The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.     

基于CAViaR模型的汇率隔夜风险研究

针对目前缺乏美元当日汇率对其他汇率市场隔夜风险影响的研究,本文在CAViaR模型中的AS模型和SAV模型基础上提出隔夜-AS模型和隔夜-SAV模型来测量汇率隔夜风险,并对日元汇率,人民币汇率和港币汇率2009年到2014年的数据进行实证分析,研究结果表明隔夜-AS模型和隔夜-SAV模型均优于AS模型和SAV模型,且隔夜-AS模型又优于隔夜-SAV模型。这三个汇率的隔夜风险均受到滞后风险的影响,且人民币汇率所受滞后风险最大,美元指数的波动都将加大这三个汇率市场的隔夜风险,美元对日元和港币汇率的冲击大于对人民币汇率的冲击,美元走弱对这三个市场隔夜风险影响大于美元走强所带来的影响,这些都为我国汇率隔夜风险的管理提供了新的方法和思路。     

sCARy! Risk Perceptions in Autonomous Driving: The Influence of Experience on Perceived Benefits and Barriers

The increasing development of autonomous vehicles (AVs) influences the future of transportation. Beyond the potential benefits in terms of safety, efficiency, and comfort, also potential risks of novel driving technologies need to be addressed. In this article, we explore risk perceptions toward connected and autonomous driving in comparison to conventional driving. In order to gain a deeper understanding of individual risk perceptions, we adopted a two‐step empirical procedure. First, focus groups ( $N=17$) were carried out to identify relevant risk factors for autonomous and connected driving. Further, a questionnaire was developed, which was answered by 516 German participants. In the questionnaire, three driving technologies (connected, autonomous, conventional) were evaluated via semantic differential (rating scale to identify connotative meaning of technologies). Second, participants rated perceived risk levels (for data, traffic environment, vehicle, and passenger) and perceived benefits and barriers of connected/autonomous driving. Since previous experience with automated functions of driver assistance systems can have an impact on the evaluation, three experience groups have been formed. The effect of experience on benefits and barrier perceptions was also analyzed. Risk perceptions were significantly smaller for conventional driving compared to connected/autonomous driving. With increasing experience, risk perception decreases for novel driving technologies with one exception: the perceived risk in handling data is not influenced by experience. The findings contribute to an understanding of risk perception in autonomous driving, which helps to foster a successful implementation of AVs on the market and to develop public information strategies.     

Exposure to Traffic and Risk of Hospitalization Due to Injuries

Research on the risk of motor vehicle injuries and their relationship with the amount of travel has been only partially analyzed. The few individual exposure assessments are related to very specific subsets of the driving and traveling populations. This study analyzes the relationship between kilometers traveled and hospitalization due to motor vehicle injuries. Twelve thousand three hundred and sixty nine Spanish university graduates from the Seguimiento Universidad de Navarra multipurpose cohort study were evaluated. They had not been hospitalized due to motor vehicle injuries at baseline and were followed up to eight years. Biannual questionnaires allowed for self‐reporting of kilometers traveled in motor vehicles, together with incidence of hospitalization. Covariates in the Cox regression models included age and gender and baseline use of safety belt while driving, driving a vehicle with driver‐side airbag, driving a motorcycle, and drinking and driving. There were 49,766 participant‐years with an average yearly travel of 7,828 km per person‐year. Thirty‐six subjects reported a first hospitalization event during this time. The adjusted hazard ratio per additional kilometer traveled was 1.00005 (95% confidence interval 1.000013 to 1.000086). Even the smallest of reductions in the amount of kilometers traveled (from an average of 3,250 km per year to 1,000) has a statistically significant protective effect on the likelihood of sustaining hospitalization due to motor vehicle injury (aHR 0.9, 95% CI 0.78 to 0.98). In light of current policies aimed to reduce motorized traffic due to environmental concerns, it may be appropriate to consider the additional health benefit related to reductions in injuries.     

