Motor vehicle crash (MVC) and its associated injuries remain a major public health problem world wide. In 2005 alone there were 6 million police-reported crashes in the United States resulting in 2.5 million injuries and 46,000 fatalities. The thorax is second only to the head in terms of frequency of injury following MVC, and pulmonary contusion (PC) is the most common intra-thoracic soft tissue injury sustained as a result of blunt chest trauma. The goal of this dissertation research is to mitigate this commonly-sustained and potentially life threatening injury. We have taken a computational approach to solving this problem by developing a predictive injury metric for PC using finite element analysis (FEA). The dissertation begins with an epidemiological examination of the crash modes, vehicles, and patient demographics most commonly associated with PC. This study was conducted using real world crash data from the Crash Injury Research and Engineering Network (CIREN) database and data from government-sponsored vehicle crash tests. The CIREN data showed that a substantial portion of the crashes resulting in PC were lateral impacts (48%). Analysis of the thoracic loading of dummy occupants in lateral crash tests resulted in mean values of medial-lateral chest compression and deflection velocity of 25.3 ± 2.6 % and 4.6 ± 0.42 m·s-1 respectively. These data provided quantified loading conditions associated with crash-induced PC and a framework for the remaining research studies, which were focused on blunt impact experiments examining the relationship between insult and outcome in a living model of this injury. A combined experimental and computational approach was used to develop injury metrics for PC. The animal model selected for this research was the Sprague-Dawley male rat. In the remaining studies that comprise this dissertation, an outcome measure of the inflammatory response in the lung parenchyma was correlated with a mechanical analog calculated via a finite element model of the lung. For all studies, a precise and instrumented electronic piston was used to apply prescribed insults directly to the lungs of the subjects. In the first set of experiments, contusion volume was calculated from MicroPET (Micro Positron Emission Tomography) scans and normalized on the basis of liver uptake of 18F-FDG. The subjects were scanned at 24 hours, 7 days, and 28 days (15 scans), and the contused volume was measured. A tentative criteria based on first principal strain in the parenchyma between 9 and 36% was established. In subsequent experiments Computed Tomography was used to acquire volumetric contusion data. The second set of experiments introduced two important aspects of this dissertation; a semi-automated algorithm for CT segmentation and a technique to match the spatial distribution of contusion within the lung to finite element analysis results. The results of this study indicated that the product of first principal strain and strain rate is the most appropriate output variable upon which to base an injury metric for PC. Digital analysis of histology from study subjects that underwent CT scanning prior to sacrifice was conducted and showed good agreement between CT and histology. A final set of experiments was conducted to synthesize the techniques developed in previous studies to determine an injury metric for PC. A concurrent optimization technique was applied to the FEA model to match force vs. deflection traces from four distinct impact cohorts. The resulting predictive injury metrics for PC were exceeding 94.5 sec-1, first principal strain exceeding 0.284 (true strain, dimensionless), and first principal strain rate exceeding 470 sec-1. The method used in this dissertation and the resulting injury metrics for PC are based on quantified inflammatory response observed in a living model, specifically in the organ of interest. This injury metric improves upon current thoracic injury criteria that rely on gross measures of chest loading such as acceleration, or deflection, and are not specific to a particular injury. We anticipate that the findings of this work will lead to more data-driven improvements to vehicular safety systems and ultimately diminish the instance of PC and mitigate its severity. / Ph. D.

