1) The document discusses the potential for using big data analytics to predict safety risks in the rail industry by analyzing rail accident data.
2) It provides examples of different types of big data that could be collected and analyzed, including real-time monitoring data, asset maintenance data, social media data, and location history data.
3) The document analyzes a rail accident in Spain and indicates which types of big data could have provided useful insights if collected, such as CCTV footage, social media posts, and emergency services communications. It proposes a new risk analysis method called the "ELBowtie" that visualizes risks.
DataAI won this Maritime Data Innovation Award, to develop a risk assessment algorithm to be applied to all boats in the Med with the French Multinational Thales
Beyond GNSS: Highly Accurate Localization for Cooperative-Intelligent Transpo...Stefano Severi
WCNC18 presentation of the results and main achievement of the EU H2020 Project HIGHTS (www.hights.eu). Take home message: accuracy is important for HAD but also robustness --> HIGHTS has developed an European Wide Service Platform for providing the best solution in term of accuracy and reliability in each context or scenario
Deriving on-trip route choices of truck drivers by utilizing Bluetooth data,...SalilSharma26
This paper models on-trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38 percent of truck drivers do not take the shortest time path, 48 percent of truck drivers do not choose the system-optimal path.
DataAI won this Maritime Data Innovation Award, to develop a risk assessment algorithm to be applied to all boats in the Med with the French Multinational Thales
Beyond GNSS: Highly Accurate Localization for Cooperative-Intelligent Transpo...Stefano Severi
WCNC18 presentation of the results and main achievement of the EU H2020 Project HIGHTS (www.hights.eu). Take home message: accuracy is important for HAD but also robustness --> HIGHTS has developed an European Wide Service Platform for providing the best solution in term of accuracy and reliability in each context or scenario
Deriving on-trip route choices of truck drivers by utilizing Bluetooth data,...SalilSharma26
This paper models on-trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38 percent of truck drivers do not take the shortest time path, 48 percent of truck drivers do not choose the system-optimal path.
Thought Leadership: Brain Power as a Leading IndicatorEva Keiser
Presentation given as part of Reputation seminar put on by Risdall McKinney Public Relations.
It takes intentional, purposeful effort to create and sustain a brand. A thought leader is an action-orientated participant in inudstry and cosnumer forums. An ongoing process, today's contrbutions lead to tomorrow's business success. Reputation is on the line every day.
The use of leading indicators - proactive, preventive and predictive measures to identify and eliminate risks and hazards in the workplace – is on the radar of many environmental, health and safety professionals.
In this webinar, John Dony and Joy Inouye of the Campbell Institute, will discuss their research into leading indicators. They will define leading indicators, explain their importance, describe applications, and share specific examples of indicators.
The presentation will include case studies and advice for getting started with leading indicators at your organization.
The Campbell Institute at the National Safety Council is built upon the belief that environment, health and safety (EHS) is at the core of business. It sees EHS as fundamental to operational and financial performance, and seeks to help organizations, of all sizes and sectors, achieve and sustain excellence.
Attend this webinar and learn:
How leading indicators could improve your EHS programs
Practical advice on how to implement them
Successful case studies from industry-leading organizations
This presentation outlines the Campbell Institute's previous two years of research on best practices for choosing, tracking and analyzing leading indicators. Attendees learned about the critical characteristics of leading indicators and how to go about choosing the right metrics. See how other companies succeeded at integrating leading indicators into their overall safety management system with three distinct case studies.
BE-GOOD is a pioneering project aiming to unlock, re-use and extract value from Public Sector Information (PSI) to develop innovative data-driven services in the area of infrastructure & environment.
BE-GOOD’s main outputs: 10 novel commercial PSI-based services prototyped operationally, with the aim to commercialise 5.
Examples: applications, visualisations, software, algorithms for traffic management, air and water quality monitoring, infrastructure maintenance planning.
http://www.nweurope.eu/begood
"Towards Value-Centric Big Data" e-SIDES Workshop - "Responsible Research: An...e-SIDES.eu
The following presentation was given by Prof. Ansar Yasar from the University of Hasselt during the e-SIDES workshop "Towards Value-Centric Big Data" held on April 2, 2019 in Brussels.