Radiological Risk Assessment by Convergence Methodology Model in RDD Scenarios

A radiological dispersal device (RDD) is a simple weapon capable of causing human harm, environmental contamination, disruption, area denial, and economic cost. It can affect small, large, or long areas depending on atmospheric stability. The risk of developing a radio‐induced cancer depends on exposure, and an effective response depends upon available timely guidance. This article proposes and demonstrates a convergence of three different capabilities to assess risk and support rapid safe resource efficient response. The three capabilities that are integrated are Hotspot for dispersion, RERF for epidemiological risk, and RESRAD‐RDD for response guidance. The combined methodology supports decisions on risk reduction and resource allocation through work schedules, the designation and composition of response teams, and siting for operations. In the illustrative RDD scenario, the contamination area for sheltering, evacuation, and long‐term public concern was greatest for calm atmospheric conditions, whilst close‐quarter responders faced highest dose rates for neutral atmospheric conditions. Generally, the risks to women responders were found to be significantly greater than for men, and the risks to 20‐year‐old responders were three times that of their 60‐year‐old counterparts for similar exposure.     

Vehicle Emission Unit Risk Factors for Transportation Risk Assessments

When the transportation risk posed by shipments of hazardous chemical and radioactive materials is being assessed, it is necessary to evaluate the risks associated with both vehicle emissions and cargo-related risks. Diesel exhaust and fugitive dust emissions from vehicles transporting hazardous shipments lead to increased air pollution, which increases the risk of latent fatalities in the affected population along the transport route. The estimated risk from these vehicle-related sources can often be as large or larger than the estimated risk associated with the material being transported. In this paper, data from the U.S. Environmental Protection Agency's Motor Vehicle-Related Air Toxics Study are first used to develop latent cancer fatality estimates per kilometer of travel in rural and urban areas for all diesel truck classes. These unit risk factors are based on studies investigating the carcinogenic nature of diesel exhaust. With the same methodology, the current per-kilometer latent fatality risk factor used in transportation risk assessments for heavy diesel trucks in urban areas is revised and the analysis expanded to provide risk factors for rural areas and all diesel truck classes. These latter fatality estimates may include, but are not limited to, cancer fatalities and are based primarily on the most recent epidemiological data available on mortality rates associated with ambient air PM-10 concentrations.     

The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk‐based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano‐specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.     

A Revised Economic Analysis of Restrictions on the Use of Cell Phones While Driving

Evidence that cell phone use while driving increases the risk of being involved in a motor vehicle crash has led policymakers to consider prohibitions on this practice. However, while restrictions would reduce property loss, injuries, and fatalities, consumers would lose the convenience of using these devices while driving. Quantifying the risks and benefits associated with cell phone use while driving is complicated by substantial uncertainty in the estimates of several important inputs, including the extent to which cell phone use increases a driver's risk of being involved in a crash, the amount of time drivers spend using cell phones (and hence their aggregate contribution to crashes, injuries, and fatalities), and the incremental value to users of being able to make calls while driving. Two prominent studies that have investigated cell phone use while driving have concluded that the practice should not be banned. One finds that the benefits of calls made while driving substantially exceed their costs while the other finds that other interventions could reduce motor vehicle injuries and fatalities (measured in terms of quality adjusted life years) at a lower cost. Another issue is that cell phone use imposes increased (involuntary) risks on other roadway users. This article revises the assumptions used in the two previous analyses to make them consistent and updates them using recent data. The result is a best estimate of zero for the net benefit of cell phone use while driving, a finding that differs substantially from the previous study. Our revised cost-effectiveness estimate for cell phone use while driving moves in the other direction, finding that the cost per quality adjusted life year increases modestly compared to the previous estimate. Both estimates are very uncertain.     

A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions

The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.     

