to quantify the impacts of transport deregulation on transport safety. Each study contains one or more estimates of the effect on transport safety of deregulation. Summary estimates of effect have been derived from the individual estimates of effect by means of meta-analysis. Airline deregulation, which has only been evaluated in the United States, does not appear to influence the safety of air travel. Deregulation of road transport has been evaluated in several countries. The summary estimate of effect indicates that no statistically significant changes in road safety have occurred as a result of deregulation. Deregulation of rail transport has only been evaluated in Great Britain and the United States. The experience so far suggests that deregulation of railways is associated with improved rail safety. This association does, however, not necessarily imply a causal relationship. [Copyright &y& Elsevier]

Abstract: Main objectives: This study was conducted to estimate the costs per crash for three police-coded crash severity groupings within 16 selected crash geometry types and within two speed limit categories (≤45 and ≥50mph). Methods: We merged previously developed costs per victim by abbreviated injury scale (AIS) score into U.S. crash data files that scored injuries in both the AIS and police-coded severity scales to estimate injury costs, then aggregated the estimates into costs per crash by maximum injury severity. Results: The most costly crashes were non-intersection fatal/disabling injury crashes on a road with a speed limit of 50 miles per hour or higher where multiple vehicles crashed head-on or a single vehicle struck a human (over $1.69 and $1.16 million per crash, respectively). The annual cost of police-reported run-off-road collisions, which include both rollovers and object impacts, represented 34% of total costs. Conclusions: This paper provides cost estimates useful for evaluating roadway countermeasures and for designing vehicles to minimize crash harm. It gives unit costs of crashes by type in the coding system used by the police. The costs are in an appropriate form for economic analysis of countermeasures addressing locally defined problems identified by analyzing police crash reports. [Copyright &y& Elsevier]

Abstract: Improvements in rail–highway grade crossing safety have resulted from engineering, law enforcement, and educating the public about the risks and the actions they should take. The primary form of the latter is a campaign called Operation Lifesaver which started in the 1970s. This paper uses a negative binomial regression to estimate whether variations in Operation Lifesaver activity across states and from year-to-year in individual states are related to the number of collisions and fatalities at crossings. Annual data on the experience in 46 states from 1996 to 2002 are used. The analysis finds that increasing the amount of educational activity will reduce the number of collisions with a point elasticity of −0.11, but the effect on the number of deaths cannot be concluded with statistical certainty. [Copyright &y& Elsevier]

Purpose: This paper investigates the relationship between precipitation and traffic crashes in the US during the period 1975–2000. Traffic crashes represent the leading cause of death and injury for young adults in the US, and the ninth leading cause of death for the overall population. Prior studies have found that precipitation raises the risk of traffic crashes significantly.Methods: A negative binomial regression approach is employed. Two different units of analysis are examined: state–months and state–days. The sample includes all 48 contiguous states.Results: A surprising negative and significant relationship between monthly precipitation and monthly fatal crashes is found. However, in the daily level analysis, a strong positive relationship is estimated, as in prior studies. The source of the contrasting results appears to be a substantial negative lagged effect of precipitation across days within a state–month. In other words, if it rained a lot yesterday, then on average, today there are fewer crashes. Additional analysis shows that the risk imposed by precipitation increases dramatically as the time since last precipitation increases. For example, 1 cm of precipitation increases the fatal crash rate for a state–day by about 3% if exactly 2 days have passed since the last precipitation and by about 9% if more than 20 days have passed. This basic pattern holds for non-fatal crashes as well.Conclusions: The lagged effects of precipitation across days may be explained by the clearing of oil that accumulates on roads during dry periods or by the conditioning of people to drive more safely in wet conditions. Either way, policy interventions that prepare drivers more adequately for the risks of precipitation following dry periods are likely to be beneficial. [Copyright &y& Elsevier]