Wireless Reporting System for Accident Detection at Higher SpeedsIJERA Editor
Speed is one of the basic reasons for vehicle accident. Many lives could have been saved if emergency service
could get accident information and reach in time. Nowadays, GPS has become an integral part of a vehicle
system. This paper proposes to utilize the capability of a GPS receiver to monitor speed of a vehicle and detect
accident basing on monitored speed and send accident location to an Alert Service Center. The GPS will
monitor speed of a vehicle and compare with the previous speed in every second through a Microcontroller
Unit. Whenever the speed will be below the specified speed, it will assume that an accident has occurred. The
system will then send the accident location acquired from the GPS along with the time and the speed by utilizing
the GSM network. This will help to reach the rescue service in time and save the valuable human life.
Integrating spatial and thematic data: the CRISOLA case for Malta and the Eur...Beniamino Murgante
Integrating spatial and thematic data: the CRISOLA case for Malta and the European project Plan4all
Saviour Formosa - Institute of Criminology, University of Malta
Vincent Magri - Fondazzjoni Temi Zammit, University of Malta
Julia Neuschmid, Manfred Schrenk - Department for Urbanism, Transport, Environment and Information Society, Central European Institute of Technology, Austria
Estimation of road condition using smartphone sensors via c4.5 and aes 256 a...EditorIJAERD
Nowadays every smart phone is integrated with many helpful sensors. Sensors are originally design to make
the computer program and application convenient. The smart phone sensors like Gyroscope and Accelerometer are used
to estimate road roughness conditions. The collected data is from sensor and easy to manage value in the frequency
domain to calculate magnitudes of vibrations. Well maintained roads contribute to a significant portion of countries
economy. Roadsense application provides information about rules and regulations (Vehicle Papers, Parking Rule,
Distraction While Driving) to be followed while driving the vehicle. Throughout this paper, we discuss the previous hole
detections ways in which has been developed and process a worth effective answers to identify the potholes and bumps
on the roads. In our application mobile sensors are accustomed establish potholes and the bumps. The proposed system
captures the geographical locations of potholes and bumps using GPS sensor among the mobile. These sense data sent
for classification and uses algorithm C4.5, AES256 then this data sends for further processing. Finally the data is send to
the vehicle driver. An android can be used to display the road condition in the map.
Franck Gallos de la société Ericsson enchaînera sur l’analyse des usages des services d’IP TV des grands opérateurs Telco. Franck détaillera comment la corrélation des données des usages IP TV avec des informations externes comme les données météorologiques ou sociales (événements politiques, sportif, vacances scolaires) permet de contextualiser les statistiques géo localisées pour un meilleur ciblage publicitaire. A noter que ce projet est arrivé second au Trophée de l’Innovation Big Data Paris 2014.
Hadoop User Group, le 11 Juin à la Tour Eiffel avec Infotel
Citiviz Corporate Presentation | Smart mobility for Citizen's Quality of LifeNicolas Lachance-Bernard
Innovaud Connect - Big Data: opportunities & challenges
EPFL, Lausanne, Switzerland
June 17th 2014
"Innovaud Connect" meetings aim at the following goals:
- Give actors in high tech and high potential innovations the opportunity to discover each other ;
- Understand the needs and expectations of all actors included in the value chain ;
- Initiate collaborations or partnerships between the actors ;
- Highlight the creativity potential of a center of competitiveness;
www.citiviz.com | www.twitter.com/Citiviz
www.innovaud.ch | www.twitter.com/Innovaud
An experienced, well-trained call-taker/dispatcher can gather a lot of high quality, vitally important information that can help first responders form an early understanding of what they will be facing upon arrival at the emergency scene. Supporting tools could however help them to do it faster and better!
Chair: Stephen Hines, Clinical Practice Learning Manager, London Ambulance Service, United Kingdom
Thought Leadership: Brain Power as a Leading IndicatorEva Keiser
Presentation given as part of Reputation seminar put on by Risdall McKinney Public Relations.
It takes intentional, purposeful effort to create and sustain a brand. A thought leader is an action-orientated participant in inudstry and cosnumer forums. An ongoing process, today's contrbutions lead to tomorrow's business success. Reputation is on the line every day.
The use of leading indicators - proactive, preventive and predictive measures to identify and eliminate risks and hazards in the workplace – is on the radar of many environmental, health and safety professionals.
In this webinar, John Dony and Joy Inouye of the Campbell Institute, will discuss their research into leading indicators. They will define leading indicators, explain their importance, describe applications, and share specific examples of indicators.
The presentation will include case studies and advice for getting started with leading indicators at your organization.
The Campbell Institute at the National Safety Council is built upon the belief that environment, health and safety (EHS) is at the core of business. It sees EHS as fundamental to operational and financial performance, and seeks to help organizations, of all sizes and sectors, achieve and sustain excellence.
