Application of Track Geometry Deterioration Modelling and Data Mining in Railway Asset Management

Modernin rautatiejärjestelmän hallinnassa rahankäyttö kohdistuu valtaosin nykyisen rataverkon korjauksiin ja parannuksiin ennemmin kuin uusien ratojen rakentamiseen. Nykyisen rataverkon kunnossapitotyöt aiheuttavat suurten kustannusten lisäksi myös usein liikennerajoitteita tai yhteyksien väliaikaisia sulkemisia, jotka heikentävät rataverkon käytettävyyttä Siispä oikea-aikainen ja pitkäaikaisia parannuksia aikaansaava kunnossapito ovat edellytyksiä kilpailukykyisille ja täsmällisille rautatiekuljetuksille. Tällainen kunnossapito vaatii vankan tietopohjan radan nykyisestä kunnosta päätöksenteon tueksi. Ratainfran omistajat teettävät päätöksenteon tueksi useita erilaisia radan kuntoa kuvaavia mittauksia ja ylläpitävät kattavia omaisuustietorekistereitä. Kenties tärkein näistä datalähteistä on koneellisen radantarkastuksen tuottamat mittaustulokset, jotka kuvastavat radan geometrian kuntoa. Nämä mittaustulokset ovat tärkeitä, koska ne tuottavat luotettavaa kuntotietoa: mittaukset tehdään toistuvasti, 2–6 kertaa vuodessa Suomessa rataosasta riippuen, mittausvaunu pysyy useita vuosia samana, tulokset ovat hyvin toistettavia ja ne antavat hyvän yleiskuvan radan kunnosta. Vaikka laadukasta dataa on paljon saatavilla, käytännön omaisuudenhallinnassa on merkittäviä haasteita datan analysoinnissa, sillä vakiintuneita menetelmiä siihen on vähän. Käytännössä seurataan usein vain mittaustulosten raja-arvojen ylittymistä ja pyritään subjektiivisesti arvioimaan rakenteiden kunnon kehittymistä ja korjaustarpeita. Kehittyneen analytiikan puutteet estävät kuntotietojen laajamittaisen hyödyntämisen kunnossapidon suunnittelussa, mikä vaikeuttaa päätöksentekoa. Tämän väitöskirjatutkimuksen päätavoitteita olivat kehittää ratageometrian heikkenemiseen mallintamismenetelmiä, soveltaa tiedonlouhintaa saatavilla olevan omaisuusdatan analysointiin sekä jalkauttaa kyseiset tutkimustulokset käytännön rataomaisuudenhallintaan. Ratageometrian heikkenemisen mallintamismenetelmien kehittämisessä keskityttiin tuottamaan nykyisin saatavilla olevasta datasta uutta tietoa radan kunnon kehityksestä, tehdyn kunnossapidon tehokkuudesta sekä tulevaisuuden kunnossapitotarpeista. Tiedonlouhintaa sovellettiin ratageometrian heikkenemisen juurisyiden selvittämiseen rataomaisuusdatan perusteella. Lopuksi hyödynnettiin kypsyysmalleja perustana ratageometrian heikkenemisen mallinnuksen ja rataomaisuusdatan analytiikan käytäntöön viennille. Tutkimustulosten perusteella suomalainen radantarkastus- ja rataomaisuusdata olivat riittäviä tavoiteltuihin analyyseihin. Tulokset osoittivat, että robusti lineaarinen optimointi soveltuu hyvin suomalaisen rataverkon ratageometrian heikkenemisen mallinnukseen. Mallinnuksen avulla voidaan tuottaa tunnuslukuja, jotka kuvaavat rakenteen kuntoa, kunnossapidon tehokkuutta ja tulevaa kunnossapitotarvetta, sekä muodostaa havainnollistavia visualisointeja datasta. Rataomaisuusdatan eksploratiiviseen tiedonlouhintaan käytetyn GUHA-menetelmän avulla voitiin selvittää mielenkiintoisia ja vaikeasti havaittavia korrelaatioita datasta. Näiden tulosten avulla saatiin uusia havaintoja ongelmallisista ratarakennetyypeistä. Havaintojen avulla voitiin kohdentaa jatkotutkimuksia näihin rakenteisiin, mikä ei olisi ollut mahdollista, jollei tiedonlouhinnan avulla olisi ensin tunnistettu näitä rakennetyyppejä. Kypsyysmallin soveltamisen avulla luotiin puitteet ratageometrian heikkenemisen mallintamisen ja rataomaisuusdatan analytiikan kehitykselle Suomen rataomaisuuden hallinnassa. Kypsyysmalli tarjosi käytännöllisen tavan lähestyä tarvittavaa kehitystyötä, kun eteneminen voitiin jaotella neljään eri kypsyystasoon, jotka loivat selkeitä välitavoitteita. Kypsyysmallin ja asetettujen välitavoitteiden avulla kehitys on suunniteltua ja edistystä voidaan jaotella, mikä antaa edellytykset tämän laajamittaisen kehityksen onnistuneelle läpiviennille. Tämän väitöskirjatutkimuksen tulokset osoittavat, miten nykyisin saatavilla olevasta datasta saadaan täysin uutta ja merkityksellistä tietoa, kun sitä käsitellään kehittyneen analytiikan avulla. Tämä väitöskirja tarjoaa datankäsittelyratkaisujen luomisen ja soveltamisen lisäksi myös keinoja niiden käytäntöönpanolle, sillä tietopohjaisen päätöksenteon todelliset hyödyt saavutetaan vasta käytännön radanpidossa.In the management of a modern European railway system, spending