Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development.

In A Quest to Solve Information System Agility Problems : A SaaS Experience

<p>Change is endemic in modern business competition. In an age of globalisation, with the rapid development of information technologies (IT), changes occur at a much faster pace, and are also more unpredictable. Being agile in a turbulent environment has been ranked highly by executives in surveys of business issues conducted in past five years. Today nearly all organisations rely on information systems (IS) to operate. IS Agility is critical in achieving overall agility and performance in business. However, despite the strong interest from the practitioner community, IS Agility in academia has often been perceived as an overly abstract concept that is difficult to study. Resultantly, despite its importance, little has been published on how to systematically solve IS Agility problems with theoretical rigour and practical relevance. This “how to” question is a challenging one to researchers and is the major motivation of the present study. A key difficulty to study IS Agility is the lack of a solid conceptualisation. In this thesis, based on a multidisciplinary literature review looking for a unified theory of IS Agility, we proposed the IS Agility Nomological Network (ISANN) as a holistic conceptualisation to be used for problem solving. Such a conceptualisation includes an IS Agility Cycle illustrating four stages (i.e. Sense, Diagnose, Select, and Execute) of the dynamic interactions between IS and its human agents (e.g. IS users and IS developers), a decision tree presenting four main IS Agility drivers (i.e. Change Frequency, Uncertainty, Information Intensity, and Time Criticality), and a pyramid incorporating four IS Agility Providers (i.e. Agile System-Development, Agile-System Architecture, Agile System-Operation, and Agile-System Configuration ). We classify IS Agility as having at least two sides, Dev Agility and Ops Agility. The former represents the agility of IS development function while the later refers to the IS operation function. We believe they are not the same, as agility in system development process doesn’t necessarily translate to agility in the resulting system operation. To be able to answer the “how to” question and design a systematic problem-solving approach, we then operationalised ISANN by developing data and task models in real-world settings. These models were used to investigate and analyse IS Agility problems faced by Software as a Service (SaaS) adopters. Such a SaaS environment, due to its multi-tenancy nature, provides a great opportunity to observe the interactions and trade-offs between Dev Agility (e.g. stories from engineers and developers) and Ops Agility (e.g. stories from operators and users), as well as an abundant source of IS Agility related business problems. Eventually, more elements and factors emerged from this SaaS practice and were merged into the final artefact created in this study: ISACAM (Information System Agility Causation Analysis Method). ISACAM incorporates all the dimensions and facts derived from the theoretical conceptualisation and the ongoing real-world problem-solving practice. The effectiveness of ISACAM in solving IS Agility problems has been observed through improved performance in real-life businesses. Furthermore, five technological rules have been synthesised to offer a prescription for designing solutions to improve IS Agility.</p>

In A Quest to Solve Information System Agility Problems : A SaaS Experience

<p>Change is endemic in modern business competition. In an age of globalisation, with the rapid development of information technologies (IT), changes occur at a much faster pace, and are also more unpredictable. Being agile in a turbulent environment has been ranked highly by executives in surveys of business issues conducted in past five years. Today nearly all organisations rely on information systems (IS) to operate. IS Agility is critical in achieving overall agility and performance in business. However, despite the strong interest from the practitioner community, IS Agility in academia has often been perceived as an overly abstract concept that is difficult to study. Resultantly, despite its importance, little has been published on how to systematically solve IS Agility problems with theoretical rigour and practical relevance. This “how to” question is a challenging one to researchers and is the major motivation of the present study. A key difficulty to study IS Agility is the lack of a solid conceptualisation. In this thesis, based on a multidisciplinary literature review looking for a unified theory of IS Agility, we proposed the IS Agility Nomological Network (ISANN) as a holistic conceptualisation to be used for problem solving. Such a conceptualisation includes an IS Agility Cycle illustrating four stages (i.e. Sense, Diagnose, Select, and Execute) of the dynamic interactions between IS and its human agents (e.g. IS users and IS developers), a decision tree presenting four main IS Agility drivers (i.e. Change Frequency, Uncertainty, Information Intensity, and Time Criticality), and a pyramid incorporating four IS Agility Providers (i.e. Agile System-Development, Agile-System Architecture, Agile System-Operation, and Agile-System Configuration ). We classify IS Agility as having at least two sides, Dev Agility and Ops Agility. The former represents the agility of IS development function while the later refers to the IS operation function. We believe they are not the same, as agility in system development process doesn’t necessarily translate to agility in the resulting system operation. To be able to answer the “how to” question and design a systematic problem-solving approach, we then operationalised ISANN by developing data and task models in real-world settings. These models were used to investigate and analyse IS Agility problems faced by Software as a Service (SaaS) adopters. Such a SaaS environment, due to its multi-tenancy nature, provides a great opportunity to observe the interactions and trade-offs between Dev Agility (e.g. stories from engineers and developers) and Ops Agility (e.g. stories from operators and users), as well as an abundant source of IS Agility related business problems. Eventually, more elements and factors emerged from this SaaS practice and were merged into the final artefact created in this study: ISACAM (Information System Agility Causation Analysis Method). ISACAM incorporates all the dimensions and facts derived from the theoretical conceptualisation and the ongoing real-world problem-solving practice. The effectiveness of ISACAM in solving IS Agility problems has been observed through improved performance in real-life businesses. Furthermore, five technological rules have been synthesised to offer a prescription for designing solutions to improve IS Agility.</p>