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (b iological risk a ssessment with R ) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach

The use of autonomous underwater vehicles (AUVs) for various applications have grown with maturing technology and improved accessibility. The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extremities in the Antarctic. A thorough analysis of risks is therefore crucial for formulating effective risk control policies and achieving a lower risk of loss. Existing risk analysis approaches focused predominantly on the technical aspects, as well as identifying static cause and effect relationships in the chain of events leading to AUV loss. Comparatively, the complex interrelationships between risk variables and other aspects of risk such as human errors have received much lesser attention. In this article, a systems-based risk analysis framework facilitated by system dynamics methodology is proposed to overcome existing shortfalls. To demonstrate usefulness of the framework, it is applied on an actual AUV program to examine the occurrence of human error during Antarctic deployment. Simulation of the resultant risk model showed an overall decline in human error incident rate with the increase in experience of the AUV team. Scenario analysis based on the example provided policy recommendations in areas of training, practice runs, recruitment policy, and setting of risk tolerance level. The proposed risk analysis framework is pragmatically useful for risk analysis of future AUV programs to ensure the sustainability of operations, facilitating both better control and monitoring of risk.     

How Safe Is Safe Enough for Self‐Driving Vehicles?

Self‐driving vehicles (SDVs) promise to considerably reduce traffic crashes. One pressing concern facing the public, automakers, and governments is “How safe is safe enough for SDVs?” To answer this question, a new expressed‐preference approach was proposed for the first time to determine the socially acceptable risk of SDVs. In our between‐subject survey (N = 499), we determined the respondents’ risk‐acceptance rate of scenarios with varying traffic‐risk frequencies to examine the logarithmic relationships between the traffic‐risk frequency and risk‐acceptance rate. Logarithmic regression models of SDVs were compared to those of human‐driven vehicles (HDVs); the results showed that SDVs were required to be safer than HDVs. Given the same traffic‐risk‐acceptance rates for SDVs and HDVs, their associated acceptable risk frequencies of SDVs and HDVs were predicted and compared. Two risk‐acceptance criteria emerged: the tolerable risk criterion, which indicates that SDVs should be four to five times as safe as HDVs, and the broadly acceptable risk criterion, which suggests that half of the respondents hoped that the traffic risk of SDVs would be two orders of magnitude lower than the current estimated traffic risk. The approach and these results could provide insights for government regulatory authorities for establishing clear safety requirements for SDVs.     

Assessing Potential Human Health Hazards and Benefits from Subtherapeutic Antibiotics in the United States: Tetracyclines as a Case Study

Many scientists, activists, regulators, and politicians have expressed urgent concern that using antibiotics in food animals selects for resistant strains of bacteria that harm human health and bring nearer a “postantibiotic era” of multidrug resistant “super‐bugs.” Proposed political solutions, such as the Preservation of Antibiotics for Medical Treatment Act (PAMTA), would ban entire classes of subtherapeutic antibiotics (STAs) now used for disease prevention and growth promotion in food animals. The proposed bans are not driven by formal quantitative risk assessment (QRA), but by a perceived need for immediate action to prevent potential catastrophe. Similar fears led to STA phase‐outs in Europe a decade ago. However, QRA and empirical data indicate that continued use of STAs in the United States has not harmed human health, and bans in Europe have not helped human health. The fears motivating PAMTA contrast with QRA estimates of vanishingly small risks. As a case study, examining specific tetracycline uses and resistance patterns suggests that there is no significant human health hazard from continued use of tetracycline in food animals. Simple hypothetical calculations suggest an unobservably small risk (between 0 and 1.75E‐11 excess lifetime risk of a tetracycline‐resistant infection), based on the long history of tetracycline use in the United States without resistance‐related treatment failures. QRAs for other STA uses in food animals also find that human health risks are vanishingly small. Whether such QRA calculations will guide risk management policy for animal antibiotics in the United States remains to be seen.     

Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment

This study presents a tree‐based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M‐94\I‐94\I‐94BL\I‐94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.     

考虑充电策略与电池损耗的电动汽车路径优化问题研究

针对目前研究电动物流车辆路径问题的文章未考虑电池损耗对运营成本的影响,且多在充电速率为恒定值的情况下对充电策略进行优化,本文将电动物流车辆在配送货物途中的充电时间和电池损耗成本纳入目标函数并建立了线性规划数学模型,统筹安排车辆行驶路径和充电策略使得物流企业整体运营成本最低。其次,提出了求解该问题的多阶段启发式算法MCWIGALNS。随后,通过多组算例验证了模型和算法的准确性。实验结果表明,考虑充电时间与深度放电成本的模型可以在配送距离不变或略有增加的情况下,较大幅度减少充电时间与电池损耗成本,到达降低运营成本的目的。最后,将算法实验结果与本领域已发表的成果进行比较,证明了MCWIGALNS算法对车辆路径问题具有出色的求解能力,提升了该问题理论成果的实用性。可以为物流企业电动汽车路径策略提供良好借鉴与帮助。     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

