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Psychophysiology meets computer science: predicting the magnitude of participant physiological response with machine learning
Title
Psychophysiology meets computer science: predicting the magnitude of participant physiological response with machine learning.
Creator
Parchment, Avonie, Wiegand, Rudolf, Matthews, Gerald, Yan, Xin, Abich, Julian, Greenwood-Ericksen, Adams, University of Central Florida
Abstract / Description
The present inquiry uses methods from psychophysiology and machine learning to reduce overall error in classification models. The field of psychophysiology, though rooted in decades of experimentation, has never reached the same level of precision as some aspects of medical inquiry. In fact, while some medical regression models, when determining some way to classify a patient's illness based on certain symptoms, can result in highly significant results with large effect sizes, equal levels...
Show moreThe present inquiry uses methods from psychophysiology and machine learning to reduce overall error in classification models. The field of psychophysiology, though rooted in decades of experimentation, has never reached the same level of precision as some aspects of medical inquiry. In fact, while some medical regression models, when determining some way to classify a patient's illness based on certain symptoms, can result in highly significant results with large effect sizes, equal levels are virtually unheard of in psychophysiology. The present investigation attempts to unravel some part of this mystery and determines some possible reasons for the difficulty in finding similar effect sizes, especially concerning methods that match participant state with physiological response. Of particular focus are two areas: baseline research and experimental data analysis methods. The role of baselining techniques in relation to overall quality of response is the first emphasis and this interest stems from the Law of Initial Value that indicates some relationship between baseline and experimental response. Though this relationship has been continually investigated and found to be lacking for many physiological measures, experimental condition heart rate response has been consistently shown to rely heavily on baseline response. This finding influences the second half of the present inquiry, which deals with the overall analysis of experimental data and the role that traditional statistics could play in the present problem. By comparing logistic regression and support vector models, it is expected that researchers would use the preferred method, based on their goals, to flag potentially highly influential cases that may greatly skew data and make modeling difficult. Additionally, demographic characteristics that could also help identify these influential cases in the future before modeling are shown.
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Date Issued
2018
Identifier
CFE0007355, ucf:52106
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007355
Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions
Title
Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions.
Creator
Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description
Connected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to...
Show moreConnected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Vehicle platooning with CV technologies is another key element of the future transportation systems which helps to simultaneously enhance traffic operations and safety. CV technologies can also further increase the efficiency and reliability of automated vehicles (AV) by collecting real-time traffic information through V2V and V2I. However, the market penetration rate (MPR) of CAVs and the higher level of automation might not be fully available in the foreseeable future. Hence, it is worthwhile to study the safety benefits of CAV technologies under different MPRs and lower level of automation. None of the studies focused on both traffic safety and operational benefits for these technologies including different roadway, traffic, and weather conditions. In this study, the effectiveness of CAV technologies (i.e., CV /AV/CAV/CV platooning) were evaluated in different roadway, traffic, and weather conditions. To be more specific, the impact of CVs in reduced visibility condition, longitudinal safety evaluation of CV platooning in the managed lane, lower level of AVs in arterial roadway, and the optimal MPRs of CAVs for both peak and off-peak period are analyzed using simulation techniques. Currently, CAV fleet data are not easily obtainable which is one of the primary reasons to deploy the simulation techniques in this study to evaluate the impacts of CAVs in the roadway. The car following, lane changing, and the platooning behavior of the CAV technologies were modeled in the C++ programming language by considering realistic car following and lane changing models in PTV VISSIM. Surrogate safety assessment techniques were considered to evaluate the safety effectiveness of these CAV technologies, while the average travel time, average speed, and average delay were evaluated as traffic operational measures. Several statistical tests (i.e., Two sample t-test, ANOVA) and the modelling techniques (Tobit, Negative binomial, and Logistic regression) were conducted to evaluate the CAV effectiveness with different MPRs over the baseline scenario. The statistical tests and modeling results suggested that the higher the MPR of CAVs implemented, the higher were the safety and mobility benefits achieved for different roadways (i.e., freeway, expressway, arterials, managed lane), weather (i.e., clear, foggy), and traffic conditions (i.e., peak and off-peak period). Interestingly, from the safety and operation perspective, at least 30% and 20% MPR were needed to achieve both the safety and operational benefits of peak and off-peak period, respectively. This dissertation has major implications for improving transportation infrastructure by recommending optimal MPR of CAVs to achieve balanced mobility and safety benefits considering varying roadway, traffic, and weather condition.
