Common use of LIST OF TABLES Clause in Contracts

LIST OF TABLES. Table 1 Summary of Literature in AVs 2 Table 2 Summary of Literature on Adoption 6 Table 3 Summary of Literature on WTP 8 Table 4 Summary of Literature on Mode Choice 9 Table 5 Summary of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.

LIST OF TABLES. Table 1 Page Table 1-1. Continuous Parameters Monitored at the NAEMS Lagoon Sites 1-4 Table 1-2. Monitoring Sites Under the NAEMS 1-5 Table 2 1. Summary of NAEMS Open-Source Sites 2-11 Table 3 1. NAEMS Emissions and Process Parameter Data Received 3-3 Table 3 2. Reported Emission Rates for NAEMS Lagoon Sites 3-5 Table 3 3. Review of Swine Lagoon Articles Received in Response to EPA’s CFI 3-9 Table 3 4. Review of Dairy Lagoon Articles Received in Response to EPA’s CFI 3-12 Table 3 5. Review of Swine Lagoon Articles Obtained by Previous EPA Literature in AVs 2 Searches 3-15 Table 2 3 6. Review of Dairy Lagoon Articles Obtained by Previous EPA Literature Searches 3-19 Table 4 1. Reported Number of Valid Emissions Days by Site 4-5 Table 4 2. Summary of Literature on Adoption 6 Intermittent Data 4-7 Table 3 Summary 4 3. Dates of Literature on WTP Lagoon Coverage Observations 4-8 Table 4 4. Dates or Animal Inventory Records 4-10 Table 4 5. Negative Emissions Values by Xxxxxxxxx and Measurement Method 4-12 Table 4 6. Number of Valid Emissions Days Available in the Spring and Summer 4-13 Table 4 7. Number of Valid Emissions Days Available in the Fall and Winter 4-13 Table 4 8. NAEMS Data for Swine and Dairy Lagoon Confinement Operations 4-14 Table 4 9. Design and Operating Parameters of the NAEMS Swine and Dairy Lagoon Sites 4-15 Table 4 10. Site-Specific Ambient and Lagoon Conditions 4-16 Table 4 11. Average Daily Emissions for by Site 4-17 Table 5 1. Summary of Literature on Mode Choice Symbols and Terms Used in Equation 5 1 5-4 Table 5 2. Number of 30-Minute NH3 Emissions Values by Site 5-7 Table 5 3. Number of 30-Minute Data Values for Continuous Variables 5-8 Table 5 4. Number of 30-Minute Values Available for Intermittent Data by Site 5-9 Table Page Table 5 5. Farm and Lagoon Information by Site 5-10 Table 5 6. Selected Candidate Predictor Variables 5-13 Table 5 7. Meteorological Variable Bin Cut-Offs 5-21 Table 5 8. Summary Statistics for NH3 and Meteorological Variables 5-25 Table 5 9. Summary of Literature on Benefit and Concerns 10 Main Effect Mean Trend Variables 5-26 Table 6 5 10. Summary of Literature on Perception of Technology and Operations 12 Farm-Based Predictor Variables 5-44 Table 7 5 11. Summary of Literature on Travel Demand 14 Table 8 Classification of Studies Data Available by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle Month and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model Site 5-52 Table 17 5 12. Fit Statistics for Subsets of Mean Trend Variables 5-53 Table 5 13. Fit Statistics for Identity, Log and Reciprocal Link Functions 5-55 Table 5 14. Fit Statistics With and Without a Random Effect of Site 5-57 Table 5 15. Fit Statistics for Three Combinations of Two Farm-Based Variables 5-58 Table 5 16. Variables Values Used In Example Calculations 5-58 Table 5 17. Results of Factor Analysis the animal/sa EEM Examples 5-60 Table 5 18. Values of Mean Trend Variables for Mode Dependency 56 the animal/sa EEM Examples 5-61 Table 18 Linear SVM Model Performances 57 5 19. Results of the animal/size EEM Examples 5-63 Table 19 Linear SVM Model Coefficients 59 5 20. Values of Mean Trend Variables for animal/size EEM Examples 5-64 Table 20 Mode Choice Model 5 21. Results of the sa/size EEM Examples 5-66 Table 5 22. Values of Mean Trend Variables for Regular Trips (tthe sa/size EEM Examples 5-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.67

