Recent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes. Ramp Metering and Variable Speed Limits are two approaches which have the potential of becoming effective implementation strategies for improving the safety conditions on congested freeways. This research evaluates both these strategies in different configurations and attempts to quantify their effect on risk of crash on a 9-mile section of Interstate-4 in the Orlando metropolitan region. The section consists of 17 Loop Detector stations, 11 On-ramps and 10 off-ramps. PARAMICS micro-simulation is used as the tool for modeling the freeway section. The simulated network is calibrated and validated for 5 minute average flows and speeds using loop detector data. Feedback Ramp Metering algorithm, ALINEA, is used for controlling access from up to 7 on-ramps. Variable Speed Limits are implemented based on real-time speed conditions prevailing in the whole 9-mile section. Both these strategies are tested separately as well as collectively to determine the individual effects of all the parameters involved. The results have been used to formulate and recommend the best possible strategy for minimizing the risk of crashes on the corridor. The study concluded that Ramp Metering improves the conditions on the freeway in terms of safety by decreasing variance in speeds and decreasing average occupancy. A safety benefit index was developed for quantifying the reduction in crash risk and it indicated that an optimal implementation strategy might produce benefits of up to 55%. The condition on the freeway section improved with increase in the number of metered ramps. It was also observed that shorter signal cycles for metered ramps were more suitable for metering multiple ramps. Ramp Metering at multiple locations also decreased the segment wide travel-times by 5% and was even able to offset the delays incurred by drivers at the metered on-ramps. Variable Speed Limits (VSL) were individually not as effective as ramp metering but when implemented along with ramp metering, they were found to further improve the safety on the freeway section under consideration. By means of a detailed experimental design it was observed that the best strategy for introducing speed limit changes was to raise the speed limits downstream of the location of interest by 5 mph and not affecting the speed limits upstream. A coordinated strategy - involving simultaneous application of VSL and Ramp Metering - provided safety benefits of up to 56 % for the study section according to the safety benefit index. It also improved the average speeds on the network besides decreasing the overall network travel time by as much as 21%.

Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.

Analysis of intersection crashes is a significant area in traffic safety research. This study contributes to the area by identifying traffic-geometric characteristics and driver demographics that affect different types of crashes at signalized intersections. A simple methodology to estimate crash frequency at intersections based on the size of the intersection is also developed herein. First phase of this thesis used the crash frequency data from 1,335 signalized intersections obtained from six jurisdictions in Florida, namely, Brevard, Seminole, Dade, Orange, and Hillsborough Counties and the City of Orlando. Using these data a simple methodology has been developed to identify the expected number of crashes by type and severity at signalized intersections. Intersection size, based on the total number of lanes, was used as a factor that was simple to identify and a representative of many geometric and traffic characteristics of an intersection. The results from the analysis showed that crash frequency generally increased with the increased size of intersections but the rates of increase differed for different intersection types (i.e., Four-legged intersection with both streets two-way, Four-legged intersection with at least one street one-way, and T-intersections). The results also showed that the dominant type of crashes differed at these intersection types and severity of crashes was higher at the intersections with more conflict points and larger differential in speed limits between major and minor roads. The analysis may potentially be useful for traffic engineers for evaluating safety at signalized intersections in a simple and efficient manner. The findings in this analysis provide strong evidence that the patterns of crashes by type and severity vary with the size and type of intersections. Thus, in future analysis of crashes at intersections, the size and type of intersections should be considered to account for the effects of intersection characteristics on crash frequency. In the second phase, data (crash and intersection characteristics) obtained from individual jurisdictions are linked to the Department of Highway Safety and Motor Vehicles (DHSMV) database to include characteristics of the at-fault drivers involved in crashes. These crashes are analyzed using contingency tables and binary logistic regression models. This study categorizes crashes into three major types based on relative initial movement direction of the involved vehicles. These crash types are, 1) Initial movement in same direction (IMSD) crashes. This crash type includes rear end and sideswipe crashes because the involved vehicles for these crashes would be traveling in the same direction prior to the crash. 2) Initial movement in opposite direction (IMOD) crashes comprising left-turn and head on crashes. 3) Initial movement in perpendicular direction (IMPD) crashes, which include angle and right-turn crashes. Vehicles involved in these crashes would be traveling on different roadways that constitute the intersection. Using the crash, intersection, and at-fault driver characteristics for all crashes as inputs, three logistic regression models are developed. In the logistic regression analyses total number of through lanes at an intersection is used as a surrogate measure to AADT per lane and also intersection type is introduced as a 'predictor' of crash type. The binary logistic regression analyses indicated, among other results, that at intersections with one-way roads, adverse weather conditions, older drivers and/or female drivers increase the likelihood of being at-fault at IMOD crashes. Similar factors associated with other groups of crashes (i.e., IMSD and IMPD) are also identified. These findings from the study may be used to develop specialized training programs by zooming in onto problematic intersections/maneuvers.

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.