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Date Issued
2019
Identifier
CFE0007709, ucf:52442
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007709
Safety, Operational, and Design Analyses of Managed Toll and Connected Vehicles' Lanes
Title
Safety, Operational, and Design Analyses of Managed Toll and Connected Vehicles' Lanes.
Creator
Saad, Moatz, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Oloufa, Amr, Yan, Xin, University of Central Florida
Abstract / Description
Managed lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is...
Show moreManaged lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is achieved by determining the optimal managed lanes access design, including accessibility level and weaving distance for an at-grade access design. (2) the second objective is to study the effects of applying CVs and CV lanes on the MLs network. Several scenarios were tested using microscopic traffic simulation to determine the optimal access design while taking into consideration accessibility levels and weaving lengths. Both safety (e.g., standard deviation of speed, time-to-collision, and conflict rate) and operational (e.g., level of service, average speed, average delay) performance measures were included in the analyses. For the first objective, the results suggested that one accessibility level is the optimal option for the 9-mile network. A weaving length between 1,000 feet to 1,400 feet per lane change was suggested based on the safety analysis. From the operational perspective, a weaving length between 1,000 feet and 2,000 feet per lane change was recommended. The findings also suggested that MPR% between 10% and 30% was recommended when the CVs are only allowed in MLs. When increasing the number of MLs, the MPR% could be improved to reach 70%. Lastly, the findings proposed that MPR% of 100% could be achieved by allowing the CVs to use all the lanes in the network.
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Date Issued
2019
Identifier
CFE0007719, ucf:52428
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007719
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level
Title
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
Creator
Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description
This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
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Date Issued
2018
Identifier
CFE0007358, ucf:52103
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007358
Nurse Managers, Work Environment Factors and Workplace Bullying
Title
Nurse Managers, Work Environment Factors and Workplace Bullying.
Creator
Parchment, Joy, Andrews, Diane, Neff, Donna, Conner, Norma, Yan, Xin, Saunders, Carol, University of Central Florida
Abstract / Description
The purpose of this dissertation is to explore relationships between authentic leadership style, global social power, job demand, job control, and workplace bullying of nurse managers in acute care settings across the United States.Over 30 years of workplace bullying research exists. Consequences are linked to intent to leave, turnover, and harmful emotional and physical effects. Published studies identifying nurse managers as targets of workplace bullying and work environment factors that...
Show moreThe purpose of this dissertation is to explore relationships between authentic leadership style, global social power, job demand, job control, and workplace bullying of nurse managers in acute care settings across the United States.Over 30 years of workplace bullying research exists. Consequences are linked to intent to leave, turnover, and harmful emotional and physical effects. Published studies identifying nurse managers as targets of workplace bullying and work environment factors that contribute to nurse managers being recipients of workplace bullying either, downward from their leaders, horizontally from their nurse manager peers, and upwards from their clinical nurses were not identified.A descriptive, cross-sectional design using an online survey was utilized. Descriptive, inferential, and multivariate analyses were used to identify relationships and the likelihood of workplace bullying occurring. Thirty-five percent (n = 80) of nurse managers reported being a target of workplace bullying. Managers sustained occasional (56%, n = 45) and severe (44%, n = 35) levels of workplace bullying, 65% (n = 43) identified their executive nurse leader as the predominate perpetrator. Authentic leadership, job demand, job control correlated significantly (p = (<).01) with workplace bullying and job demand demonstrated the strongest likelihood (OR = 3.9) for predicting workplace bullying. Nurse Managers are four times more likely to be a recipient of workplace bullying when their job responsibilities are classified as demanding. This study expanded the science and demonstrated that nurse managers, the backbone of organizations, are recipients of workplace bullying emanating predominately from executive nurse leaders, but also from clinical nurses and their nurse manager peers. Given the harmful consequences of workplace bullying, as 'guardians' of and 'advocates' for their teams, executive nursing leaders, have an ethical and operational responsibility to ensure nurse managers are able to practice in a safe environment.
