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ASSESSING CRASH OCCURRENCE ON URBAN FREEWAYS USING STATIC AND DYNAMIC FACTORS BY APPLYING A SYSTEM OF INTERRELATED EQUATIONS
Title
ASSESSING CRASH OCCURRENCE ON URBAN FREEWAYS USING STATIC AND DYNAMIC FACTORS BY APPLYING A SYSTEM OF INTERRELATED EQUATIONS.
Creator
Pemmanaboina, Rajashekar, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description
Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models...
Show moreTraffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.
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Date Issued
2005
Identifier
CFE0000587, ucf:46468
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000587
DRIVING SIMULATOR VALIDATION AND REAR-END CRASH RISK ANALYSIS AT A SIGNALISED INTERSECTION
Title
DRIVING SIMULATOR VALIDATION AND REAR-END CRASH RISK ANALYSIS AT A SIGNALISED INTERSECTION.
Creator
Chilakapati, Praveen, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description
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...
Show moreIn 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.
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Date Issued
2006
Identifier
CFE0001391, ucf:46964
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001391
Analysis of Remote Tripping Command Injection Attacks in Industrial Control Systems Through Statistical and Machine Learning Methods
Title
Analysis of Remote Tripping Command Injection Attacks in Industrial Control Systems Through Statistical and Machine Learning Methods.
Creator
Timm, Charles, Caulkins, Bruce, Wiegand, Rudolf, Lathrop, Scott, University of Central Florida
Abstract / Description
In the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to...
Show moreIn the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to both quickly identify and mitigate the effects of a remote tripping command injection attack, a dataset comprised of 128 variables (primarily synchrophasor measurements) was analyzed via statistical methods and machine learning algorithms in RStudio and WEKA software respectively. While statistical methods were not successful due to the non-linearity and complexity of the dataset, machine learning algorithms surpassed accuracy metrics established in previous research given a simplified dataset of the specified attack and normal operational data. This research allows future cybersecurity researchers to better understand remote tripping command injection attacks in comparison to normal operational conditions. Further, an incorporation of the analysis has the potential to increase detection and thus mitigate risk to the North American power grid in future work.
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Date Issued
2018
Identifier
CFE0007257, ucf:52193
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007257
Factors Contributing to Low Adequate Prenatal Care Rates in Orange County, Florida
Title
Factors Contributing to Low Adequate Prenatal Care Rates in Orange County, Florida.
Creator
Daniel, Lauren, Donley, Amy, Hinojosa, Melanie, University of Central Florida
Abstract / Description
In 2017, only 56% of births in Orange County, Florida, received adequate prenatal care(-)care that has been shown to prevent maternal and infant death. The Florida Department of Health uses the Kotelchuck Index to determine care adequacy. This index rates care adequacy based on when the mother first receives care, and how many recommended appointments she attends. Prenatal care is rated (")inadequate(") if it starts after the fourth month of pregnancy, and/or if less than half of the...
Show moreIn 2017, only 56% of births in Orange County, Florida, received adequate prenatal care(-)care that has been shown to prevent maternal and infant death. The Florida Department of Health uses the Kotelchuck Index to determine care adequacy. This index rates care adequacy based on when the mother first receives care, and how many recommended appointments she attends. Prenatal care is rated (")inadequate(") if it starts after the fourth month of pregnancy, and/or if less than half of the recommended appointments are attended. Receiving earlier and consistent prenatal care has been shown to be an effective way to improve birth outcomes.In Florida, counties that have low adequate prenatal care rates like Orange County's tend to be less populous and rural. However, Orange County stands out with its large population of 1.3 million and more urban environment; other Florida counties similar in population and environment to Orange tend to have rates like that of the state's, at approximately 70%.The objective of this study is to determine which factors contribute most significantly to prenatal care inadequacy in Orange, Duval, Hillsborough, Miami-Dade, and Pinellas counties; determine the differences between the most significant factors in Orange County and those in the other four counties; and to determine if residing in Orange County in of itself a risk factor for inadequate prenatal care, using logistic regression. By identifying factors that may lead to low adequacy rates, interventions intended to increase care adequacy in Orange County can be better targeted towards populations in need.
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Date Issued
2019
Identifier
CFE0007447, ucf:52715
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007447