When a vehicle changes speed or direction, the interaction between the contacting surfaces of the tire and the pavement form frictional forces. The pavement's contribution to tire-pavement friction is referred to as skid resistance and is provided by pavement microtexture and macrotexture. The amount of skid resistance depreciates over time due to the polishing action of traffic, and for this reason, the skid resistance should be monitored with friction testing equipment. The equipment use one of four test methods to measure network-level friction: ASTM E 274 locked-wheel, ASTM E 2340 fixed-slip technique, ASTM E 1859 variable-slip technique, and sideways-force coefficient (SFC) technique. The fixed-slip, variable-slip, and SFC techniques are used in continuous friction measurement equipment (CFME). In the United States, skid resistance is traditionally measured with a locked-wheel skid trailer (LWST) equipped with either a ASTM E 501 ribbed or a ASTM E 524 smooth 'no tread' tire. Since the LWST fully-locks the test wheel to measure friction, it is only capable of spot testing tangent sections of roadway. By contrast, the remaining three test methods never lock their test wheels and, therefore, they can collect friction measurements continuously on all types of roadway, including curves and t-intersections. For this reason, highway agencies in the U.S. are interested in transitioning from using a LWST to using one of three continuous methods. This dissertation explores the use of continuous friction measurements, collected with a Sideways-force Coefficient Routine Investigation Machine (SCRIM), in a systemic highway safety management approach to reduce crashes that result in fatalities, injuries, and property damage only. The dissertation presents four manuscripts. In the first manuscript, orthogonal regression is used to develop models for converting between friction measurements with a SCRIM and LWST with both a ribbed and smooth tire. The results indicated that the LWST smooth tire measured friction with greater sensitivity to changes in macrotexture than the SCRIM and LWST ribbed tire. The SCRIM also had greater correlation to the LWST ribbed tire than the LWST smooth tire. The second investigation establishes the relationship between friction measured with a SCRIM and the risk of crashes on dry and wet pavement surfaces. The results of this showed that increasing friction decreases both dry and wet pavement crashes; however, friction was found to have greater impact in wet conditions. Due to the negative relationship between friction and crashes, eventually there will be a point where further losses in friction can result in a rapid increase in crash risk. This point can be identified with a friction threshold known as an investigatory level. When measured friction is at or below the investigatory level, an in- and out-of-field investigation is required to determine whether a countermeasure is necessary to improve safety. The third manuscript proposes a statistical regression approach for determining investigatory levels. Since this approach relies on statistical regression, the results are objective and should be the same for any analyst reviewing the same data. The investigatory levels can be used in a systemic approach that identifies locations where crashes can be reduced based on a benefit-cost analysis of surface treatments. Last, the forth manuscript demonstrates a benefit-cost analysis that selects surface treatments based on crash reductions predicted with continuous friction measurements. / Doctor of Philosophy / When a vehicle changes speed or direction, the tires slide over the pavement surface, creating friction that produces the traction that is necessary for the vehicle to change speed or direction. Friction can diminish when water, dust, and other contaminants are present, or over time due to traffic. Over time, the loss in friction causes the risk of a crash to increase. However, this relationship is non-linear, and therefore, eventually there will be a point where further losses in friction can cause a rapid increase in crash risk. For this reason, the pavement friction is monitored with equipment that slides a rubber tire with known properties over a pavement surface. Since friction is lowest when the pavement is wet, the equipment applies a film of water to the surface directly in front of the sliding tire. There are different types of equipment used to measure friction. The physical designs of the equipment and their method of testing may be different. For example, some devices measure friction by sliding a wheel that is angled away from the path of the vehicle, while others slide a wheel that is aligned with the vehicle but reduced in speed compared to the vehicle. The factors that make the equipment different can affect the quantity of friction that is measured, as well as the timing between each consecutive measurement. The advantages that some equipment offers can entice highway agencies to transition from a pre-existing system to a more advantageous system. Before transitioning, the measurements from the two types of equipment should be compared directly to determine their correlation. Statistical regression can also be used to develop models for converting the measurements from the new equipment to the units of the current, which can help engineers interpret the measurements, and to integrate them into an existing database. The presence of water on a pavement surface can result in a temporary loss of friction that can increase the risk of a crash beyond the normal, dry pavement state. This does not guarantee that dry pavements have sufficient friction as is suggested in most literature. In this dissertation, the relationship between friction and the risk of a crash on dry and wet pavements are evaluated together. The results show that increasing friction can decrease the crash risk on both dry and wet pavement surfaces. The amount of friction that is needed to maintain low crash risk is not the same for every section of road. Locations such as approaches to curves or intersections can increase the risk of a crash, and for that reason, some sections of roadway require more friction than others. Minimum levels of friction called investigatory levels can be established to trigger an in- and out-of-field investigation to determine whether improving friction can improve safety when the measured friction is at or below a specific value. This dissertation proposes a methodology for determining the investigatory levels of friction for different sections of roadway using a statistical regression approach. The investigatory levels are then used to identify locations where pavement surface treatments can reduce crashes based on a benefit-cost analysis. Last, the ability of a surface treatment to reduce crashes is evaluated using another statistical regression approach that predicts changes in crash risk using friction measurements. Since there are several treatment options, a treatment is selected based on estimated cost and benefit.