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Date Issued
2015
Identifier
CFE0005986, ucf:50771
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005986
The Impact of Relational Coordination and the Nurse on Patient Outcomes
Title
The Impact of Relational Coordination and the Nurse on Patient Outcomes.
Creator
Dejesus, Fanya, Andrews, Diane, Sole, Mary Lou, Neff, Donna, Yan, Xin, Unruh, Lynn, University of Central Florida
Abstract / Description
Healthcare quality remains a significant issue due to fragmentation of care in our complex U.S. healthcare systems. While coordination of care is foundational to healthcare quality as well as identified as a National Priority, fragmentation and uncoordinated care continues to afflict our systems. The purpose of this study was to explore the relationship between relational coordination and adverse nurse sensitive patient outcomes, namely hospital acquired pressure ulcers, patient falls with...
Show moreHealthcare quality remains a significant issue due to fragmentation of care in our complex U.S. healthcare systems. While coordination of care is foundational to healthcare quality as well as identified as a National Priority, fragmentation and uncoordinated care continues to afflict our systems. The purpose of this study was to explore the relationship between relational coordination and adverse nurse sensitive patient outcomes, namely hospital acquired pressure ulcers, patient falls with injury, catheter- associated urinary tract infection, and central line-associated blood stream infection. A retrospective correlational survey design using cross sectional data was used to conduct this quantitative study. An electronic relational coordination survey was sent to 1124 eligible registered nurses from 43 nursing units within a 5-hospital magnet-designated healthcare system to gather their perception of the strength of relationship and communication ties of their work team. The nurse practice environment as well as nurse education were control variables. With 406 nurses who completed the survey (36% response rate), findings revealed that the stronger relational coordination ties are amongst the healthcare team, the lower the rate of adverse nurse sensitive patient outcomes as indicated by their inverse relationship. (rs=-.31, p=.050). In a Negative Binomial Regression model, relational coordination was a significant predictor (?-1.890, p=.034) of nurse sensitive patient outcomes whereas nurse education level (p=.859) and nurse practice environment (p=.230) were not. Data affirms that relational coordination, a relationship and communication intensive form of coordination does impact patient outcomes. This research provides significant information to health care leaders and institutions with goals of improving patient care outcomes through enhancement of coordination of care and optimization of healthcare teams.
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Date Issued
2015
Identifier
CFE0005939, ucf:50823
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005939
Perceived readiness to transition to adult health care for youth with cystic fibrosis and congruence with their caregivers' views
Title
Perceived readiness to transition to adult health care for youth with cystic fibrosis and congruence with their caregivers' views.
Creator
Lapp, Valerie, Chase, Susan, Aroian, Karen, Weiss, Josie, Yan, Xin, Robinson, Patricia, University of Central Florida
Abstract / Description
Youth with cystic fibrosis must gradually assume considerable self-care management skills in order to optimize longevity and quality of life, and healthcare providers and caregivers play a role in youth gradually assuming these skills. The purpose of this study was to determine how youth with cystic fibrosis perceive their self-care management skills required for transition to adult healthcare, the relationship between age and skill acquisition, youth and caregiver congruence on perceived...
Show moreYouth with cystic fibrosis must gradually assume considerable self-care management skills in order to optimize longevity and quality of life, and healthcare providers and caregivers play a role in youth gradually assuming these skills. The purpose of this study was to determine how youth with cystic fibrosis perceive their self-care management skills required for transition to adult healthcare, the relationship between age and skill acquisition, youth and caregiver congruence on perceived transition readiness, and frequency of transition discussion with provider. In this descriptive, correlational, cross-sectional design, 58 youth ages 14-22 rated their skill ability in managing cystic fibrosis using the Transition Readiness Assessment Questionnaire (TRAQ) during visits to the cystic fibrosis clinic. Using an adapted version of the questionnaire, the TRAQ-C, 52 caregivers also rated youth readiness to transition to determine congruence in self-care management ability. Five simple regressions were calculated to determine age effects for the self-care management skills. Independent t-tests were used to compare mean scores of youth and caregiver perceptions of self-care management skills. Age predicted youth perception of readiness for self-care management skills. Youth scored significantly higher than their caregivers did in perception of self-care skill management. Study findings suggest that preparation for transition to adult care should begin at an earlier age to prepare youth to assume self-care. Including transition discussion with youth and caregiver assessments using questionnaires such as the TRAQ and TRAQ-C may guide learning of skills and timing of transition to adult health care.
