A Methodology for the Design and Creation of Asset Administration Shell for Manufacturing Systems

Within Industry 4.0 the communication between the physical and the cyber part of manufacturing systems is in growing rise in complexity. The Asset Administration Shell (AAS) is an information framework that represents the technological features of an asset. This work addresses the design of AAS by proposing a methodology to guide practitioners through the process of creating AAS models for manufacturing systems, and populating them with real-time data from the field. The aim of the paper is to design a methodology for the creation of AAS that is user friendly and functional to be followed by non-IT experts. The proposed methodology has been applied and validated within the Industry 4.0 Lab of the School of Management of Politecnico Di Milano

Prediction of individual automobile RBNS claim reserves in the context of Solvency II

Automobile bodily injury (BI) claims remain unsettled for a long time after the accident. The estimation of an accurate reserve for Reported But Not Settled (RBNS) claims is therefore vital for insurers. In accordance with the recommendation included in the Solvency II project (CEIOPS, 2007) a statistical model is here implemented for RBNS reserve estimation. Lognormality on empirical compensation cost data is observed for different levels of BI severity. The individual claim provision is estimated by allocating the expected mean compensation for the predicted severity of the victim’s injury, for which the upper bound is also computed. The BI severity is predicted by means of a heteroscedastic multiple choice model, because empirical evidence has found that the variability in the latent severity of injured individuals travelling by car is not constant. It is shown that this methodology can improve the accuracy of RBNS reserve estimation at all stages, as compared to the subjective assessment that has traditionally been made by practitioners.Automobile accident, Solvency II, bodily injury claims, individual RBNS reserve..

Occupational Vehicular Accidents: A Workers' Compensation Analysis of Oregon Truck Drivers 1990-1997

This study used workers' compensation data from Oregon from 1990-1997 to examine injuries due to vehicular accidents by truck drivers, and calculate rate estimates using baseline data derived from the U.S. Bureau of Census' Current Population Survey. During this period, 1,168 valid injury claims due to vehicular accidents were filed representing an accident rate of 50.3% (95% C.I. = 45.1-55.5) per 10,000 truck drivers annually. There were 19 work-related vehicular accident fatalities recorded in the data over the 8-year period. Of all claimants, males constituted the majority (80.7%), most were 35 years of age or younger (51.4%) and had less than 1 year of job tenure (51.0%). Truck driver injury rates due to vehicular accidents were lowest during the 6:00 A.M. - 12:00 P.M. period. The average amount of compensable lost workdays per injury claim was 57.8 days, of which male claimants lost an average of 60.5 days of work and females lost an average of 46.9 days of work. The amount of lost work days due to vehicular accident increased with the claimant's age. A total of $11,642,635 was paid in claims for vehicular accidents of truck drivers in Oregon over the period examined, averaging$9,966.01 per claim. Sprains were the most frequently cited injury experienced from vehicular accidents.vehicular accidents, driving, truck drivers, workers' compensation, public health, workplace safety

Identification of Air Traffic Management Principles Influential in the Development of an Airport Arrival Delay Prediction Model

Since the September 11, 2001 attacks, worldwide air traffic has steadily been increasing towards peak levels reported from 2000 to 2001 (Federal Aviation Administration [FAA], 2011). Although U.S. system-wide traffic is still around 10% less than the highest volumes, congestion at particular airports prone to delays, such as Newark, Philadelphia, New York LaGuardia, and New York Kennedy, is up nearly 10% from 2000 metrics. Other airports, such as Chicago O’Hare and Atlanta in the U.S. and London Heathrow, Madrid, and Istanbul in Europe, are seemingly continually plagued with flight delays regardless of variations in traffic (FAA, 2012). According to the Bureau of Transportation Statistics (2013), the best flight punctuality rate among the 29 largest primary U.S. airports in January 2012 was 89.7% with the worst being 77.2%. In Europe, 14 major airports reported arrival delays in excess of 15 minutes for more than 25% of flights (FAA, 2012). Air traffic forecasts through 2031 indicate that both the passenger volume and the number of transport aircraft will be double that of 2012 levels. Considering many of the aforementioned airports are operating near or beyond capacity, it is likely that air traffic delays will only get worse (Airbus, 2012). The importance of delay management is critical to a variety of stakeholders from passengers to air carrier operations management to air traffic control personnel. Reliable delay prediction can mitigate the snowball effects delays can have on the air traffic management system and air carrier structures (Xu, Sherry, & Laskey, 2008). A variety of studies have been implemented to study air traffic delays but generally focus on a system-wide approach that includes arrival, enroute, and departure delays (Brooker, 2009; Coy, 2006; Santos & Robin, 2011; Xu, Sherry, & Laskey, 2008). Alternatively, others have focused on individual airports and their potential influence on the whole air traffic management system (Nayak & Zhang, 2011). More research on the factors associated with and prediction of airport-related delays have been advocated (Brooker, 2009; Coy, 2006; Nayak & Zhang, 2011; Santos & Robin, 2011; Xu, Sherry, & Laskey, 2008). Ideally, an improved model with predictive capabilities would assist in planning for and potentially mitigating negative effects of airport-based arrival congestion. The goal of this pilot study is to begin the construction of an improved airport delay prediction model by exploring potentially influential air traffic management principles. Utilizing expert panel-based model and procedural improvement techniques similar to those used in medical and technical fields, this study aims to bolster existing airport arrival delay prediction models (Deason & Jefferson, 2010; Estes, 2008; Gisev, Bell, O’Reilly, Rosen, & Chen, 2010). In this Phase I pilot study, a purposeful sample of air traffic control instructors, college faculty, and air traffic controllers will be asked to generate a list of air traffic management principles that influence airport arrival efficiency. This data will be utilized to create subsequent phases which will include a Delphi Panel to rank the identified principles, confirmatory analysis, statistical modeling, and model testing

Spatiotemporal Variation of Risk Preceding Crashes on Freeways

Research into the application of freeway loop detector data for traffic safety has gained momentum in recent years. The incompleteness of data from loop detectors has been a common problem in both the development and the implementation of models. The effect of individual crash precursors, obtained one at a time from a series of loop detectors, on relative risk of crash occurrence was examined through within-stratum one-covariate logistic regression models. The hazard ratio (resultant change in log odds of observing a crash by changing the covariate by one unit) was used as the measure of risk. The log of coefficient of variation in speed expressed as percentage, standard deviation of volume, and average occupancy expressed as percentage were found to be the most significant individual covariates affecting the odds of crash occurrence at a crash site. It was also observed that these parameters calculated at a 5-min level (as opposed to a 3-min level) are more significantly associated with crash occurrence. Hazard ratios corresponding to these covariates observed at a series of stations during six 5-min slices were plotted as a contour variable. The location and time of measurements of these parameters with respect to the location and time of the crash were used as ordinate and abscissa, respectively, in the contour plots depicting spatiotemporal variation of crash risk. The chart corresponding to the log of coefficient of variation in speed demonstrated the most clear patterns of increasing risk as the time and location of the crash are approached. On the basis of these spatiotemporal patterns, a methodology with which to identify freeway black spots in real time is proposed. This information could be used by traffic management centers to take preventive measures to avoid crashes or to prepare law enforcement and emergency vehicles for the impending situation