Crash data suggests that intersections are areas producing conflicts among the various road users because of entering and crossing movements. Traffic signal control systems may not always be sufficient in preventing collisions at intersections between emergency and other vehicles. The Firefighter Fatality Retrospective Study of 2002 illustrates that the second leading cause of fatal injury for firefighters is vehicle collisions. Furthermore, the involvement of an emergency vehicle in a crash can negatively affect the overall efficiency of emergency response services. Thus, there is a need to facilitate the implementation of higher-payoff strategies to improve the safety of emergency vehicle passage through signalized intersections. This research aims to provide a basis for the transportation professionals to identify problem areas and take measures that will potentially enhance intersection safety for emergency vehicles. It includes the presentation and comparison of the EV crash situation in Northern Virginia. The results indicate that 49% of all EV accidents along U.S. Highways in Northern Virginia occurred at signalized intersections. This percentage is 75% along U.S. Highways in Fairfax County, the largest county in Northern Virginia, and it is 79% along U.S. 1 in Fairfax County. The analysis, also, illustrates that the major collision type at signalized intersections was of the angle type, which suggests that an appropriate warning sign may be absent. These findings enhance our understanding of emergency vehicle crash characteristics and thus, may facilitate the identification of possible warrants to be used in determining the appropriateness of installing signal preemption equipment at signalized intersections. / Master of Science

Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts. / Doctor of Philosophy / Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts.

Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses. / Master of Science / Fixed-object crashes happen when a single vehicle strikes a roadway feature such as a curb or a median, or runs off the road and hits a roadside feature such as a tree or utility pole. They have long time been considered as major highway safety concerns due to their high frequency, fatality rate, and associated property cost. Previous studies relevant to fixed-object crashes tended to address such crashes in the contexture of roadway departures, and heavily relied on police-reported accident data. However, many fixed-object crashes involved objects in roadway such as traffic control devices, roadway debris, etc. The police-reported accident data were found to be weak in depicting scenarios prior to, during crashes. Also, many minor crashes were often kept unreported. The Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) is the largest NDS project launched across the country till now, aimed to study driver behavior or, performance-related safety problems under real-world scenarios. The data acquisition systems (DASs) equipped on participated vehicles collect vehicle kinematics, roadway, traffic, environment, and driver behavior data continuously, which enable researchers to address such crash scenarios closely. This study integrated SHRP2 NDS and roadway information database (RID) data to conduct a comprehensive analysis of fixed-object crashes. A total of 1,639 crash, near-crash events relevant to fixed objects and animals, and 1,050 baseline events were used. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. The logistic regression analyses identified 16 and 10 variables with significance levels of 0.1 for fixed-object event occurrence and severity level models respectively. The influence of explanatory variables was discussed in detail. SVM classifiers and ANN models were also constructed to predict the fixed-object crash occurrence and severity level. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods achieved satisfactory prediction accuracies of around 88% for crash occurrence prediction and 75% for crash severity level prediction, which suggested the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.

<p dir="ltr">Reliability against errors, such as soft errors—transient bit flips in transistors caused by energetic particle strikes—and crash inconsistency arising from power failure, is as crucial as performance and power efficiency for a wide range of computing devices, from embedded systems to datacenters. If not properly addressed, these errors can lead to massive financial losses and even endanger human lives. Furthermore, the dynamic nature of modern computing workloads complicates the implementation of reliable systems as the likelihood and impact of these errors increase. Consequently, system designers often face a dilemma: sacrificing reliability for performance and cost-effectiveness or incurring high manufacturing and/or run-time costs to maintain high system dependability. This trade-off can result in reduced availability and increased vulnerability to errors when reliability is not prioritized or escalated costs when it is.</p><p dir="ltr">In light of this, this dissertation, for the first time, demonstrates that with a synergistic compiler and architecture co-design, it is possible to achieve reliability while maintaining high performance and low hardware cost. We begin by illustrating how compiler/architecture co-design achieves near-zero-overhead soft error resilience for embedded cores (Chapter 2). Next, we introduce ReplayCache (Chapter 3), a software-only approach that ensures crash consistency for energy harvesting systems (backed with embedded cores) and outperforms the state-of-the-art by 9x. Apart from embedded cores, reliability for server-class cores is more vital due to their widespread adoption in performance-critical environments. With that in mind, we then propose VeriPipe (Chapter 4), which showcases how a straightforward microarchitectural technique can achieve near-zero-overhead soft error resilience for server-class cores with a storage overhead of just three registers and one countdown timer. Finally, we present two approaches to achieving performant crash consistency for server-class cores by leveraging existing dynamic register renaming in out-of-order cores (Chapter 5) and repurposing Intel’s non-temporal path (Chapter 6), respectively. Through these innovations, this dissertation paves the way for more reliable and efficient computing systems, ensuring that reliability does not come at the cost of performance degradation or hardware complexity.</p>