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Date Issued
2016
Identifier
CFE0006133, ucf:51185
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006133
A Faith-Based Primary Diabetes Prevention Intervention for At-Risk Puerto Rican Adults: A Feasibility Study
Title
A Faith-Based Primary Diabetes Prevention Intervention for At-Risk Puerto Rican Adults: A Feasibility Study.
Creator
Torres-Thomas, Sylvia, Chase, Susan, Covelli, Maureen, Gonzalez, Laura, Yan, Xin, Miller, Ann, University of Central Florida
Abstract / Description
Diabetes is a serious health threat that disproportionately affects Hispanics of Puerto Rican heritage. Current evidence supports diabetes prevention programs to change health behaviors in people who are at risk and thus prevent the development of type 2 diabetes. However, few interventions exist for Hispanics, and even fewer have been designed for Puerto Rican adults. A literature review of community-based diabetes prevention programs involving at-risk Hispanics was conducted using a...
Show moreDiabetes is a serious health threat that disproportionately affects Hispanics of Puerto Rican heritage. Current evidence supports diabetes prevention programs to change health behaviors in people who are at risk and thus prevent the development of type 2 diabetes. However, few interventions exist for Hispanics, and even fewer have been designed for Puerto Rican adults. A literature review of community-based diabetes prevention programs involving at-risk Hispanics was conducted using a cultural sensitivity framework to determine the state of the science and identify gaps in knowledge regarding diabetes prevention for Puerto Ricans. An integrated theoretical framework was developed using constructs from the extended parallel process model (perceived severity and susceptibility) and social cognitive theory (self-efficacy) to design program components aimed to educate and motivate positive dietary behavior change in Puerto Rican adults. The two key components were a diabetes health threat message and dietary skill building exercises that incorporated spirituality and relevant faith practices, and were culturally-tailored for Puerto Ricans. A pretest-posttest, concurrent mixed methods design was used to test the impact and evaluate feasibility of a diabetes health threat message and skill-building exercises in a sample of Puerto Rican adults. A total of 24 participants enrolled in the study and attended six-weekly meetings that included baseline data collection, a health threat message, dietary skill building exercises, focus group interviews, posttest data collection, and an end-of-study potluck gathering. All of the study participants were Puerto Rican and a majority were female (70.8%), with a mean age of 55.5 years (SD 13.71). Most had a family history of diabetes (n = 21, 87.5%) and believed they were at-risk for the disease (n = 16, 66.7%). Using Wilcoxon matched-pairs signed rank test, significant increases or improvements were found in perceptions of diabetes severity (p (<) .01), dietary self-efficacy (p = .002), and dietary patterns (p = .02) at posttest in comparison to baseline. Spearman's rank correlations found moderate to strong relationships between the following variables: perceived severity and weight (rs = -.44, p = .03), dietary self-efficacy and dietary patterns (rs = .43, p = .04), dietary self-efficacy and fasting blood glucose levels (rs = - .45, p = .03), and American acculturation and weight (rs = .51, p = .02). The qualitative themes that emerged contributed to our understanding of participants' perspective relative to the health threat message, dietary skill building exercises, and the importance of cultural relevance and spirituality. The data support feasibility of this faith-based intervention that had an attendance rate of 58% and no loss of sample due to attrition. Diabetes prevention interventions for at-risk Puerto Ricans adults that incorporate a faith-based, culturally-tailored health threat message and dietary skill building exercises may help educate those who are at-risk and motivate lifestyle behavior change to prevent the development of diabetes. Further faith-based, culturally-tailored diabetes prevention research is indicated for Puerto Rican adults.
