Bowtie models as preventive models in maritime safety

Aquest treball ha sorgit d’una proposta del Dr. Rodrigo de Larrucea que ha acabat de publicar un llibre ambiciós sobre Seguretat Marítima. Com ell mateix diu, el tema “excedeix amb molt les potencialitats de l’autor”, així que en el meu cas això és més cert. Es pot aspirar, però, a fer una modesta contribució a l’estudi i difusió de la seguretat de la cultura marítima, que només apareix a les notícies quan tenen lloc desastres molt puntuals. En qualsevol cas, el professor em va proposar que em centrés en els Bowtie Models, models en corbatí, que integren l’arbre de causes y el de conseqüències (en anglès el Fault Tree Analysis, FTA, i l’Event Tree Analysis, ETA). Certament, existeixen altres metodologies i aproximacions (i en el seu llibre en presenta vàries, resumides), però per la seva senzillesa conceptual i possibilitat de generalització i integració dels resultats era una bona aposta. Així, després d’una fase de meditació i recopilació de informació, em vaig decidir a presentar un model en corbatí molt general on caben les principals causes d’accidents (factores ambientals, error humà i fallada mecànica), comptant també que pot existir una combinació de causes. De tota manera, a l’hora d’explotar aquest model existeix la gran dificultat de donar una probabilitat de ocurrència, un nombre entre 0 i 1, a cada branca. Normalment les probabilitats d’ocurrència són petites i degut a això difícils d’estimar. Cada accident és diferent, de grans catàstrofes n’hi ha poques, i cada accident ja és estudiat de manera exhaustiva (més exhaustiva quan més greu és). Un altre factor que dificulta l’estima de la probabilitat de fallada és l’evolució constant del món marítim, tant des del punt de vista tècnic, de formació, legal i fins i tot generacional doncs cada generació de marins és diferent. Els esforços estan doncs enfocats a augmentar la seguretat, encara que sempre amb un ull posat sobre els costs. Així, he presentat un model en corbatí pel seu valor didàctic i gràfic però sense entrar en detalls numèrics, que si s’escau ja aniré afinant i interioritzant en l’exercici de la professió. En aquest treball també he intentat no mantenir-me totalment al costat de la teoria (ja se sap que si tot es fa bé, tot surt perfecte, etc…) sinó presentar amb cert detall 2 casos ben coneguts d’accidents marítims: el petroler Exxon Valdez, el 1989 i el ferry Estonia en 1994, entre altres esmentats. Són casos ja una mica vells però que van contribuir a augmentar la cultura de la seguretat, fins a arribar al nivell del que gaudim actualment, al menys als països occidentals. Doncs la seguretat, com esmenta Rodrigo de Larrucea “és una actitud i mai és fortuïta; sempre és el resultat d’una voluntat decidida, un esforç sincer, una direcció intel·ligent i una execució acurada. Sens lloc a dubtes, sempre suposa la millor alternativa”. The work has been inspired in its initial aspects by the book of my tutor Jaime Rodrigo de Larrucea, that presents a state of the art of all the maritime aspects related to safety. Evidently, since it covers all the topics, it cannot deepen on every topic. It was my opportunity to deepen in the Bowtie Model but finally I have also covered a wide variety of topics. Later, when I began to study the topics, I realized that the people in the maritime world usually do not understand to a great extent statistics. Everybody is concerned about safety but few nautical students take a probabilistic approach to the accidents. For this it is extremely important to study the population that is going to be studied: in our case the SOLAS ships Also, during my time at Riga, I have been very concerned with the most diverse accidents, some of them studied during the courses at Barcelona. I have seen that it is difficult to model mathematically the accidents, since each one has different characteristics, angles, and surely there are not 2 equal. Finally, it was accorded that I should concentrate on the Bowtie Model, which is not very complex from a statistical point of view. It is simply a fault tree of events model and a tree of effects. I present some examples in this Chapter 2. The difficulty I point out is to try to estimate the probabilities of occurrence of events that are unusual. We concentrated at major accidents, those that may cause victims or heavy losses. Then, for the sake of generality, at Chapter 4, I have divided the causes in 4 great classes: Natural hazards, human factor, mechanical failure and attacks (piracy and terrorism). The last concern maybe should not be included beside the others since terrorism and piracy acts are not accidents, but since there is an important code dedicated to prevent security threats, ISPS, it is example of design of barriers to prevent an undesired event (although it gives mainly guidelines to follow by the States, Port Terminals and Shipping Companies). I have presented a detailed study of the tragedy of the Estonia, showing how a mechanical failure triggered the failure of the ferry, by its nature a delicate ship, but there were other factors such as poor maintenance and heavy seas. At the next Chapter, certain characteristics of error chains are analyzed. Finally, the conclusions are drawn, offering a pretty optimistic view of the safety (and security) culture at the Western World but that may not easily permeate the entire World, due to the associated costs