Attend this webinar and learn:
How leading indicators could improve your EHS programs
Practical advice on how to implement them
Successful case studies from industry-leading organizations
This presentation outlines the Campbell Institute's previous two years of research on best practices for choosing, tracking and analyzing leading indicators. Attendees learned about the critical characteristics of leading indicators and how to go about choosing the right metrics. See how other companies succeeded at integrating leading indicators into their overall safety management system with three distinct case studies.
BE-GOOD is a pioneering project aiming to unlock, re-use and extract value from Public Sector Information (PSI) to develop innovative data-driven services in the area of infrastructure & environment.
BE-GOOD’s main outputs: 10 novel commercial PSI-based services prototyped operationally, with the aim to commercialise 5.
Examples: applications, visualisations, software, algorithms for traffic management, air and water quality monitoring, infrastructure maintenance planning.
http://www.nweurope.eu/begood
"Towards Value-Centric Big Data" e-SIDES Workshop - "Responsible Research: An...e-SIDES.eu
The following presentation was given by Prof. Ansar Yasar from the University of Hasselt during the e-SIDES workshop "Towards Value-Centric Big Data" held on April 2, 2019 in Brussels.
Wireless Reporting System for Accident Detection at Higher SpeedsIJERA Editor
Speed is one of the basic reasons for vehicle accident. Many lives could have been saved if emergency service
could get accident information and reach in time. Nowadays, GPS has become an integral part of a vehicle
system. This paper proposes to utilize the capability of a GPS receiver to monitor speed of a vehicle and detect
accident basing on monitored speed and send accident location to an Alert Service Center. The GPS will
monitor speed of a vehicle and compare with the previous speed in every second through a Microcontroller
Unit. Whenever the speed will be below the specified speed, it will assume that an accident has occurred. The
system will then send the accident location acquired from the GPS along with the time and the speed by utilizing
the GSM network. This will help to reach the rescue service in time and save the valuable human life.
Integrating spatial and thematic data: the CRISOLA case for Malta and the Eur...Beniamino Murgante
Integrating spatial and thematic data: the CRISOLA case for Malta and the European project Plan4all
Saviour Formosa - Institute of Criminology, University of Malta
Vincent Magri - Fondazzjoni Temi Zammit, University of Malta
Julia Neuschmid, Manfred Schrenk - Department for Urbanism, Transport, Environment and Information Society, Central European Institute of Technology, Austria
Estimation of road condition using smartphone sensors via c4.5 and aes 256 a...EditorIJAERD
Nowadays every smart phone is integrated with many helpful sensors. Sensors are originally design to make
the computer program and application convenient. The smart phone sensors like Gyroscope and Accelerometer are used
to estimate road roughness conditions. The collected data is from sensor and easy to manage value in the frequency
domain to calculate magnitudes of vibrations. Well maintained roads contribute to a significant portion of countries
economy. Roadsense application provides information about rules and regulations (Vehicle Papers, Parking Rule,
Distraction While Driving) to be followed while driving the vehicle. Throughout this paper, we discuss the previous hole
detections ways in which has been developed and process a worth effective answers to identify the potholes and bumps
on the roads. In our application mobile sensors are accustomed establish potholes and the bumps. The proposed system
captures the geographical locations of potholes and bumps using GPS sensor among the mobile. These sense data sent
for classification and uses algorithm C4.5, AES256 then this data sends for further processing. Finally the data is send to
the vehicle driver. An android can be used to display the road condition in the map.
Franck Gallos de la société Ericsson enchaînera sur l’analyse des usages des services d’IP TV des grands opérateurs Telco. Franck détaillera comment la corrélation des données des usages IP TV avec des informations externes comme les données météorologiques ou sociales (événements politiques, sportif, vacances scolaires) permet de contextualiser les statistiques géo localisées pour un meilleur ciblage publicitaire. A noter que ce projet est arrivé second au Trophée de l’Innovation Big Data Paris 2014.
