Each year about 400,000 trucks are involved in motor vehicle crashes. Crashes involving a car and truck have always been a major concern due to the heavy fatality rates. These types of crashes result in about 60 percent of all fatal truck crashes and two-thirds of all police-reportable truck crashes. Car-truck crashes need to be analyzed further to study the trends for a car-truck crash and develop some countermeasures to lower these crashes. Various types of car-truck crashes are analyzed in this study and the effects of various roadway/environment factors and variables related to driver characteristics in these car-truck crashes are investigated. To examine the crash characteristics and to investigate the significant factors related to a car-truck crash, this study analyzed five years of data (2000-2004) of the General estimates system of National Sampling System (GES) and the Fatality Analysis Reporting system database (FARS). All two vehicle crashes including either a car or truck (truck-truck cases excluded because of their low percentage composition) were obtained from these databases. Based on the five year data (GES/FARS) the percentage of car-truck angle collisions constituted the highest percent of frequency of all types of car-truck collisions. Furthermore, based on the 2004 GES data there is a clear trend that the frequency of angle collision increases with the increase in driver injury severity. When analyzing the GES data it was observed that the percentage of angle collisions was the highest followed by the rear end and sideswipe (same direction) collisions respectively. When the fatalities were considered (FARS database used), the percentage of angle collisions was the highest followed by head-on and rear-end collisions. The nominal multinomial logit model and logistic regression models were utilized for this analysis. Divided section, alcohol involvement, adverse weather conditions, dark lighting condition and old age of drivers had a significant effect on the car-truck crashes and were likely to increase the likelihood of a car-truck crash. Whereas dark but light conditions, young aged drivers showed a less likelihood of involving in a car-truck crash. This research is significant in providing an insight into various car-truck crash types and provides with results, which have impacted the car-truck crashes. A better understanding of the factors impacting these crashes will help in providing better countermeasures, which would result in reducing the car-truck crashes.

Recent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway. Because these models use the real-time traffic data, they are capable of calculating the respective crash risk values as the traffic flow changes along the freeway. The purpose of this study is to examine the potential of two Intelligent Transportation System strategies for reducing the crash risk along the freeway by changing the traffic flow parameters. The two ITS measures that are examined in this research are route diversion and ramp metering. Route diversion serves to change the traffic flow by keeping some vehicles from entering the freeway at one location and diverting them to another location where they may be more efficiently inserted into the freeway traffic stream. Ramp metering alters the traffic flow by delaying vehicles at the freeway on-ramps and only allowing a certain number of vehicles to enter at a time. The two strategies were tested by simulating a 36.25 mile section of the Interstate-4 network in the PARAMICS micro-simulation software. Various implementations of route diversion and ramp metering were then tested to determine not only the effects of each strategy but also how to best apply them to an urban freeway. Route diversion was found to decrease the overall rear-end and lane-change crash risk along the network at free-flow conditions to low levels of congestion. On average, the two crash risk measures were found to be reduced between the location where vehicles were diverted and the location where they were reinserted back into the network. However, a crash migration phenomenon was observed at higher levels of congestion as the crash risk would be greatly increased at the location where vehicles were reinserted back onto the network. Ramp metering in the downtown area was found to be beneficial during heavy congestion. Both coordinated and uncoordinated metering algorithms showed the potential to significantly decrease the crash risk at a network wide level. When the network is loaded with 100 percent of the vehicles the uncoordinated strategy performed the best at reducing the rear-end and lane-change crash risk values. The coordinated strategy was found to perform the best from a safety and operational perspective at moderate levels of congestion. Ramp metering also showed the potential for crash migration so care must be taken when implementing this strategy to ensure that drivers at certain locations are not put at unnecessary risk. When ramp metering is applied to the entire freeway network both the rear-end and lane-change crash risk is decreased further. ALINEA is found to be the best network-wide strategy at the 100 percent loading case while a combination of Zone and ALINEA provides the best safety results at the 90 percent loading case. It should also be noted that both route diversion and ramp metering were found to increase the overall network travel time. However, the best route diversion and ramp metering strategies were selected to ensure that the operational capabilities of the network were not sacrificed in order to increase the safety along the freeway. This was done by setting the maximum allowable travel time increase at 5% for any of the ITS strategies considered.

This thesis describes the development and evaluation of real-time crash risk assessment models for four freeway corridors, US-101 NB (northbound) and SB (southbound) as well as I-880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop detector data. The analysis techniques adopted for this study are logistic regression and classification trees, which are one of the most common data mining tools. The crash risk assessment models are developed based on a binary classification approach (crash and non-crash outcomes), with traffic parameters measured at surrounding vehicle detection station (VDS) locations as the independent variables. The classification performance assessment methodology accounts for rarity of crashes compared to non-crash cases in the sample instead of the more common pre-specified threshold-based classification. Prior to development of the models, some of the data-related issues such as data cleaning and aggregation were addressed. Based on the modeling efforts, it was found that the turbulence in terms of speed variation is significantly associated with crash risk on the US-101 NB corridor. The models estimated with data from US-101 NB were evaluated based on their classification performance, not only on US-101 NB, but also on the other three freeways for transferability assessment. It was found that the predictive model derived from one freeway can be readily applied to other freeways, although the classification performance decreases. The models which transfer best to other roadways were found to be those that use the least number of VDSs–that is, using one upstream and downstream station rather than two or three. The classification accuracy of the models is discussed in terms of how the models can be used for real-time crash risk assessment, which may be helpful to authorities for freeway segments with newly installed traffic surveillance apparatuses, since the real-time crash risk assessment models from nearby freeways with existing infrastructure would be able to provide a reasonable estimate of crash risk. These models can also be applied for developing and testing variable speed limits (VSLs) and ramp metering strategies that proactively attempt to reduce crash risk. The robustness of the model output is assessed by location, time of day and day of week. The analysis shows that on some locations the models may require further learning due to higher than expected false positive (e.g., the I-680/I-280 interchange on US-101 NB) or false negative rates. The approach for post-processing the results from the model provides ideas to refine the model prior to or during the implementation.