Yes / This study examines the effect of carbon price uncertainty on stock price crash risk. Utilizing the dynamic panel model on the data of Chinese listed firms from 2011 to 2018, we find that high carbon price uncertainty increases stock price crash risk. The impact of carbon price uncertainty is more prominent in the heavily polluting industries and during the post-period of the Paris agreement. The two underlying channels through which carbon price uncertainty induces stock price crashes are managers' hoarding of bad news and investors' heterogeneity. Furthermore, reducing information asymmetry inside and outside the firms can mitigate the influence of carbon price uncertainty on stock price crash risk. Our findings demonstrate that carbon price uncertainty as a newly underexplored factor induced by the prevailing curb of catastrophe risks has unintended but important implications on stock prices. / This study was supported by the Project of Social Science Achievement Evaluation Committee of Hunan Province (Grant No. XSP22YBZ160), Hunan Provincial Natural Science Foundation of China (Grant No. 2022JJ40644 and No. 2022JJ40647). / The full-text of this article will be released for public view at the end of the publisher embargo on 24th Oct 2024.

The purpose of this study is to develop and analyze the possibilities of implementing a new crash test method for child bike trailers based on StVZO §67 Explanation 19 Appendix 2 at Thule Test Center or use an alternative method. The study is based on the following three problem statements: can a new method based on StVZO §67 Explanation 19 Appendix 2 replace TÜV and RISE test methods? Is it possible to implement a new test method in Thule Crash Lab using the acceleration sled track or using a different approach? How can crash testing contribute to optimizing the development of child bike trailers? This study began with background information and the problem description of Thule’s lack of test method and their need for a new alternative. The theorical background provides information about testing for product development. It also provides information about StVZO §67 Explanation 19 Appendix 2 which will be the basis for the study and the previous test methods Thule has used, which were RISE and TÜV.  Observations, analyzes and an interview from the previous test methods established the foundation for the concept study which included brainstorming sessions to generate concepts for the new test method. The concepts consisted both of methods with and without the acceleration sled track. Several workshops served as concept selections which filtered out concepts that exceeded limitations of the facilities at Thule Test Center. The concepts that could not fulfill the requirements from StVZO §67 Explanation 19 Appendix 2 was also filtered out.  To find out if the acceleration sled could be used as a test method, different tests were involved which included evaluation of test impulses and crash tests with handmade fixtures for the child bike trailer. This was carried out to ensure that the crash sled could operate under the variables that was calculated in preparation for the test and that the fixtures could handle the accelerations.  The study also included discussions and conclusions with suggestions for modifications to the concepts that could not be directly implemented due to the limitations in Thule facilities. Some of the modifications were also taken up as suggestions for further research. The study also included comparison between internal and external testing. The comparison explained the importance for Thule to have an internal test method and generally an alternative for crash testing their child bike trailers. As presented in the study, crash testing is crucial for an optimized development of new child bike trailers to ensure safe, qualitative and durable child bike trailers. It is also crucial to stay competitive in the market. To validate stresses and forces subjected onto the construction of the final test method, static calculations were performed which gave an indication of what dimensions the beams for the construction could have. As discussed, dynamic calculations could have been done to achieve a more precise and accurate result.  The final test method is an example of what Thule could use for crash testing their child bike trailers with the requirements from StVZO §67 Explanation 19 Appendix 2. Design changes are possible if it were to be implemented and the new test method gives Thule an internally option.