LIST OF TABLES. Table 1 1: 2014 Lake Chelan Xxxxx Survey Angler Data 3 Table 2: 2014 Lake Chelan Xxxxx Survey Results 3 Table 3: Cutthroat/Rainbow Trout Redds Observed in Three Lake Chelan Tributaries, 2011 6 Table 4: Snorkel Survey Results for Three Lake Chelan Tributaries, 2011 6 Table 5: Estimated 2013 Lake Chelan Tributary Rainbow Trout Density and Population Abundance 8 Table 6: Estimated 2014 Lake Chelan Tributary Rainbow Trout Density and Population Abundance 8 Table 7: Estimated 2013 Lake Chelan Tributary Cutthroat Trout Density and Population Abundance 9 Table 8: Estimated 2014 Lake Chelan Tributary Cutthroat Trout Density and Population Abundance 9 Table 9: A Comparison of Species Abundance and Composition 1982 vs. 2014 10 Table 10: A Comparison of Species Abundance and Composition 1982 vs 2013. 10 Table 11: A Comparison of Species Abundance and Composition 1982 vs. 2012 10 Table 12: A Comparison of Species Abundance and Composition 1982 vs. 2011 10 Table 13: Number of Greater than Six-Inch Fish Observed and Size Classes within Two Mainstem Xxxxxx Sites, 2012 – 2014 13 Table 14: 2011 Fish Stocking Plan 21 Table 15: 2011 Actual Fish Stocking 21 Table 16: 2012 Fish Stocking Plan 22 Table 17: 2012 Actual Fish Stocking 22 Table 18: 2013 Fish Stocking Plan 23 Table 19: 2013 Actual Fish Stocking 23 Table 20: 2014 Fish Stocking Plan 24 Table 21: 2014 Actual Fish Stocking 24 Table 22: 2015 Fish Stocking Plan 25 Table 23: USFS Estimated Lake Chelan Tributaries Spawning Monitoring and Evaluation Budget and Schedule 26 Table 24: WDFW Estimated Juvenile Westslope Cutthroat and Rainbow Trout Abundance Budget and Schedule 27 Table 25: NPS Estimated Cutthroat Trout Spawning Abundance and Genetic Budget and Schedule 28 Table 26: NPS Estimated Stehekin River Kokanee Escapement Budget and Schedule 29 Table 27: WDFW Estimated Kokanee Xxxxx Survey Budget and Schedule 30 Table 28: Summary of Literature 2015 LCFP Expenditures 32 List of Figures Figure 1: 2014 Lake Chelan Catch Compositions 3 Figure 2: 2014 Angler County/State of Origin. 4 Figure 3: Maximum Count of Greater than Six-Inch Trout Observed within Six Mainstem Pool Index Sites, 2011 – 2014. 12 Figure 4: Total Number of Greater than Six-Inch Trout Observed in AVs 2 Table 2 Summary of Literature on Adoption 6 Table 3 Summary of Literature on WTP 8 Table 4 Summary of Literature on Mode Choice 9 Table 5 Summary of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (tEleven-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolvesSide Channel Index Sites, 2011 – 2014. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.14

LIST OF TABLES. Table 1 1: The 6 main constructs of the Health Belief Model 18 Table 2: Search Strategies and Results from Literature Review 22 Table 3: Results of public XXXXX search (Xxxxx & Xxx, 2008 ) 33 Table 4: XXXXX Search Criteria for Surgical Fire Reports 41 Table 5: Surgical Fire Report Counts 46 Table 6: 2008 Surgical Fire Reports Referencing an Oxidizer 47 Table 7: 2008 Surgical Fire Reports Mentioning Skin Preparation Agent 49 Table 8: 2008 Surgical Fire Reports - Reported Outcomes 51 Table 9: 2008 Surgical Fire Burn Reports - Severity of Xxxxx. 53 Table 10: Device Product Codes associated with 2008 Surgical Fire Reports 55 Table 11: 2009 Surgical Fire Reports Referencing an Oxidizer 56 Table 12: 2009 Surgical Fire Reports Referencing a Skin Preparation Agent 58 Table 13: 2009 Surgical Fire Reports - Outcomes 60 Table 14: 2009 Surgical Fire Reports – Burn Severity 62 Table 15: Device Product Codes Associated with 2009 Surgical Fire Reports 64 Table 16: Summary of Literature in AVs 2 Surgical Fire Reports Submitted to FDA’s XXXXX database 65 Table 2 Summary 17: Comparison of Literature on Adoption 6 Table 3 Summary XXXXX results to State data and National Estimates 74 Introduction and Statement of Literature on WTP 8 Table 4 Summary and Context for the Problem Purpose statement The purpose of Literature on Mode Choice 9 