Hadoop User Group, le 11 Juin à la Tour Eiffel avec Infotel
Citiviz Corporate Presentation | Smart mobility for Citizen's Quality of LifeNicolas Lachance-Bernard
Innovaud Connect - Big Data: opportunities & challenges
EPFL, Lausanne, Switzerland
June 17th 2014
"Innovaud Connect" meetings aim at the following goals:
- Give actors in high tech and high potential innovations the opportunity to discover each other ;
- Understand the needs and expectations of all actors included in the value chain ;
- Initiate collaborations or partnerships between the actors ;
- Highlight the creativity potential of a center of competitiveness;
www.citiviz.com | www.twitter.com/Citiviz
www.innovaud.ch | www.twitter.com/Innovaud
An experienced, well-trained call-taker/dispatcher can gather a lot of high quality, vitally important information that can help first responders form an early understanding of what they will be facing upon arrival at the emergency scene. Supporting tools could however help them to do it faster and better!
Chair: Stephen Hines, Clinical Practice Learning Manager, London Ambulance Service, United Kingdom
Session on: Supporting call-takers/dispatchers
decision making and situational awareness
Chair: Chair: Stephen Hines, Clinical Practice Learning Manager, London Ambulance Service, United Kingdom
An experienced, well-trained call-taker/dispatcher can gather a lot of high quality, vitally important information that can help first responders form an early understanding of what they will be facing upon arrival at the emergency scene. Supporting tools could however help them to do it faster and better!
Use of Road Accidents Data by Government Stakeholders to reduce Road Accident...Data Portal India
Use of Road Accidents Data by Government Stakeholders to reduce Road Accidents and ensure Road Safety – A study on Black Spot Management. Presented by Sh. Ranjan Mukherjee, Director, M/o Road Transport & Highways at Workshop on Data Driven Decision Making for Chief Data Officers.
In Finpro's seminar on May 4, Josef Czako spoke about ITS and MaaS opportunities in Germany, Austria and Switzerland. He also gave understanding on what kind of players there are in the field of ITS in respective countries, and what steps one should follow when entering the market.
Snap4City November 2019 Course: Smart City IOT Data AnalyticsPaolo Nesi
• Data Analytics: Examples from Snap4City
o Smart parking: Predictions
o User Behavior Analysis, via Wi-Fi, OD, Trajectories
o Recognition of Used Transportation means
o Traffic Flow Reconstruction, from Traffic Sensors Data
o Quality of Public Transport Service
o Origin Destination Matrices from: Wi-Fi, Mobile Apps, etc.
o Demand of Mobility vs Offer of Transportation
o Modal and Multimodal Routing for Navigation and Travel Planning
o Environmental Data Analysis and Predictions, early Warning
o Prediction of Air Quality Conditions
o Anomaly Detection
o What-IF Analysis
• Data Analytics: Enforcing and Exploiting
o Real Time Data Analytics: using R Studio Exploitation in IOT Applications
• Decision Support Systems, Smart DS and Resilience DS
• Twitter Vigilance: Social Media Analysis: Early Warning, Predictions
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Good morning ladies and gentlemen. Great to be here and thank you for attending this special session on Accidents Analysis and R&D of Safety Technologies.
What is Big Data and why it’s useful
Data Taxonomy
Causes, Hazards and Accidents
Accident Analysis and links to data
“BDness” of accidents
A new approaching using the “ELBowtie”
Big Data, massive amounts of data, structure and unstructured, real time and historical. Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone – IBM Source. Zetta byte 1000to the power 7: 1000kB kilobyte10002MB megabyte10003GB gigabyte10004TB terabyte10005PB petabyte10006EB exabyte10007ZBzettabyte10008YBy ottabyte
IOT, massive interconnectivity of assets. Intelligent monitoring using cheap electronics, sensors and connectivety, for exam[le GPRS 4G etc. Long battery lives so can be remote. Could festoon the rail network with this technology creating and internet of rail. We can have thero couples, accelometers, strain gauges, etc etc.
Lot of hype about big data. Number of conference papers maps the exponential growth in data. Gartnet cycle. We did Neural Nets back in the last centrury. Alos involved in an AI in design project. These were followed by what has been described as an AI winter.We are now climbing out of the trough of disillutionment, up the slope of enlightenment and onto the heady uplans of the Plateau of productivity.
Volume (amount),
•Velocity (speed of capture or change),
•Variety (number of sources),
•Veracity (quality), and
•Value (here the safety related value).
I this paper we have used these Big Data attributes to try to categorise the analysis of accidents and the associated data.
What data is available in the railway and related areas?
condition based monitoring information from sensors, either analogue or digital, that would provide digital information, including vibration (accelerometers), machine vision, heat, displacement, strain, humidity, particle ingress, etc. would be classified as ‘Real Time, remote monitoring’, which is already an accepted means of classifying this type of data.