This research sought to understand the relationship between licensing policy and the opportunity for the development of a scientifically-based approach to identifying high risk older drivers based on prior driving history. This research focused on five tasks: 1) review of the literature, 2) compilation of information on licensing policy for use by decision-makers, 3) assessment of charges and payer source for older driver crashes using linked crash and hospital data , and 4) the development and 5) validation of an older driver crash prediction model. There is relatively little available in the way of information for policymakers regarding licensing, and there is even less information available on evaluation of licensing practice effectiveness. Emergency department charges for older males were lower than females even though males accounted for a larger percentage of the injured population. Older drivers were no more likely to be covered by public insurance than the comparison group. Crash and citation data used to develop a driver history showed no differences between drivers in injury causing crashes and drivers in non-injury crashes. Logistic regression, Poisson regression, and negative binomial regression models were unable to effectively predict crash involvement based on driver history. This is likely due to self-selection bias for older drivers and truncated distribution of count variable (injury causing crashes). Recommendations resulting from this research include Massachusetts and national policy recommendations and additional research. Massachusetts should expand beyond its referral-based system for reviewing older drivers, consider restriction rather than only revocation, review medical advisory board practices, conduct evaluation of any policies it does implement, and conduct a thorough review of alternative transportation options. Nationally, efforts should focus on developing effective cognitive/functional testing by licensing agents, identification of effective second phase of testing, determination of a mechanism for determining when to retest, and assessment of the differences between older males and females for potential use in training, education, and testing. Research recommendations include continued exploration of the potential for systematic identification of high risk drivers using administrative data and in-depth analyses of the differences between males and females in terms of aging and driver safety.

This dissertation has two main objectives: first, quantify the effects of environmental conditions on spatio-temporal spawning and larval dynamics of zebra mussels (Dreissena polymorpha [Pallas 1771]) in Lake Texoma, and second, quantify the effects of environmental conditions on survival, growth, and reproduction of young of the year (YOY) juvenile zebra mussels. These biological responses directly influence population establishment success and invasive spread dynamics. Reproductive output of the zebra mussel population in Lake Texoma was significantly related to water temperature and lake elevation. Annual maximum larval (veliger) density decreased significantly indicating a population crash, which was likely caused by thermal stress and variability of lake elevation. In 2011, temperatures peaked at 34.3°C and lake elevation decreased to the lowest level recorded during the previous 18 years, which desiccated a substantial number of settled mussels in littoral zones. Estimated mean date of first spawn in Lake Texoma was observed approximately 1.5 months earlier than in Lake Erie, and peak veliger densities were observed two months earlier. Veligers were observed in the deepest oxygenated water after lake stratification. During a 69-day in situ experiment during summer in Lake Texoma, age-specific mortality of zebra mussels was generally high until temperatures decreased to approximately 28°C, which was observed after lake turnover in late summer. No study organism died after temperatures decreased to less than 26°C, which indicates individuals that survive high summer temperatures are likely to persist into autumn/winter. Shell length growth and soft tissue growth rates were related to temperature and chlorophyll-a concentration, respectively. Growth rates of study organisms were among the highest ever reported for D. polymorpha. Water temperature and body size influenced reproduction of YOY zebra mussels in Lake Texoma. Fecundity of females were positively related to temperature; however, sperm production was negatively related to temperature, which indicates males could be more sensitive to physiologically-stressful conditions than females and could perform better in cooler waters. YOY mussels spawned up to approximately 40,000 eggs and 3.47E+08 sperm after a single-summer growing season. Reproductive effort and reproductive mass were independent of sex. YOY individuals from each study site (n = 5) were able to spawn viable gametes capable of sperm binding and egg cleavage, which provides the first evidence that YOY zebra mussels can successfully reproduce. Individual mortality of zebra mussels will likely be high in warm waters and intermittent, extreme droughts, which are observed more frequently at lower latitudes, can significantly reduce population sizes. However, rapid growth and single-season maturation can decrease generation times and could facilitate establishment and spread of zebra mussels in warm-water environments in the southern United States.

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.

This thesis focuses on evaluating the appropriate speed at which vehicles should travel under different traffic conditions on freeways and its impact on crash frequency. The common belief is that the lower speed results in fewer crashes as reduced speed provides drivers with more time to react effectively and avoid collisions. However, this perspective overlooks the interplay among traffic speed, average spacing between consecutive vehicles, and the distance available for stopping a vehicle. Hence, we propose a safety parameter termed ‘Safety Correlate' (SCORE), which is defined as the proportion of average spacing relative to the stopping distance. To determine the relationship between SCORE and crash frequency, data from 366 4-lane urban freeway segments located in Virginia was analyzed and a Random-effects Poisson Lognormal model was developed. The obtained result indicated that the safety parameter SCORE is negatively associated with the annual hourly crash frequency, implying that the lesser the average spacing as a proportion of the stopping distance while traffic flow remains constant, the more frequent will be the crashes. Additionally, this research presents an application of SCORE in setting variable speed limits under various traffic flows. Overall, the study results provide valuable insights by investigating SCORE to improve traffic safety. Also, this research would help practitioners and policymakers to incorporate safety aspects while setting variable speed limits on freeways.

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.

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