Table 5 Summary this study is to identify the number, and severity, of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed surgical fire adverse event reports submitted to the quick expansion U.S. Food and Drug Administration’s (FDA's) Manufacturer and User Facility Device Experience (XXXXX) database over a two year period from January 1, 2008 through December 31, 2009. This information will be used to support FDA’s Preventing Surgical Fires Initiative (xxx.xxx.xxx/xxxxxxxxxxxxxxxxxxxxxxx) and may be used in public communications and to answer press inquiries. Introduction and rationale The Institute of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle Medicine (AVIOM) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance estimates that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented many as 98,000 people die each year in the first phase United States due to preventable medical errors (Institute of this research effortMedicine, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES1999). The nationwide survey engaged morbidity and mortality associated with these errors, coupled with the fact that they are preventable, makes their study a worthwhile public health endeavor. Of particular interest to the author1, and to FDA, is the preventable medical error of surgical fires. These are fires that occur on, in, or in carefully designed choice experiments close proximity to measure the likelihood and extent of behavioral changesa patient undergoing a medical procedure. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspectsSurgical fires can result in serious injury, including 2nd or 3rd degree xxxxx. Some of these xxxxx may lead to permanent scarring or disfigurement. They can also result in death, primarily in cases where the willingness fire occurs in the patient’s airway. 1 The author is currently employed by FDA. Figure 1: Surgical Fire Depiction (FDA, 2011) Surgical fires resulting in injury are believed to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automationbe relatively rare, and attitudes and perceptions toward mobility optionsit is thought that the majority of fires are put out before the patient is injured. Using these survey dataHowever, this study intends to investigate those that do occur have a significant financial cost associated with them. A review of closed malpractice claims for monitored anesthesia care (1990-2002) in the factors American Society of Anesthesiologists closed claims database revealed that influence people’s mobility choice behavior facing emerging mobility optionspayment was made in 89% of on- patient fires, with a focus on exploring median payment of $71, 375 (Xxxxxxxxx et al., 2006). Another closed claims review noted that payment was made in 100% of airway fires, which also had the role highest median payment of user attitudes and perceptions$167, 500 (Kressin, Xxxxxx, Xxx, Xxxxxx, & Domino, 2004). Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude In terms of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodologyindirect costs, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoptionpatients with additional injuries may have additional hospitalization time, and mode choice behaviorlost work hours. Then recommendations on how to incorporate ACES considerations into Additionally, the modeling framework are presented in surgical team and healthcare facility suffer the next chaptereffects of negative publicity, loss of equipment and procedure space, and time spent reporting and preparing for court depositions instead of caring for patients (Xxxxxxx, 2004). The last chapter summarizes Furthermore, there may be increased oversight from hospital accreditation agencies such as the study with major findings Joint Commission 2 or the Centers for Medicare and conclusionsMedicaid (Hart, Yajnik, Ashford, Springer, & Xxxxxx, 2011).