is predominantly allocated to maintaining and renewing the existing rail network rather than constructing completely new lines. In addition to major costs, the maintenance and renewals of the existing rail network often cause traffic restrictions or line closures, which decrease the usability of the rail network. Therefore, timely maintenance that achieves long-lasting improvements is imperative for achieving competitive and punctual rail traffic. This kind of maintenance requires a strong knowledge base for decision making regarding the current condition of track structures. Track owners commission several different measurements that depict the condition of track structures and have comprehensive asset management data repositories. Perhaps one of the most important data sources is the track recording car measurement history, which depicts the condition of track geometry at different times. These measurement results are important because they offer a reliable condition database; the measurements are done recurrently, two to six times a year in Finland depending on the track section; the same recording car is used for many years; the results are repeatable; and they provide a good overall idea of the condition of track structures. However, although high-quality data is available, there are major challenges in analysing the data in practical asset management because there are few established methods for analytics. Practical asset management typically only monitors whether given threshold values are exceeded and subjectively assesses maintenance needs and development in the condition of track structures. The lack of advanced analytics prevents the full utilisation of the available data in maintenance planning which hinders decision making. The main goals of this dissertation study were to develop track geometry deterioration modelling methods, apply data mining in analysing currently available railway asset data, and implement the results from these studies into practical railway asset management. The development of track geometry deterioration modelling methods focused on utilising currently available data for producing novel information on the development in the condition of track structures, past maintenance effectiveness, and future maintenance needs. Data mining was applied in investigating the root causes of track geometry deterioration based on asset data. Finally, maturity models were applied as the basis for implementing track geometry deterioration modelling and track asset data analytics into practice. Based on the research findings, currently available Finnish measurement and asset data was sufficient for the desired analyses. For the Finnish track inspection data, robust linear optimisation was developed for track geometry deterioration modelling. The modelling provided key figures, which depict the condition of structures, maintenance effectiveness, and future maintenance needs. Moreover, visualisations were created from the modelling to enable the practical use of the modelling results. The applied exploratory data mining method, General Unary Hypotheses Automaton (GUHA), could find interesting and hard-to-detect correlations within asset data. With these correlations, novel observations on problematic track structure types were made. The observations could be utilised for allocating further research for problematic track structures, which would not have been possible without using data mining to identify these structures. The implementation of track geometry deterioration and asset data analytics into practice was approached by applying maturity models. The use of maturity models offered a practical way of approaching future development, as the development could be divided into four maturity levels, which created clear incremental goals for development. The maturity model and the incremental goals enabled wide-scale development planning, in which the progress can be segmented and monitored, which enhances successful project completion. The results from these studies demonstrate how currently available data can be used to provide completely new and meaningful information, when advanced analytics are used. In addition to novel solutions for data analytics, this dissertation research also provided methods for implementing the solutions, as the true benefits of knowledge-based decision making are obtained in only practical railway asset management

