Risk Analysis of Safety Service Patrol (SSP) Systems in Virginia

The transportation infrastructure is a vital backbone of any regional economy as it supports workforce mobility, tourism, and a host of socioeconomic activities. In this article, we specifically examine the incident management function of the transportation infrastructure. In many metropolitan regions, incident management is handled primarily by safety service patrols (SSPs), which monitor and resolve roadway incidents. In Virginia, SSP allocation across highway networks is based typically on average vehicle speeds and incident volumes. This article implements a probabilistic network model that partitions “business as usual” traffic flow with extreme‐event scenarios. Results of simulated network scenarios reveal that flexible SSP configurations can improve incident resolution times relative to predetermined SSP assignments.     

基于韧性城市视角的冰雪天气下路网恢复问题研究

城市的快速发展使其愈加依赖于生命线基础设施系统,城市在自然或人为突发事件面前的脆弱性日益凸显,城市面对突发事件后的运行与恢复问题受到广泛关注。冬季极端冰雪天气对城市路网系统带来极大冲击,严重降低路网服务能力。本文基于韧性城市视角,对冰雪天气下城市路网韧性的概念和度量方法进行了分析。以提升路网韧性为目标,建立冰雪天气下路网恢复问题的数学模型,解决极端冰雪天气不确定信息下的城市路网除雪应急物资布局问题及其除雪作业优化问题,并设计了相应的启发式求解算法。最后通过算例验证了模型和算法的有效性,以期为城市冰雪天气应对提供决策支持,提升城市应对极端冰雪天气的韧性。     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

Organizational factors affecting the incident reporting of train drivers

The under-reporting of accidents and incidents is well established as a substantial problem across industries; however, few studies have specifically examined employees' reasons for failing to report. This study investigates the factors affecting incident reporting by train drivers (n = 128) in three different areas of British Rail (the company that operated the railway network in Britain, prior to its privatization in 1994). Drivers completed a questionnaire rating their likelihood of reporting a number of hazardous incidents and giving their reasons for not reporting them. Intention not to report incidents was predicted by 'managers take no notice' of reports and the interaction between 'just part of the day's work' and 'nothing would get done'. It is concluded that incident reporting is most influenced by the way that drivers perceive managers' reactions to reports. In addition, the three areas differed significantly in their likelihood of reporting incidents, suggesting that the local subculture has an important influence on intentions to report, with negative perceptions of managers' attitudes suppressing the reporting of incidents.     

Geographic Distributions of Extreme Weather Risk Perceptions in the United States

Weather and climate disasters pose an increasing risk to life and property in the United States. Managing this risk requires objective information about the nature of the threat and subjective information about how people perceive it. Meteorologists and climatologists have a relatively firm grasp of the historical objective risk. For example, we know which parts of the United States are most likely to experience drought, heat waves, flooding, snow or ice storms, tornadoes, and hurricanes. We know less about the geographic distribution of the perceived risks of meteorological events and trends. Do subjective perceptions align with exposure to weather risks? This question is difficult to answer because analysts have yet to develop a comprehensive and spatially consistent methodology for measuring risk perceptions across geographic areas in the United States. In this project, we propose a methodology that uses multilevel regression and poststratification to estimate extreme weather and climate risk perceptions by geographic area (i.e., region, state, forecast area, and county). Then we apply the methodology using data from three national surveys (n = 9,542). This enables us to measure, map, and compare perceptions of risk from multiple weather hazards in geographic areas across the country.     

Correlation of Quantitative Risk Results for High Hazard Processes

A quantitative risk analysis was conducted to evaluate the design of the VX neutralization subsystem and related support facilities of the U.S. Army Newport Chemical Agent Disposal Facility. Three major incidents including agent release, personnel injury, and system loss were studied using fault tree analysis methodology. Each incident was assigned a risk assessment code based on the severity level and probability of occurrence of the incident. Safety mitigations or design changes were recommended to bring the "undesired" risk level (typical agent release events) to be "acceptable with controls" or "acceptable."     