LIST OF TABLES. Table 1 1: The 6 main constructs of the Health Belief Model 18 Table 2: Search Strategies and Results from Literature Review 22 Table 3: Results of public XXXXX search (Xxxxx & Xxx, 2008 ) 33 Table 4: XXXXX Search Criteria for Surgical Fire Reports 41 Table 5: Surgical Fire Report Counts 46 Table 6: 2008 Surgical Fire Reports Referencing an Oxidizer 47 Table 7: 2008 Surgical Fire Reports Mentioning Skin Preparation Agent 49 Table 8: 2008 Surgical Fire Reports - Reported Outcomes 51 Table 9: 2008 Surgical Fire Burn Reports - Severity of Xxxxx. 53 Table 10: Device Product Codes associated with 2008 Surgical Fire Reports 55 Table 11: 2009 Surgical Fire Reports Referencing an Oxidizer 56 Table 12: 2009 Surgical Fire Reports Referencing a Skin Preparation Agent 58 Table 13: 2009 Surgical Fire Reports - Outcomes 60 Table 14: 2009 Surgical Fire Reports – Burn Severity 62 Table 15: Device Product Codes Associated with 2009 Surgical Fire Reports 64 Table 16: Summary of Literature in AVs 2 Surgical Fire Reports Submitted to FDA’s XXXXX database 65 Table 2 Summary 17: Comparison of Literature on Adoption 6 Table 3 Summary XXXXX results to State data and National Estimates 74 Introduction and Statement of Literature on WTP 8 Table 4 Summary and Context for the Problem Purpose statement The purpose of Literature on Mode Choice 9 Table 5 Summary this study is to identify the number, and severity, of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed surgical fire adverse event reports submitted to the quick expansion U.S. Food and Drug Administration’s (FDA's) Manufacturer and User Facility Device Experience (XXXXX) database over a two year period from January 1, 2008 through December 31, 2009. This information will be used to support FDA’s Preventing Surgical Fires Initiative (xxx.xxx.xxx/xxxxxxxxxxxxxxxxxxxxxxx) and may be used in public communications and to answer press inquiries. Introduction and rationale The Institute of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle Medicine (AVIOM) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance estimates that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented many as 98,000 people die each year in the first phase United States due to preventable medical errors (Institute of this research effortMedicine, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES1999). The nationwide survey engaged morbidity and mortality associated with these errors, coupled with the fact that they are preventable, makes their study a worthwhile public health endeavor. Of particular interest to the author1, and to FDA, is the preventable medical error of surgical fires. These are fires that occur on, in, or in carefully designed choice experiments close proximity to measure the likelihood and extent of behavioral changesa patient undergoing a medical procedure. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspectsSurgical fires can result in serious injury, including 2nd or 3rd degree xxxxx. Some of these xxxxx may lead to permanent scarring or disfigurement. They can also result in death, primarily in cases where the willingness fire occurs in the patient’s airway. 1 The author is currently employed by FDA. Figure 1: Surgical Fire Depiction (FDA, 2011) Surgical fires resulting in injury are believed to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automationbe relatively rare, and attitudes and perceptions toward mobility optionsit is thought that the majority of fires are put out before the patient is injured. Using these survey dataHowever, this study intends to investigate those that do occur have a significant financial cost associated with them. A review of closed malpractice claims for monitored anesthesia care (1990-2002) in the factors American Society of Anesthesiologists closed claims database revealed that influence people’s mobility choice behavior facing emerging mobility optionspayment was made in 89% of on- patient fires, with a focus on exploring median payment of $71, 375 (Xxxxxxxxx et al., 2006). Another closed claims review noted that payment was made in 100% of airway fires, which also had the role highest median payment of user attitudes and perceptions$167, 500 (Kressin, Posner, Lee, Cheney, & Domino, 2004). Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude In terms of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodologyindirect costs, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoptionpatients with additional injuries may have additional hospitalization time, and mode choice behaviorlost work hours. Then recommendations on how to incorporate ACES considerations into Additionally, the modeling framework are presented in surgical team and healthcare facility suffer the next chaptereffects of negative publicity, loss of equipment and procedure space, and time spent reporting and preparing for court depositions instead of caring for patients (Xxxxxxx, 2004). The last chapter summarizes Furthermore, there may be increased oversight from hospital accreditation agencies such as the study with major findings Joint Commission 2 or the Centers for Medicare and conclusionsMedicaid (Hart, Yajnik, Ashford, Springer, & Xxxxxx, 2011).