Although the rapid growth in light truck vehicle (LTV) sales, including minivans, sports utility vehicles (SUVs), and light-duty trucks, has not been associated with an overall increase in collisions or traffic deaths in the US, there is a need for a research program to determine whether particular types of collisions have become more frequent or injurious because of the increase in the percent of LTVs in traffic. This paper presents an analysis of the effect of the increasing number of LTV registrations on fatal angle collision trends in the US. The analysis investigates the number of annual fatalities that result from angle collisions as well as collision configuration (car–car, car–LTV, LTV–car, and LTV–LTV). The analysis uses the Fatality Analysis Reporting System (FARS) crash databases covering the period 1975–2000. Results showed that death rates differ based on the collision configuration. Time series modeling results showed that fatalities in angle collisions will increase in the next 10 years, and that they are affected by the expected increase in the percentage of LTVs in traffic. Forecast showed that the total number of annual deaths is expected to reach 6300 deaths by the year 2010 (an increase of 12% over 2000). Analysis into the configuration of the collision indicated the seriousness of angle collisions involving an LTV striking a common passenger car (LTV–car). A time series model illustrated the significance of time lag and percent of LTVs in traffic on the increase of this type of fatal collisions. Forecasts from the time series model indicated a 32% increase in deaths due to this type of collisions in the next 10 years. [Copyright &y& Elsevier]

The characteristics of crash-involved seat belt non-users in a high use state (Hawaii) are examined in order to better design enforcement and education programs. Using police crash report data over a 10-year period (1986–1995), we compare belted and unbelted drivers and front seat occupants, who were seriously injured in crashes, in terms of personal (age, gender, alcohol involvement, etc.) and crash characteristics (time, location, roadway factors, etc.). A logistic regression model combined with the spline method is used to analyze and categorize the salient differences between users and non-users. We find that unbelted occupants are more likely to be male, younger, unlicensed, intoxicated and driving pickup trucks versus other vehicles. Moreover, non-users are more likely than users to be involved in speed-related crashes in rural areas during the nighttime. Passengers are 70 times more likely to be unbelted if the driver is also unbelted than passengers of vehicles with belted drivers. While our general findings are similar to other seat belt studies, the contribution of this paper is in terms of a deeper understanding of the relative importance of various factors associated with non-use among seriously injured occupants as well as demonstrating a powerful methodology for analyzing safety problems entailing the categorization of various groups. While the former has implication for seat belt enforcement and education programs, the latter is relevant to a host of other research questions. [Copyright &y& Elsevier]

• We analyzed 143 near-side crash occupants in the United States from 2010–2015. • Poor performance in US side impact crash tests was linked to poor injury outcomes. • Delta-V, age, sex, BMI, and side impact crash test performance all affect injury. The objective of this paper was to develop an injury risk model relating real world injury outcomes in near-side crashes with U.S. New Car Assessment Program (NCAP) test performance, crash, and occupant properties. The study was motivated by the longer-term goal of predicting injury outcomes in a future fleet in which all vehicles are expected to have passive safety performance equivalent to a 5-star NCAP rating level (the highest star rating and lowest risk of injury). The dataset used to evaluate injury risk was the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). Case years 2010–2015 were used. An injured occupant was defined as a vehicle occupant who experienced an injury of maximum Abbreviated Injury Scale (AIS) of 2 or greater, or who were fatally injured. Injury severity was scored using AIS-2005 (2008 update). Cases were selected in which front-row occupants of late-model vehicles were exposed to a near-side crash. Logistic regression was used to develop an injury model with delta-v, belt status, age, and gender as predictor variables. The side crash performance of each vehicle was identified and added to the model by matching each case with the associated performance in the NCAP moving deformable barrier side impact crash test. NCAP MDB test performance, delta-v, and occupant age, sex, and BMI were found to be significant predictors of injury risk. The effect of a 5 % higher risk in the MDB test (approximately one star rating worse) was comparable to a 2.84 km/h increase in delta-v. This model informs the development of active safety systems in a future fleet where vehicle passive safety performance is higher, quantified by the NCAP MDB test. [ABSTRACT FROM AUTHOR]

• Developing a framework to quantify instability in driving prior to crash occurrence. • Exploring the association of driving instability, in terms of volatility, with crash intensity. • Investigating the correlation of behavioral and environmental factors with driving volatility. • Higher instability in driving increase the probability of a severe crash. • Distracted and aggressive driving increase instability in driving. While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed. [ABSTRACT FROM AUTHOR]