国际油价、宏观经济变量与中国股市的尾部风险溢出效应研究

本文首先基于Massacci (2017)提出的时变POT模型、应用纯时间序列方法测度动态尾部风险,再利用Diebold和Yilmaz (2012, 2014)提出的溢出指数模型,结合滚动样本估计方法,从方向、大小和动态性的角度考察中国股票市场与宏观经济体系之间的尾部风险溢出效应。研究结果表明,中国股票市场与宏观经济变量之间存在显著的尾部风险溢出效应,总溢出指数在危机时期显著提高。股票市场对宏观经济的方向性尾部风险溢出效应要弱于其接收自宏观经济体系的冲击,是系统中最大的尾部风险净接收者。其中,国际原油价格、货币政策的极端变动等均对股市尾部风险水平产生重要影响,是中国股票市场尾部风险的重要来源。此外,我国经济政策不确定性指数与股票市场之间的尾部风险溢出效应正逐渐增强。因此,在经济转型的关键时期,政策制定者和监管当局应特别关注经济政策不确定性对金融市场和实体经济的负面影响。     

Safety Impacts of SUVs, Vans, and Pickup Trucks in Two-Vehicle Crashes

Policy makers, vehicle manufacturers, and consumers have shown growing concern about the relative safety of sport utility vehicles (SUVs), vans, pickups, and cars. Empirical analysis of real-world crashes is complicated by the possibility that apparent relationships between vehicle type and safety may be confounded by other factors, such as driver behavior and crash circumstances. This study compares different vehicle types with respect to their crashworthiness (self-protection) and aggressivity (risk to others) in crashes between two passenger vehicles. The U.S. Crashworthiness Data System is used to analyze detailed information on 6,481 drivers involved in crashes during 1993-1999. Logistic regression analysis is used to model the risk of serious injury or death to a driver, conditional on a crash occurring. Covariates include the body type of each vehicle in the crash; the driver's age, gender, and restraint use; and the configuration of the crash. A unique feature of this study is the use of "delta-v" to represent the joint effects of vehicle mass and crash severity. While estimated effects are somewhat sensitive to the injury severity level used as the outcome variable, SUVs, vans, and pickups appear to be more aggressive and may be more crashworthy than cars. Effects of pickups are most pronounced. Drivers in pickups face less risk of serious injury than car drivers (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.20-0.60), and drivers who collide with pickups experience more than twice the risk than those who collide with a car (OR, 2.18; 95% CI, 1.03-4.62). While vehicle mass and crash severity contribute to the apparent crashworthiness and aggressivity of passenger vehicles, other vehicle characteristics associated with body type (e.g., the stiffness and height of the underlying structure of the vehicle) also influence safety risks.     

Shannon Entropy for Quantifying Uncertainty and Risk in Economic Disparity

The rise in economic disparity presents significant risks to global social order and the resilience of local communities. However, existing measurement science for economic disparity (e.g., the Gini coefficient) does not explicitly consider a probability distribution with information, deficiencies, and uncertainties associated with the underlying income distribution. This article introduces the quantification of Shannon entropy for income inequality across scales, including national‐, subnational‐, and city‐level data. The probabilistic principles of Shannon entropy provide a new interpretation for uncertainty and risk related to economic disparity. Entropy and information‐based conflict rise as world incomes converge. High‐entropy instances can resemble both happy and prosperous societies as well as a socialist–communist social structure. Low entropy signals high‐risk tipping points for anomaly and conflict detection with higher confidence. Finally, spatial–temporal entropy maps for U.S. cities offer a city risk profiling framework. The results show polarization of household incomes within and across Baltimore, Washington, DC, and San Francisco. Entropy produces reliable results at significantly reduced computational costs than Gini coefficients.