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Product Development within Artificial Intelligence, Ethics and Legal Risk

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch

implications to CRM and public policy

Thesis(Doctoral) --KDI School:Ph.D in Public Policy,2017With the advent of the Internet and Mobile Communications, the nature of communication has changed significantly over the past few decades .The promotion of technologies among the common people has been found to be an important element of public policy to reduce the digital divide. The rapid advancement of information technology (IT), automation systems and data communications systems leads to improvement of intelligent transport systems (ITS). ITS covers all branches of transportation and involves all dynamically interacting elements of transportation system, i.e. transport means, infrastructure, drivers and commuters. However, few researches have been carried out in the context of public sectors, especially that involving ITS. The purpose of this study is to investigate the justice dimensions that influence satisfaction and public confidence in the context of ITS and to explore implications to Citizen/Customer Relationship Management (CRM) and public policy. This study investigates the following research questions: i) Do levels of perceived justice (distributive, procedural and interactional) in ITS environment affect levels of satisfaction/dissatisfaction? ii) Do levels of satisfaction form ITS affect levels of public confidence? iii) Do levels of dissatisfaction form ITS affect levels of willingness to complain? iv) Do levels of dissatisfaction form ITS affect levels of complaining behavior? v) Do levels of complaining behavior in ITS environment affect levels of satisfaction with complaint handling when the complaints are resolved based on three dimensions (distributive, procedural and interactional)of justice? vi) Do levels of willingness to complain in ITS environment affect levels of public confidence? vii) Do levels of satisfaction with complaint handling in ITS environment affect levels of public confidence? The findings of this study imply that ITS users are more importantly perceive to equity and equality issues, or distributive justice. The employment of ITS should not be limited to the technical aspects of ITS, but should focus more attention on the subjective domain of justice. The results of this study also have important implications for public complaint handling in terms of increasing public satisfaction with ITS, which is crucial for CRM.Part I: Exploring Satisfaction/Dissatisfaction and Public Confidence in the ITS Environment; Implications to CRM and Public Policy Part II: ComparingSatisfaction/Dissatisfaction and Public Confidence in the ITS Environment in Public and Private Transportation Part III: Implementation Strategy of ITS in Developing CountriesdoctoralpublishedA. K. M. Anisur RAHMAN