Other data types are less well defined, for example, data from industry reports, staff morale, organisational culture, but can be equally as important in safety evaluations.
What are the main causes of railway accidents?
Accident Causes. A Study Based Upon research of 43 accidents [THE CAUSES OF ACCIDENTS IMechE 2007]
From a study it can bee seen that there are always several causes for each accident. One of the causes in isolation would probably not resluted in an accident, but the combination of that cause with others has the potential for disaster.
These have a big overlap or mapping to our Data Taxonomy.
The Hatfield rail crash was a railway accident on 17 October 2000, at Hatfield, Hertfordshire, UK. Although the accident killed fewer than other accidents, it exposed the major stewardship shortcomings of the privatised national railway infrastructure company Railtrack and the failings of the regulatory oversight which the company displayed in its initial years (principally a failure to ensure that the company had a sound knowledge of the condition of its assets) and ultimately triggered its partial renationalisation.
A GNER InterCity 225 train bound for Leeds had left London King's Cross at 12:10, and was travelling at approximately 115 miles per hour (185 km/h) when it derailed south of Hatfield station at 12:23. The primary cause of the accident was later determined to be the left-hand rail fracturing as the train passed over it. The accident killed four passengers and injured a further seventy.
The leading Class 91 locomotive (91023) and the first two coaches remained upright and on the rails. All of the following coaches, and the trailing Driving Van Trailer were derailed, and the train set separated into three sections. The restaurant coach, the eighth vehicle in the set, overturned onto its side and struck an overhead line gantry after derailing, resulting in severe damage to the vehicle.
Crash investigators identified the integrity and strength of the British Rail-designed Mark 4 coaches for protecting occupants. Coincidentally, the locomotive in the crash was also involved in the Great Heck rail crash (where the leading Driving Van Trailer hit a road vehicle on the track) a few months later.
A preliminary investigation found a rail had fragmented as trains passed and that the likely cause was "rolling contact fatigue" (defined as multiple surface-breaking cracks). Such cracks are caused by high loads where the wheels contact the rail.[2] Repeated loading causes fatigue cracks to grow. When they reach a critical size, the rail fails. Over 300 critical cracks were found in rails at Hatfield. The problem was known about before the accident, and replacement rails made available but never delivered to the correct location for installation.
speed restrictions
Railtrack, got rid of the engineering knowledge of British Rail into maintenance contractors, had inadequate maintenance records and no accessible asset register. No knowledge of extent of problem and 1200 restriction imposed, the railway had a nervous breakdown. Road deaths increased
The Santiago de Compostela derailment occurred on 24 July 2013, when an Alvia high-speed train travelling from Madrid to Ferrol, in the north-west of Spain, derailed at high speed on a bend about 4 kilometres (2.5 mi) outside of the railway station at Santiago de Compostela, Spain. Of the 222 people (218 passengers and 4 crew) aboard, around 140 were injured and 79 died.[2]
The train's data recorder showed that it was travelling at about twice the posted speed limit of 80 kilometres per hour (50 mph) when it entered a bend in the line. The crash was recorded on a track-side camera which shows all thirteen vehicles derailing and four overturning. On 28 July 2013, the train's driver Francisco José Garzón Amo was charged with 79 counts of homicide by professional recklessness and an undetermined number of counts of causing injury by professional recklessness.[3]
The Castelldefels train accident occurred on 23 June 2010 when a passenger train struck a group of people who were crossing the railway on the level at Platja de Castelldefels station to the southwest of Barcelona, in Catalonia, Spain. Twelve people were killed, and fourteen injured: all victims but one Romanian were of Latin American origin, with a majority from Ecuador.[1]
The accident occurred on St. John's Eve,[2][3][4] a major celebration in Spain and in several other European countries. The victims were apparently trying to get to the beach less than 200 metres (660 ft) from the station, where a concert by Ecuadorian singer Rubén de Rey had been organized.[1] It was the worst railway accident in Spain since the Valencia Metro derailment in July 2006 killed 43 and injured 47 others.[5][6]
Enetrprise data Linked Bowtie.
Good simple of model of accident causation.
Easy to understand and explain.
There are many other models such as Swiss Cheese, STAMP, etc .
H|ow will we build the model and what type of machine learning do we need.
What does normal look like and what does heightened risk look like.
We are starting to build up our analytics
Neural Networks for predicting train derailment quotinets
Analysis of close Call Information.
Machine vision
We have looked at 3 accidents and tried to define their Bdness in terms of the 5 V.