Speeding is a major concern on all roadways and is a leading factor in traffic fatalities and serious injuries. Rural roadways are often disproportionately impacted by these traffic crashes and fatalities, despite the lower traffic volumes and populations. It is important to address this speeding issue, especially in rural areas, which can be done with an organized plan, such as a Speed Management Action Plan (SMAP), and collaboration with all parties involved. The goal of this research is to provide a framework to help rural areas identify locations that are at higher risk of speeding-related crashes by analyzing roadway characteristics that have a higher likelihood of a speeding-related crash to occur and which characteristics have a larger proportional influence associated with them. Identifying these roadway characteristics can help focus state crash analysis or countermeasure implementation to ensure that locations that are at highest risk of speeding-related crashes are receiving appropriate and effective speed management countermeasures. The framework identifies roadway characteristics that are more likely to contribute to speeding-related crashes, focusing on rural, non-interstate, and non-intersection roads. It underscores the importance of data-driven decision-making to prioritize high-risk locations and optimize resource allocation. By providing states with tools and information, the framework facilitates the identification of critical factors influencing speeding-related crashes, such as roadway alignment, surface conditions, and lighting. Additionally, it provides comprehensive guidance on data collection, data filtering, key characteristics to identify, data analysis, prioritizing findings, applying the results, and monitoring the implementations. This structured approach not only supports the effective use of crash data for informed decision-making but also aligns with the development and execution of SMAPs. The research utilized Virginia Department of Motor Vehicles Traffic Records Electronic Data System (TREDS) crash data for the year 2022 to create the framework. The roadway characteristics included in the analysis were determined using engineering judgement and past studies. Those characteristics were identified from the attributes: location of the first harmful event, light conditions, roadway alignment, roadway description, roadway defects, and roadway surface conditions. A proportional analysis method was used to calculate the speeding-related crash likelihood percentage and the systemic impact percentage for each characteristic included in the analysis. Key findings from this analysis revealed certain roadway characteristics, such as roadside, darkness, curved, two-way not divided, and wet surfaces that had high impacts on both the likelihood of a speeding-related crash occurring and high systemic impact. Virginia crash data was used in this study to test the framework developed to show its effectiveness and how it can be utilized by other states. / Master of Science / Speeding is a major concern on all roadways and is a leading factor in traffic fatalities and serious injuries. Rural roadways are often disproportionately impacted by these traffic crashes and fatalities, despite the lower traffic volumes and populations. It is important to address this speeding issue, especially in rural areas, which can be done with an organized plan, such as a Speed Management Action Plan (SMAP), and collaboration with all parties involved. The goal of this research is to provide a framework to help rural areas identify locations that are at higher risk of speeding-related crashes by analyzing roadway characteristics that have a higher likelihood of a speeding-related crash to occur and which characteristics have a larger proportional influence associated with them. Identifying these roadway characteristics can help focus state crash analysis or countermeasure implementation to ensure that locations that are at highest risk of speeding-related crashes are receiving appropriate and effective speed management countermeasures. The framework identifies roadway characteristics that are more likely to contribute to speeding-related crashes, focusing on rural, non-interstate, and non-intersection roads. It underscores the importance of data-driven decision-making to prioritize high-risk locations and optimize resource allocation. By providing states with tools and information, the framework facilitates the identification of critical factors influencing speeding-related crashes, such as roadway alignment, surface conditions, and lighting. Additionally, it provides comprehensive guidance on data collection, data filtering, key characteristics to identify, data analysis, prioritizing findings, applying the results, and monitoring the implementations. This structured approach not only supports the effective use of crash data for informed decision-making but also aligns with the development and execution of SMAPs. The research utilized Virginia Department of Motor Vehicles Traffic Records Electronic Data System (TREDS) crash data for the year 2022 to create the framework. The roadway characteristics included in the analysis were determined using engineering judgement and past studies. Those characteristics were identified from the attributes: location of the first harmful event, light conditions, roadway alignment, roadway description, roadway defects, and roadway surface conditions. A proportional analysis method was used to calculate the speeding-related crash likelihood percentage and the systemic impact percentage for each characteristic included in the analysis. Key findings from this analysis revealed certain roadway characteristics, such as roadside, darkness, curved, two-way not divided, and wet surfaces that had high impacts on both the likelihood of a speeding-related crash occurring and high systemic impact. Virginia crash data was used in this study to test the framework developed to show its effectiveness and how it can be utilized by other states.

Yes / This paper employs a textual analysis approach to quantify financial constraints information from the narrative sections of annual reports in the UK firms. Then, the paper examines the impact of this information on the stock price crash risk in 250 firms from 2005 to 2021. The paper also analyses the moderating role of adopting the International Financial Reporting Standards (IFRS) on the impact of text-based financial constraints on stock price crash risk. It is found that text-based financial constraints are positively associated with stock crash risk measures. It is also observed that adopting the IFRS weakens the positive impact of financial constraints on stock price crash risk. These results are robust to several controls and model specifications. The findings also have several implications for investors and other market participants for seeking evidence of the credibility of annual reports in reflecting relevant information that highlights financial constraints. / The full-text of this article will be released for public view at the end of the publisher embargo on 8 Jan 2027.