Analytic approaches to problems of a middle-sized defence department

The application of operational research to the problems of Canadian defence was very similar to that in Great Britain and the United States during World War II and through the 1950s. But because of the rapidly increasing costs of major weapon systems, since the 1960s systems analysis has been very thoroughly applied to the study of the large procurement programs, including far-reaching examination of their economic impact on the nation. Other problems of special interest to Canada which received analytical attention included peacekeeping and military involvement in activities such as maritime surveillance and search and rescue. Analytical studies have been extended into examination of logistics and manpower, and into economic, sociological, and strategic problems of concern to the Canadian defence department, including arms control. This type of research represents a considerable excursion from the original operational research, but is needed in the Canadian Department of National Defence.     

Critical incident stress debriefing and its effects on coping strategies and anger in a sample of Australian police officers involved in shooting incidents

This study investigates appraisal and coping behaviours, and symptom and expectation outcomes following a critical incident and Critical Incident Stress Debriefing (CISD). Two groups of 30 Australian police officers from the New South Wales Police Service who had been involved in shooting incidents were examined. One group received CISD and the other did not. The group that received CISD showed a significant reduction in anger levels and greater use of some specific adaptive coping strategies. However, one cannot be certain as to the extent of CISD's contribution to this improvement, since a variety of other factors in the officers' lives, as evidenced by other measures taken throughout the study, may help to explain the variation. Results are discussed in light of the contextual features in officers' lives that impact upon the outcome responses to such incidents.     

Empirical Analysis of the Effects of Cyber Security Incidents

We analyze the time series associated with web traffic for a representative set of online businesses that have suffered widely reported cyber security incidents. Our working hypothesis is that cyber security incidents may prompt (security conscious) online customers to opt out and conduct their business elsewhere or, at the very least, to refrain from accessing online services. For companies relying almost exclusively on online channels, this presents an important business risk. We test for structural changes in these time series that may have been caused by these cyber security incidents. Our results consistently indicate that cyber security incidents do not affect the structure of web traffic for the set of online businesses studied. We discuss various public policy considerations stemming from our analysis.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

基于社会资本的突发事件理论分析

近年来,在全球频发的各种突发事件与社会资本运作空间的失当密不可分。在突发事件的前期,存在着社会资本原状与突发事件前期因子之间的不良互动和媾和,这种状态持续到质变的程度时,又在某种诱发因素的作用下,以积累爆发或扩散爆发等形式表现为突发事件,并产生一系列消极性的社会影响。突发事件发生和扩散的过程同时也是各种社会资本被动性应急调整、资本组织治理的过程。鉴于社会资本在突发事件中的动因作用,积极地改善社会资本的运作状况,消化突发事件的消极影响,改变社会资本误运作和优化社会资本结构的过程,对于减少突发事件的发生和积极的应对,具有十分重要的现实意义。     

Analyzing Collision,Grounding, and Sinking Accidents Occurring in the Black Sea Utilizing HFACS and Bayesian Networks

This study examines and analyzes marine accidents that have occurred over the past 20 years in the Black Sea. Geographic information system, human factor analysis and classification system (HFACS), and Bayesian network models are used to analyze the marine accidents. The most important feature distinguishing this study from other studies is that this is the first study to analyze accidents that have occurred across the whole Black Sea. Another important feature is the application of a new HFACS structure to reveal accident formation patterns. The results of this study indicate that accidents occurred in high concentrations in coastal regions of the Black Sea, especially in the Kerch Strait, Novorossiysk, Kilyos, Constanta, Riva, and Batumi regions. The formation of grounding and sinking accidents has been found to be similar in nature; the use of inland and old vessels has been highlighted as important factors in sinking and grounding incidents. However, the sequence of events leading to collision-contact accidents differs from the sequence of events resulting in grounding and sinking accidents. This study aims to provide information to the maritime industry regarding the occurrence of maritime incidents in the Black Sea, in order to assist with reduction and prevention of the marine accidents.     