LIST OF TABLES. Table 1 Summary of Literature in AVs 2 1. Time Horizon Based on Age When Child First Receives Teledentistry Services 31 Table 2 Summary of Literature on Adoption 6 2. Tangible Costs and Descriptions 32 Table 3 Summary of Literature on WTP 8 3. Data Sources Used to Obtain Costs 34 Table 4 Summary of Literature on Mode Choice 9 4. Teledentistry Costs 42 Table 5 Summary of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 5. Traditional Dentistry Costs 44 Table 14 Result of Structural Equations 6. Traditional Dentistry Dental Service Charges 45 Table 15 Result 7. Annuitization Values for Capital Equipment 46 Table 8. Average Cost-Effectiveness Ratios 48 Table 9. MCER Teledentistry Costs 50 LIST OF FIGURES Figure 1. 2006 Dental Expenditures 3 Figure 2. Decision Analysis Tree 52 Figure 3. Decision Analysis Tree with Expected Value Results, without Intangible Cost 54 Figure 4. Decision Analysis Tree with Expected Value Results, with Intangible Cost 55 Figure 5. Tornado Diagram without Intangible Cost 57 Figure 6. One-Way Sensitivity Analysis-Probability of Measurement Equations 50 Table 16 Result Getting Teledentistry without Intangible Cost 58 Figure 7. One-Way Sensitivity Analysis probability of Structural Model 52 Table 17 Getting Teledentistry Intangible Cost 59 Figure 8. Tornado Diagram with Intangible Cost 60 Figure 9. One-Way Sensitivity Analysis-Probability of Getting Traditional Dentistry with Intangible Cost 61 Figure 10. One-Way Sensitivity Analysis-Cost of Getting Traditional Dentistry with Intangible Cost 62 Figure 11. One-Way Sensitivity Analysis-Cost of Getting Teledentistry with Intangible Cost 63 Figure 12. Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Parent Survey Question #1 64 Figure 13. Results for Regular Trips (t-ratios in brackets) of Parent Survey Question #2a 65 Figure 14. Results of Parent Survey Question #3 66 Figure 15. Results of Parent Survey Question #4 67 Figure 16. Results of Parent Survey Question #5 67 Figure 17. Results of Parent Survey Question #6 68 Table 21 Mode Choice Model Figure 18. Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 Parent Survey Question #7 69 CHAPTER 1: INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed Public Health Problem According to the quick expansion Centers for Disease Control and Prevention (CDC) more than one- quarter of car sharingchildren ages two to five and one-half of children ages twelve to nineteen are affected by tooth decay (Centers for Disease Control and Prevention [CDC], ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES2010a). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood In Georgia, more than half of all third-grade students have dental caries and extent more than a quarter of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspectsthese students have tooth decay that is untreated (Georgia Department of Human Resources [GDHR], including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions2007).

LIST OF TABLES. Table 1 Summary of Literature in AVs 2 Median Age: Auglaize, Darke and Xxxxxx Counties, Ohio 13 Table 2 Summary of Literature on Adoption 6 Population and Percent Change, 1950 – 2000 14 Table 3 Summary of Literature on WTP 8 Population and Percent Change 1990 – 2000 17 Table 4 Summary of Literature on Mode Choice 9 Education Indicators: Auglaize, Darke and Xxxxxx Counties, Ohio 19 Table 5 Summary of Literature on Benefit Employment and Concerns 10 Income Indicators: Auglaize, Darke and Xxxxxx Counties, Ohio 19 Table 6 Summary Economic Impact of Literature on Perception of Technology Tourism in Auglaize and Operations 12 Xxxxxx Counties 22 Table 7 Summary of Literature on Travel Demand 14 Rare & Endangered Species, Upper Wabash Watershed 40 Table 8 Classification Ohio EPA Division of Studies by Detailed Approach 16 Surface Water: Regulated Point Sources 62 Table 9 PCA result Land Use/Land Cover for AT1 (preferences for lifestyle and mobility options) 22 Grand Lake/Wabash Watershed 63 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 General Farm Structure 66 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 2005 Commodity Production Rankings 66 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Coldwater Creek Acreage 82 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Coldwater Creek Riparian Corridor Status 85 Table 14 Result of Structural Equations 45 Coldwater Creek Operations and Animal Units 87 Table 15 Result of Measurement Equations 50 Coldwater Creek Manure and Nutrient Production 89 Table 