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

Continuous metro-operation accidents lead to serious economic loss and a negative social impact. The accident causation analysis is of great significance for accident prevention and metro operation safety promotion. Network node importance (NNI) evaluation has been widely used as a tool for ranking the nodes in complex networks; however, traditional indicators such as degree centrality (DC) are insufficient for examining accident networks. This study proposed an improved method by integrating decision making trail and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) into traditional NNI evaluation, where the key nodes are determined by both the nature of the accident network topology and the contribution of the nodes to accident development. Drawing on this method, 32 accident causal factors were identified and prioritized on the ground of 248 accident cases. It was found that 14 important factors related to staff (e.g., “driver noncompliance”), environment (e.g., “extrinsic nature disturbance”), passenger (e.g., “passenger sudden illness”), and machine (e.g., “track failures”) should be given priority in safety management due to their significant tendency of causing metro accidents. Theoretical and managerial implications were discussed to provide useful insights into the understanding of the causation of metro accidents and form a basis for metro managers to develop targeted safety countermeasures related to metro operation. The proposed hybrid method is proven effective in investigating accident networks involving sequential and casual relationships and revealing factors with high possibility to increase accidents.

Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

This paper addresses crashes involving heavy goods vehicles (HGV) in Europe focusing on long-haul trucks weighing 16 tons or more (16t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis: general crash statistics from CARE addressing all HGVs, results about 16t+ trucks from national crash databases and a detailed study of in-depth crash data from GIDAS, including a crash causation analysis. Most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on non-highway roads. Three main scenarios for 16t+ trucks are characterized in-depth: (1) rear-end crashes in which the truck is the striking partner, (2) conflicts during right turn maneuvers of the truck and a cyclist riding alongside and (3) pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g. distraction) were the main causing factors in 72% of cases in scenario (1) while information access problems (e.g. blind spots) were present for 72% of cases in scenario (2) and 75% of cases in scenario (3). The results provide both a global overview and sufficient depth of analysis in the most relevant cases and thereby aid safety system development.

Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks

The prototypes of autonomous vessels are expected to come into service within the coming years, but safety concerns remain due to complex traffic and natural conditions (e.g., Yangtze River). However, the response of autonomous vessels to potential accidents is still uncertain. The accident prevention for autonomous vessels is unconvincing due to the lack of objective studies on the causation analysis for maritime accidents. This paper constitutes an attempt to cover the aforementioned gap by studying the potential causations for maritime accidents in the Yangtze River by using a Bayesian-based network training approach. More than two hundred accidents reported between 2013 and 2019 in the Yangtze River are collected. As a result, a Bayesian network (BN) is successfully established to describe the causations among different risk influencing factors. By analysing the BN, this study reveals that the occurrence of maritime accidents (e.g., collision, grounding) can be expected to reduce with the development of autonomous vessels as the crews are removed. However, the extent of the consequences from some accidents (e.g., fire, critical weathers) could be more serious than conventional ones. Therefore, more attention and thoughts are needed to ensure the safe navigation of autonomous vessels in the Yangtze River.

Medicolegal Causation Investigation of Bacterial Endocarditis Associated with an Oral Surgery Practice Using the INFERENCE Approach

Investigating causation is a primary goal in forensic/legal medicine, aiming to establish the connection between an unlawful/negligent act and an adverse outcome. In malpractice litigation involving a healthcare-associated infection due to a failure of infection prevention and control practices, the medicolegal causal analysis needs to quantify the individual causal probabilities to meet the evidentiary requirements of the court. In this paper, we present the investigation of the most probable cause of bacterial endocarditis in a patient who underwent an invasive procedure at a dental/oral surgical practice where an outbreak of bacterial endocarditis had already been identified by the state Department of Health. We assessed the probability that the patient’s endocarditis was part of the outbreak versus that it was an unrelated sporadic infection using the INFERENCE (Integration of Forensic Epidemiology and the Rigorous Evaluation of Causation Elements) approach to medicolegal causation analysis. This paper describes the step-by-step application of the INFERENCE approach to demonstrate its utility in quantifying the probability of causation. The use of INFERENCE provides the court with an evidence-based, transparent, and reliable guide to determine liability, causation, and damages.

Correlation and Causation Analysis Between COVID-19 and Environmental Factors in China

Coronavirus disease 2019 (COVID-19) is seriously threatening and altering human society. Although prevention and control measures play an important role in preventing the transmission of severe acute respiratory syndrome coronavirus, signals of climate impact can still be detected globally. In this paper, the data of 265 cities in China were analyzed. The results show that the correlations between COVID-19 and air quality index (AQI) and PM2.5 concentration were very weak and that the correlations between COVID-19 and meteorological factors were significantly different in different climate backgrounds. So, a fixed model is not enough to describe the correlations. Overall, high humidity, low wind speed, and relatively lower air temperature are conducive to the spread of COVID-19. The climate background suitable for the spread of COVID-19 in China is air temperature 0~15°C, specific humidity &lt;3 g kg−1, and wind speed &lt;3 m s−1. The Granger causality test shows that there is a causal relationship between daily average air temperature and the number of COVID-19 confirmed cases in some cities of China, and air temperature is indicative of the number of confirmed cases the next day. However, this phenomenon is not universal due to regional climate differences.