Functional failure sequences in traffic accidents

This thesis examines the interactions between road users and the factors that contribute to the occurrence of traffic accidents, and discusses the implications of these interactions with regards to driver behaviour and accident prevention measures. Traffic accident data is collected on a macroscopic level by local police authorities throughout the UK. This data provides a description of accident related factors on a macroscopic level which does not allow for a complete understanding of the interaction between the various road users or the influence of errors made by active road users. Traffic accident data collected on a microscopic level analysis of real world accident data, explaining why and how an accident occurred, can further contribute to a data driven approach to provide safety measures. This data allows for a better understanding of the interaction of factors for all road users within an accident that is not possible with other data collection methods. In the first part of the thesis, a literature review presents relevant research in traffic accident analysis and accident causation research, afterwards three accident causation models used to understand behaviour and factors leading to traffic accidents are introduced. A comparison study of these accident causation coding models that classify road user error was carried out to determine a model that would be best suited to code the accident data according to the thesis aims. Latent class cluster analyses were made of two separate datasets, the UK On the Spot (OTS) in-depth accident investigation study and the STATS19 national accident database. A comparison between microscopic (in-depth) accident data and macroscopic (national) accident data was carried out. This analysis allowed for the interactions between all relevant factors for the road users involved in the accident to be grouped into specific accident segmentations based on the cluster analysis results. First, all of the cases that were collected by the OTS team between the years 2000 to 2003 were analysed. Results suggested that for single vehicle accidents males and females typically made failures related to detection and execution issues, whereas male road users made diagnosis failures with speed as a particularly important factor. In terms of the multiple vehicle accidents the interactions between the first two road users and the subsequent accident sequence were demonstrated. A cluster analysis of all two vehicle accidents in Great Britain in the year 2005 and recorded within the STATS19 accident database was carried out as a comparison to the multiple vehicle accident OTS data. This analysis demonstrated the necessity of in-depth accident causation data in interpreting accident scenarios, as the resulting accident clusters did not provide significant differences between the groups to usefully segment the crash population. Relevant human factors were not coded for these cases and the level of detail in the accident cases did not allow for a discussion of countermeasure implications. An analysis of 428 Powered Two Wheeler accidents that were collected by the OTS team between the years 2000 to 2010 was carried out. Results identified 7 specific scenarios, the main types of which identified two particular looked but did not see accidents and two types of single vehicle PTW accidents. In cases where the PTW lost control, diagnosis failures were more common, for road users other than the PTW rider, detection issues were of particular relevance. In these cases the interaction between all relevant road users was interpreted in relation to one another. The subsequent study analysed 248 Pedestrian accidents that were collected by the OTS team between the years 2000 to 2010. Results identified scenarios related to pedestrians as being in a hurry and making detection errors, impairment due to alcohol, and young children playing in the roadside. For accidents that were initiated by the other road user s behaviour pedestrians were either struck after an accident had already occurred or due to the manoeuvre that a road user was making, older pedestrians were over-represented in this accident type. This thesis concludes by discussing how (1) microscopic in-depth accident data is needed to understand accident mechanisms, (2) a data mining approach using latent class clustering can benefit the understanding of failure mechanisms, (3) accident causation analysis is necessary to understand the types of failures that road users make and (4) accident scenario development helps quantify accidents and allows for accident countermeasure implication discussion. The original contribution to knowledge is the demonstration that when relevant data is available there is a possibility to understand the interactions that are occurring between road users before the crash, that is not possible otherwise. This contribution has been demonstrated by highlighting how latent class cluster analysis combined with accident causation data allows for relevant interactions between road users to be observed. Finally implications for this work and future considerations are outlined

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Airborne Advanced Reconfigurable Computer System (ARCS)

A digital computer subsystem fault-tolerant concept was defined, and the potential benefits and costs of such a subsystem were assessed when used as the central element of a new transport's flight control system. The derived advanced reconfigurable computer system (ARCS) is a triple-redundant computer subsystem that automatically reconfigures, under multiple fault conditions, from triplex to duplex to simplex operation, with redundancy recovery if the fault condition is transient. The study included criteria development covering factors at the aircraft's operation level that would influence the design of a fault-tolerant system for commercial airline use. A new reliability analysis tool was developed for evaluating redundant, fault-tolerant system availability and survivability; and a stringent digital system software design methodology was used to achieve design/implementation visibility

Human factors of flight-deck checklists: The normal checklist

Although the aircraft checklist has long been regarded as the foundation of pilot standardization and cockpit safety, it has escaped the scrutiny of the human factors profession. The improper use, or the non-use, of the normal checklist by flight crews is often cited as the probable cause or at least a contributing factor to aircraft accidents. An attempt is made to analyze the normal checklist, its functions, format, design, length, usage, and the limitations of the humans who must interact with it. The development of the checklist from the certification of a new model to its delivery and use by the customer are discussed. The influence of the government, particularly the FAA Principle Operations Inspector, the manufacturer's philosophy, the airline's culture, and the end user, the pilot, influence the ultimate design and usage of this device. The effects of airline mergers and acquisitions on checklist usage and design are noted. In addition, the interaction between production pressures and checklist usage and checklist management are addressed. Finally, a list of design guidelines for normal checklists is provided

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Analyzing human factors in road accidents: TRACE WP5 Summary Report

The main objectives of TRACE WP5 'Human factors' deliverables are: i) To support a better standardization of accident analysis in Europe on a scientific background, ii) To provide operational models and methodological classification grids dealing with 'human factors' aspects involved in road accidents, iii) To promote a comprehensive analysis of the involvement of human beings, going further than the usual 'user-orientated causal analysis' often limited at establishing the driver 'at fault' and without searching for the background reasons of the problems met par road users. Such objectives involve analyzing accidents as the symptom of the difficulties met by drivers in certain driving situations, and as a revelatory of their needs in help. Two questions have to be asked in order to progress in the understanding of accident causation: 1) What are precisely and operationally the human failures in accidents? But also: 2) What are the reasons for these human failures? Keeping in mind that these reasons are of multiple natures and combine most of the time to produce the final event. By so doing, the definition of typical scenarios of 'human error' production can open to the definition of more appropriate countermeasures, well fitted to human needs