16 Result of Structural Model 52 Coldwater Creek Livestock Operations and Proximity to Streams 90 Table 17 Results of Factor Analysis for Mode Dependency 56 Coldwater Creek NPS Pollution Potential 92 Table 18 Linear SVM Model Performances 57 Grassy/Monroe Creeks Acreage 96 Table 19 Linear SVM Model Coefficients 59 Grassy/Monroe Creeks Riparian Corridor Status 98 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Grassy/Monroe Creeks Operations and Animal Units 100 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Grassy/Monroe Creeks Manure and Nutrient Production 102 Table 22 Identified Latent Attitude Factors 72 Grassy/Monroe Creeks Livestock Operations and Proximity to Streams103 Table 23 Model Results for Transit Users 74 Grassy/Monroe Creeks NPS Pollution Potential 105 Table 24 Model Results for Car Users 76 Beaver Creek Acreage 109 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Beaver Creek Riparian Corridor Status 111 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Beaver Creek Operations and Animal Units 113 Table 27 Beaver Creek Manure and Nutrient Production 115 Table 28 Beaver Creek Livestock Operations and Proximity to Streams 116 Table 29 Beaver Creek NPS Pollution Potential Model Changes 118 Table 30 Prairie Creek Acreage 122 Table 31 Prairie Creek Riparian Corridor Status 124 Table 32 Prairie Creek Operations and Animal Units 126 Table 33 Prairie Creek Manure and Nutrient Production 128 Table 34 Prairie Creek Livestock Operations and Proximity to Streams 129 Table 35 Prairie Creek NPS Pollution Potential 131 Table 36 Chickasaw Creek Acreage 135 Table 37 Chickasaw Creek Riparian Corridor Status 137 Table 38 Chickasaw Creek Operations and Animal Units 139 Table 39 Chickasaw Creek Manure and Nutrient Production 141 Table 40 Chickasaw Creek Livestock Operations and Proximity to Streams 142 LIST OF TABLES (continued) Table 41 Chickasaw Creek NPS Pollution Potential 144 Table 42 Xxxxxx Creek Acreage 148 Table 43 Xxxxxx Creek Riparian Corridor Status 150 Table 44 Xxxxxx Creek Operations and Animal Units 152 Table 45 Xxxxxx Creek Manure and Nutrient Production 154 Table 46 Xxxxxx Creek Livestock Operations and Proximity to Streams 155 Table 47 Xxxxxx Creek NPS Pollution Potential 157 Table 48 North Lake Shore Acreage 161 Table 49 North Lake Shore Riparian Corridor Status 163 Table 50 North Lake Shore Operations and Animal Units 165 Table 51 North Lake Shore Manure and Nutrient Production 167 Table 52 North Lake Shore Livestock Operations and Proximity to Streams 168 Table 53 North Lake Shore NPS Pollution Potential 170 Table 54 Wabash River Headwaters to below Bear Creek Acreage 174 Table 55 Wabash River Headwaters to below Bear Creek Riparian Corridor Status 176 Table 56 Wabash River Headwaters to below Bear Creek Operations and Animal Units 178 Table 57 Wabash River Headwaters to below Bear Creek Manure and Nutrient Production 180 Table 58 Wabash River Headwaters to below Bear Creek Livestock Operations and Proximity to Streams 181 Table 59 Wabash River Headwaters to below Bear Creek NPS Pollution Potential 183 Table 60 Wabash River above Bear Creek below Stony Creek Acreage 187 Table 61 Wabash River above Bear Creek below Stony Creek Riparian Corridor Status 189 Table 62 Wabash River above Bear Creek below Stony Creek Operations and Animal Units 191 Table 63 Wabash River above Bear Creek below Stony Creek Manure and Nutrient Production 193 Table 64 Wabash River above Bear Creek below Stony Creek Livestock Operations and Proximity to Streams 194 Table 65 Wabash River above Bear Creek below Stony Creek NPS Pollution Potential 196 Table 66 Wabash River below Stony Creek above Beaver Creek Acreage 200 Table 67 Wabash River below Stony Creek above Beaver Creek Riparian Corridor Status 202 Table 68 Wabash River below Stony Creek above Beaver Creek Operations and Animal Units 204 Table 69 Wabash River below Stony Creek above Beaver Creek Manure and Nutrient Production 206 Table 70 Wabash River below Stony Creek above Beaver Creek Livestock Operations and Proximity to Streams 207 LIST OF TABLES (continued) Table 71 Wabash River below Stony Creek above Beaver Creek NPS Pollution Potential 209 Table 72 Beaver Creek from Grand Lake to above Little Beaver Creek Acreage 213 Table 73 Beaver Creek from Grand Lake to above Little Beaver Creek Riparian Corridor Status 215 Table 74 Beaver Creek from Grand Lake to above Little Beaver Creek Operations and Animal Units 217 Table 75 Beaver Creek from Grand Lake to above Little Beaver Creek Manure