Do Faster Rescues Save More Lives

The primary mission of search and rescue (SAR) is the saving of lives. To assess SAR operations from a planning perspective, one must draw a connection between operations and the number of lives saved. Our approach is to model the probability that an incident results in at least one fatality, given the response time between the time of incident occurrence and time of rescue. We show that incidents involving air crashes, capsizing, foundering, grounding and other/unknown types of incidents tended to have higher probabilities of fatalities as the response time became higher. However, other emergency types did not exhibit the same overall tendency as these did. These statistical results do not prove causality between faster response times and lower fatality incidence for the above-mentioned emergency types. They can be used, however, for estimating the average number of fatalities for a given distribution of response time, and ultimately the marginal savings in lives for a change in the mix of resources and locations.     

Wildfire Policy in Mediterranean France: How Far is it Efficient and Sustainable?

A new fire policy reinforcing aggressive fire suppression was established in Mediterranean France in response to the devastating wildfires of the 1990s, but to what extent this has changed fire activity yet remains poorly understood. For this purpose, we compared the number and location of ignitions and of burned areas between two 20‐year periods (1975–1994 vs. 1995–2014), in parallel to the changes in fuel covering, human activity promoting ignitions, and fire weather. The number of fires decreased almost continuously since 1975, but sharply after 1994, suggesting an effect of better fire prevention due to the new policy. But the major change in fire activity is a considerable reduction in fire size and burned areas after 1994, especially during summer and in the most fire‐prone places, in response to massive efforts put into fire suppression. These reductions have occurred while the covering by fuel biomass, the human pressure on ignition, and the fire weather index increased, thus making the study area more hazardous. Our results suggest that a strategy of aggressive fire suppression has great potential for counterbalancing the effects of climate changes and human activities and for controlling fire activity in the short term. However, we discuss whether such a suppression‐oriented approach is sustainable in the context of global changes, which cast new fire challenges as demonstrated by the devastative fires of 2003 and 2016. We advocate for a more comprehensive fire policy to come.     

Risk-Based Ranking of Dominant Contributors to Maritime Pollution Events

This report describes a conceptual approach for identifying dominant contributors to risk from maritime shipping of hazardous materials. Maritime transportation accidents are relatively common occurrences compared to more frequently analyzed contributors to public risk. Yet research on maritime safety and pollution incidents has not been guided by a systematic, risk-based approach. Maritime shipping accidents can be analyzed using event trees to group the accidents into "bins," or groups, of similar characteristics such as type of cargo, location of accident (e.g., harbor, inland waterway), type of accident (e.g., fire, collision, grounding), and size of release. The importance of specific types of events to each accident bin can be quantified. Then the overall importance of accident events to risk can be estimated by weighting the events' individual bin importance measures by the risk associated with each accident bin.     

Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network

This article compares two nonparametric tree‐based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high‐resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2‐km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree‐leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.     

Case study in Six Sigma methodology: manufacturing quality improvement and guidance for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities.     

跨国公司在华专利活动的技术溢出效应

基于技术领域构建了跨国公司专利活动技术溢出分析框架,以跨国公司在华专利活动为研究对象,分析了1987～2007年跨国公司在华专利活动的技术溢出效应.研究发现:跨国公司在华专利活动对中国发明专利具有正向溢出效应,1987～2007年间,跨国公司发明专利技术溢出呈现明显的阶段特征;中外技术差距和竞争程度对跨国公司专利活动技术溢出程度具有显著的影响.     

Analyzing Risk Response Dynamics on the Web: The Case of Hurricane Katrina

Using a unique data set that documented the hourly web-surfing behavior of over 140,000 Internet users in five southeastern states in August 2005, we explore the dynamics of information gathering as Hurricane Katrina developed and then hit South Florida and the Northern Gulf Coast. Using both elementary statistical methods and advanced techniques from functional data analysis,^{( 1 )} we examine both how storm events (such as the posting of warnings) affected traffic to weather-related websites, and how this traffic varied across locations and by characteristics of the web user. A general finding is that spatial-temporal variation in weather-site web traffic generally tracked the timing and scale of the storm threat experienced by a given area. There was, however, considerable variation in this responsiveness. Residents in Florida counties that had been most directly affected by Hurricane Dennis just a month earlier, for example, displayed more active visitation rates than those who had been less affected. We also find evidence of a gender effect where male users displayed a disproportionately larger rate of visitation to weather sites given the onset of storm warnings than females. The implications of this work for the broader study of behavioral risk response dynamics during hazards are explored.