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Date Issued
2015
Identifier
CFE0005725, ucf:50124
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005725
Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models
Title
Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models.
Creator
Cai, Qing, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Lee, JaeYoung, Yan, Xin, University of Central Florida
Abstract / Description
Crash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so...
Show moreCrash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so as to provide countermeasures from a planning perspective. On the other hand, the microscopic crashes on a segment or intersection are analyzed to identify the influence of geometric design, lighting and traffic flow characteristics with the objective of offering engineering solutions (such as installing sidewalk and bike lane, adding lighting). Although numerous traffic safety studies have been conducted, still there are critical limitations at both levels. In this dissertation, several methodologies have been proposed to alleviate several limitations in the macro- and micro-level safety research. Then, an innovative method has been suggested to analyze crashes at the two levels, simultaneously. At the macro-level, the viability of dual-state models (i.e., zero-inflated and hurdle models) were explored for traffic analysis zone based pedestrian and bicycle crash analysis. Additionally, spatial spillover effects were explored in the models by employing exogenous variables from neighboring zones. Both conventional single-state model (i.e., negative binomial) and dual-state models such as zero-inflated negative binomial and hurdle negative binomial models with and without spatial effects were developed. The model comparison results for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency. Then, the modifiable areal unit problem for macro-level crash analysis was discussed. Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), traffic analysis zones (TAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) were developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposed a method to compare the modeling performance of the three types of geographic units at different spatial configuration through a grid based framework. Specifically, the study region was partitioned to grids of various sizes and the model prediction accuracy of the various macro models was considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperformed the ones that do not consider it. Finally, based on the modeling results, it is recommended to adopt TADs for transportation safety planning.After determining the optimal traffic safety analysis zonal system, further analysis was conducted for non-motorist crashes (pedestrian and bicycle crashes). This study contributed to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we converted the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulated a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model was estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model was also estimated and compared with the joint model. The results indicated that the joint model provides better data fit and could identify more significant variables. Subsequently, a novel joint screening method was suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes were identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. At the microscopic level, crash modeling analysis was conducted for road facilities. This study, first, explored the potential macro-level effects which are always excluded or omitted in the previous studies. A Bayesian hierarchical model was proposed to analyze crashes on segments and intersection incorporating the macro-level data, which included both explanatory variables and total crashes of all segments and intersections. Besides, a joint modeling structure was adopted to consider the potentially spatial autocorrelation between segments and their connected intersections. The proposed model was compared with three other models: a model considering micro-level factors only, one hierarchical model considering macro-level effects with random terms only, and one hierarchical model considering macro-level effects with explanatory variables. The results indicated that models considering macro-level effects outperformed the model having micro-level factors only, which supports the idea to consider macro-level effects for micro-level crash analysis. Besides, the micro-level models were even further enhanced by the proposed model. Finally, significant spatial correlation could be found between segments and their adjacent intersections, supporting the employment of the joint modeling structure to analyze crashes at various types of road facilities. In addition to the separated analysis at either the macro- or micro-level, an integrated approach has been proposed to examine traffic safety problems at the two levels, simultaneously. If conducted in the same study area, the macro- and micro-level crash analyses should investigate the same crashes but aggregating the crashes at different levels. Hence, the crash counts at the two levels should be correlated and integrating macro- and micro-level crash frequency analyses in one modeling structure might have the ability to better explain crash occurrence by realizing the effects of both macro- and micro-level factors. This study proposed a Bayesian integrated spatial crash frequency model, which linked the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considered the spatial autocorrelation of different types of road facilities (i.e., segments and intersections) at the micro-level with a joint modeling structure. Two independent non-integrated models for macro- and micro-levels were also estimated separately and compared with the integrated model. The results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model provides more valuable insights about the crash occurrence at the two levels by revealing both macro- and micro-level factors. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels. It is expected that the proposed integrated model and hotspot identification method can help practitioners implement more reasonable transportation safety plans and more effective engineering treatments to proactively enhance safety.
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Date Issued
2017
Identifier
CFE0006724, ucf:51891
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006724