and Nutrient Production 219 Table 76 Beaver Creek from Grand Lake to above Little Beaver Creek Livestock Operations and Proximity to Streams 220 Table 77 Beaver Creek from Grand Lake to above Little Beaver Creek NPS Pollution Potential 222 Table 78 Little Beaver Creek Acreage 226 Table 79 Little Beaver Creek Riparian Corridor Status 228 Table 80 Little Beaver Creek Operations and Animal Units 230 Table 81 Little Beaver Creek Manure and Nutrient Production 232 Table 82 Little Beaver Creek Livestock Operations and Proximity to Streams 233 Table 83 Little Beaver Creek NPS Pollution Potential 235 Table 84 Beaver Creek below Little Beaver Creek to Wabash River Acreage 239 Table 85 Beaver Creek below Little Beaver Creek to Wabash River Riparian Corridor Status 241 Table 86 Beaver Creek below Little Beaver Creek to Wabash River Operations and Animal Units 242 Table 87 Beaver Creek below Little Beaver Creek to Wabash River Manure and Nutrient Production 245 Table 88 Beaver Creek below Little Beaver Creek to Wabash River Livestock Operations and Proximity to Streams 246 Table 89 Beaver Creek below Little Beaver Creek to Wabash River NPS Pollution Potential 248 Table 90 Wabash River below Beaver Creek to New Corydon Acreage 252 Table 91 Wabash River below Beaver Creek to New Corydon Riparian Corridor Status 254 Table 92 Wabash River below Beaver Creek to New Corydon Operations and Animal Units 256 Table 93 Wabash River below Beaver Creek to New Corydon Manure and Nutrient Production 258 Table 94 Wabash River below Beaver Creek to New Corydon Livestock Operations and Proximity to Streams 259 Table 95 Wabash River below Beaver Creek to New Corydon NPS Pollution Potential 261 Table 96 Limberlost Creek Headwaters to below Bull Creek (IN) Acreage 265 Table 97 Limberlost Creek Headwaters to below Bull Creek (IN) Riparian Corridor Status 267 LIST OF TABLES (continued) Table 98 Limberlost Creek Headwaters to below Bull Creek (IN) Operations and Animal Units 269 Table 99 Limberlost Creek Headwaters to below Bull Creek (IN) Manure and Nutrient Production 271 Table 100 Limberlost Creek Headwaters to below Bull Creek (IN) Livestock Operations and Proximity to Streams 272 Table 101 Limberlost Creek Headwaters to below Bull Creek (IN) NPS Pollution Potential 274 Table 102 05120101-020-010 - Chickasaw and Xxxxxx Creeks 316 Table 103 05120101-020-020 – Coldwater and Beaver Creeks 317 Table 104 05120101-020-030 – North Shore/Grassy/Monroe/Prairie Creeks 318 Table 105 05120101-020-010 – Xxxxxx Creek 319 Table 106 05120101-020-020 – Beaver Creek 320 Table 107 05120101-020-010 – Chickasaw Creek 321 Table 108 05120101-020-020 – Coldwater Creek 322 Table 109 05120101-020-030 – Grassy & Monroe Creeks 323 Table 110 05120101-020-030 – Prairie Creek 324 Table 111 05120101-020-030 – North Lake Shore 325 Table 112 05120101-010-010 – Wabash Headwaters to below Bear Creek 326 Table 113 05120101-010-020 – Wabash R. above Bear Cr. below Stony Creek 327 Table 114 05120101-010-030 – Wabash R. below Stony Cr. above Xxxxxx Xx 328 Table 115 05120101-030-010 – Xxxxxx Xx. from Grand Lake to above Little Beaver Creek 329 Table 116 05120101-030-020 – Little Beaver Creek 330 Table 117 05120101-030-030 – Xxxxxx Xx. below Little Beaver to Wabash R. 331 Table 118 05120101-040-010 – Wabash R. below Little Beaver to New Corydon 332 Table 119 05120101-050-050 – Limberlost Cr. Headwaters to below Bull Cr 333 LIST OF FIGURES Figure 1 Mercer County Employment Statistics 20 Figure 2 Auglaize County Employment Statistics 21 Figure 3 Darke County Employment Statistics 21 Figure 4 Average monthly precipitation 1956 – 1995 46 LIST OF APPENDICES Appendix A Riparian Corridor Status for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Advances in information Grand Lake/Wabash River Watershed Appendix B Operational Documents Appendix C Aquatic Life Use Designations Appendix D List of Abbreviations and communication technologies have played an important role in how we live Acronyms Appendix E Maps and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharingInfographs Appendix F Work Plan January 2008 through December 2008 Appendix G Home Sewage Treatment System Plan, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle Mercer County Appendix H Local Endorsement Signature Pages Appendix I Auglaize County Ditch Maintenance Appendix J Wabash Conservancy District Maintenance Appendix K Sediment Trap Monitoring Chart Appendix L Amendments (AVreserved) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.EXECUTIVE SUMMARY