This thesis explores board nationality diversity, focusing on foreign non-executive directors and their relationship with CEO compensation, firm performance, and crash risk for a sample of UK firms from 2002 to 2015. First, we examine the changes in board composition over the years and find an increase in foreign non-executive directors and in the number of foreign CEOs managing UK firms. We discover boards have become smaller, more independent and CEOs occupying dual roles have considerably reduced. Next, we analyse the relationship between foreign non executive directors and CEO compensation and note that firms with more foreign non executive directors pay less to their CEO. Moreover, European and other international non-executive directors are particularly effective at limiting CEO compensation. Then we examine the impact of foreign non-executive directors on firm performance and show that foreign non-executive directors positively impact firm value. CEO and executive directors’ equity-like compensation and share ownership also positively influences firm performance. Our findings suggest that European and American non executive directors are more effective in improving corporate performance. Finally, we analyse the relationship between foreign non-executive directors, CEO compensation and crash risk. Foreign non-executive directors monitor the board and mitigate the impact of CEO equity-linked pay on stock price crash risk. Our analysis reveals that leverage increases crash risk, but that foreign non-executive directors, of high leverage firms lower crash risk. Overall, foreign non-executive directors serve as effective monitors and advisors to moderate executive pay, improve firm performance and reduce stock price crash risk.

In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.

Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment. / Master of Science

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.

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.

While driver distraction has been extensively studied in laboratory and empirical field studies, the prevalence of driver distraction on our nation's highways and the relative crash risk is unknown. It has recently become technologically feasible to conduct unobtrusive large-scale naturalistic driving studies as the costs and size of computer equipment and sensor technology have both dramatically decreased. A large-scale naturalistic driving study was conducted using 100 instrumented vehicles (80 privately-owned and 20 leased vehicles). This data collection effort was conducted in the Washington DC metropolitan area on a variety of urban, suburban, and rural roadways over a span of 12-13 months. Five channels of video and kinematic data were collected on 69 crashes and 761 near-crashes during the course of this data collection effort. The analyses conducted here are the first to establish direct relationships between driving inattention and crash and near-crash involvement. Relative crash risk was calculated using both crash and near-crash data as well as normal, baseline driving data, for various sources of inattention. Additional analyses investigated the environmental conditions drivers choose to engage in secondary tasks or drive fatigued, assessed whether questionnaire data were indicative of an individual's propensity to engage in inattentive driving, and examined the impact of driver's eyes off the forward roadway. The results indicated that driving inattention was a contributing factor in 78% of all crashes and 65% of all near-crashes. Odds ratio calculations indicated that fatigued drivers have a 4 times higher crash risk than alert drivers. Drivers engaging in visually and/or manually complex tasks are at 7 times higher crash risk than alert drivers. There are specific environmental conditions in which engaging in secondary tasks or driving fatigued is deemed to be more dangerous, including intersections, wet roadways, undivided highways, curved roadways, and driving at dusk. Short, brief glances away from the forward roadway for the purpose of scanning the roadway environment (e.g., mirrors and blind spots) are safe and decrease crash risk, whereas such glances that total more than 2 seconds away from the forward roadway are dangerous and increase crash risk by 2 times over that of more typical driving. / Ph. D.

Tidsperioden mellan räkenskapsårets slut och datumet för påskriven revisionsberättelse benämns audit report lag (ARL). Anledningarna till att ARL uppstår har studerats i stor utsträckning, men de konkreta effekterna som uppstår till följd av ARL är mindre studerade. En tidigare studie om ARL:s samband med ökad risk för aktieprisfall på den kinesiska aktiemarknaden visade på ett positivt samband. På grund av detta samband finns ett intresse att studera om ett liknande samband existerar på den svenska aktiemarknaden. Syftet med studien är att förklara ett eventuellt samband mellan lång ARL och ökad risk för aktieprisfall på den svenska aktiemarknaden. Studien använder sig av en deduktiv ansats och en longitudinell forskningsdesign bestående av kvantitativ data för att försöka förklara ett eventuellt samband mellan lång ARL och en ökad risk för aktieprisfall. Datamaterialet bestod av sekundärdata. Studien finner inget samband mellan lång ARL och ökad risk för aktieprisfall på den svenska aktiemarknaden. Däremot visas svaga indikationer på att kort ARL leder till ökad risk för aktieprisfall på den svenska aktiemarknaden. Studien bidrar med utökad kunskap om sambanden mellan ARL och ökad risk för aktieprisfall. Vidare bidrar studien med kunskap för revisorer, bolagsledningar och investerare om vilka konsekvenser ARL kan ha på börsnoterade bolags aktiekurs. / The time period between the fiscal year end and the audit report date is termed audit report lag (ARL). The determinants of ARL have been frequently studied, however the practical consequences of ARL have not been studied to the same extent. A previous study about ARLs association with stock price crash risk on the Chinese stock market showed a positive association. This association made it interesting to study if a similar association exists on the Swedish stock market. The purpose of this study is to explain a possible association between long ARL and an increased stock price crash risk on the Swedish stock market. This study uses a deductive approach and a longitudinal research design consisting of quantitative data to explain a possible association between long ARL and an increased stock price crash risk. The data set consisted of secondary data. The study finds no association between long ARL and an increased stock price crash risk on the Swedish stock market. However, the study does find weak indications that short ARL leads to an increased stock price crash risk on the Swedish stock market. The study contributes with increased knowledge regarding associations between ARL and an increased stock price crash risk. Furthermore, the study contributes with knowledge for auditors, company management and investors of the consequences ARL can have on listed companies’ stock price.

In recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world signalized intersection (Alafaya trail (SR-434) and Colonial Drive (SR-50)). A total of sixty one subjects of age ranging from sixteen to sixty years were recruited to drive the simulator for the experiment, which consists of eight scenarios. This research validates the driving simulator for speed, safety and visual aspects. Based on the overall comparisons of speed between the simulated results and the real world, it was concluded that the UCF driving simulator is a valid tool for traffic studies related to driving speed behavior. Based on statistical analysis conducted on the experiment results, it is concluded that SR-434 northbound right turn lane and SR-50 eastbound through lanes have a higher rear-end crash risk than that at SR-50 westbound right turn lane and SR-434 northbound through lanes, respectively. This conforms to the risk of rear-end crashes observed at the actual intersection. Therefore, the simulator is validated for using it as an effective tool for traffic safety studies to test high-risk intersection locations. The driving simulator is also validated for physical and visual aspects of the intersection as 87.10% of the subjects recognized the intersection and were of the opinion that the replicated intersection was good enough or realistic. A binary logistic regression model was estimated and was used to quantify the relative rear-end crash risk at through lanes. It was found that in terms of rear-end crash risk SR50 east- bound approach is 23.67% riskier than the SR434 north-bound approach.

<p dir="ltr">The emergence of time-dependent data provides researchers with unparalleled opportunities to investigate disaggregated levels of safety performance on roadway infrastructures. A disaggregated crash risk analysis uses both time-dependent data (e.g., hourly traffic, speed, weather conditions and signal controls) and fixed data (e.g., geometry) to estimate hourly crash probability. Despite abundant research on crash risk analysis, coordinated signalized intersections continue to require further investigation due to both the complexity of the safety problem and the relatively small number of past studies that investigated the risk factors of coordinated signalized intersections. This dissertation aimed to develop robust crash risk prediction models to better understand the risk factors of coordinated signalized intersections and to identify practical safety countermeasures. The crashes first were categorized into three types (same-direction, opposite-direction, and right-angle) within several crash-generating scenarios. The data needed were organized in hourly observations and included the following factors: road geometric features, traffic movement volumes, speeds, weather precipitation and temperature, and signal control settings. Assembling hourly observations for modeling crash risk was achieved by synchronizing and linking data sources organized at different time resolutions. Three different non-crash sampling strategies were applied to the following three statistical models (Conditional Logit, Firth Logit, and Mixed Logit) and two machine learning models (Random Forest and Penalized Support Vector Machine). Important risk factors, such as the presence of light rain, traffic volume, speed variability, and vehicle arrival pattern of downstream, were identified. The Firth Logit model was selected for implementation to signal coordination practice. This model turned out to be most robust based on its out-of-sample prediction performance and its inclusion of important risk factors. The implementation examples of the recommended crash risk model to building daily risk profiles and to estimating the safety benefits of improved coordination plans demonstrated the model’s practicality and usefulness in improving safety at coordinated signals by practicing engineers.</p>

This study examines the relationship between company management guidance, and ex-ante crash risk over the duration of 2008(Jan 2006-Dec 2009) financial crisis using the implied volatility skew, which is based upon ex-ante volatility implied by the pricing model developed by Black-Scholes (1973). The study finds that over the duration of this crisis period, management guidance decreases with a rise in ex-ante crash risk. Further, the study provides evidence on the relationship of management guidance and earnings volatility, and how that is affected by a firm's industry product concentration based on the Herfindahl-Hirschman Index (HHI) score.