Product Development within Artificial Intelligence, Ethics and Legal Risk

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch

Koneoppimiseen perustuvat sään vaikutusennustukset

Defence is held on 2.11.2021 15:00 – 19:00 Remote connection link https://aalto.zoom.us/j/69735940472Natural disasters influenced over 4 billion people, required 1.23 million lives, and caused almost US$ 3 trillion economic losses between 2000 and 2019. The picture becomes even more deplorable when hazards, smaller-scale severe weather events not requiring casualties, are considered. For example, 78 percent of power outages in Finland were inflicted by extreme weather in 2017, and train delays, often caused by adverse weather, have been estimated to cost 1 billion pounds during 2006 and 2007 in the UK. To mitigate the effects of the adverse weather and increase the resilience of the societies, the World Meteorological Organisation (WMO) raised the consciousness of impact-based warnings along with impact forecasts. Such warnings and predictions can be used in various domains to prepare, alleviate and recuperate from adverse weather conditions.  This thesis studies how to preprocess data and use machine learning to create valuable impact forecasts for power grid and rail traffic operators. The thesis introduces a novel object-oriented method to predict power outages caused by convective storms. The method combines state-of-the-art storm identification, tracking, and nowcasting algorithms with modern machine learning methods. The proposed object-oriented method is also adapted to predict power outages caused by large-scale extratropical storms days ahead. In addition, the thesis studies the task of predicting weather-inflicted train delays. The method presented in the thesis hinges weather parameters on train delays to anticipate the delays days ahead. The thesis shows that the object-oriented approach is a vindicable method to predict power outages caused by convective storms and that a similar approach is feasible also in the context of extratropical storms. The introduced methods provide power grid operators increasingly accurate outage predictions. The thesis also demonstrates that the train delays related to adverse weather can be predicted with good quality training data. Such predictions offer cardinal information for rail traffic operators in preparing the challenging conditions. Presumably, similar approaches can be applied to any other domain with quantitative impacts produced by identifiable weather events, if sufficient impact data are available. Several advanced machine learning methods were evaluated in the tasks. The results corroborate with existing research: random forests provided a robust performance in all tasks, but also gradient boosting trees, Gaussian processes, and support vector machines proved useful.Luonnonkatastrofit vaikuttivat yli 4 miljardiin henkeen, vaativat 1,23 miljoonaa kuolonuhria ja tuottivat lähes 3 biljoonan dollarin taloudelliset tappiot vuosina 2000 -- 2019. Kuva heikkenee entisestään, mikäli huomioidaan myös pienemmän luokan vakavat säätapahtumat. Esimerkiksi 78 prosenttia Suomen vuoden 2017 sähkökatkoista oli sään aiheuttamia. Toisaalta -- usein säähän liittyvät -- junien myöhästymiset tuottivat arviolta miljardin punnan tappiot vuosina 2006 -- 2007 Isossa-Britanniassa. Maailman ilmatieteiden järjestö (WMO) onkin tähdentänyt vaikutusperusteisen varoitusten ja vaikutusennusteiden tärkeyttä vaaralliseen säähän varautumisessa. Vaikutusperusteiset varoitukset ja ennustukset ovat tärkeä apuväline useilla yhteiskunnan osa-alueilla varautuessa ääreviin sääilmiöihin sekä lievittäessä niiden vaikutuksia ja toipuessa niistä.  Tämä väitöskirja tutkii kuinka esiprosessoida dataa ja hyödyntää koneoppmimista sähköverkko- ja junaliikenneoperaattoreille tuotetuissa vaikutusennusteissa. Väitöskirja esittelee uuden oliopohjaisen metodin konvektiivisten rajuilmojen aiheuttamien sähkökatkojen ennustamiseksi. Metodi yhdistää ajantasaiset myrskyn tunnistus-, seuraus- ja lähihetkiennustusalgoritmit moderneihin koneoppimismenetelmiin. Ehdotettu oliopohjainen metodi on myös muokattu ennustamaan laaja-alaisten matalapainemyrskyjen aiheuttamia sähköatkoja. Lisäksi, väitöskirja tutkii sään aiheuttamien junien myöhästymisten ennustamista. Väitöskirjassa esitetty methodi yhdistää sääparametrit junien myöhästymisdataan, jotta myöhästymisiä voidaan ennakoida päiviä etukäteen.  Väitöskirja osoittaa, että oliopohjainen lähestymistapa toimii hyvin konvektiivisten myrskyjen aiheuttamien sähkökatkojen ennustamisessa, ja että vastaavaa metodia voidaan soveltaa myös matalapainemyrskyjen tapauksessa. Väitöskirjassa esitetyt metodit tarjoavat sähköverkko-operaattoreille entistä tarkempia sähkökatkoennusteita. Väitöskirja osoittaa myös, että sään aiheuttamien junien myöhästymisiä voidaan ennustaa mikäli hyvälaatuista koulutusdataa on saatavilla. Tällaiset ennustukset ovat hyvin tärkeitä junaliikenneoperaattoreille haasteellisiin olosuhteisiin varauduttaessa. Oletettavasti samoja lähestymistapoja voidaan hyödyntää myös muilla aloilla, joilla vaikutuksia ovat numeerisesti mallinnettavia ja tunnistettavan säätapahtuman tuottamia sekä kunnollista vaikutusdataa on saatavilla. Väitöskirja vertailee useiden koneoppmismetodeiden soveltuvuutta käsiteltäviin tähtäviin. Tulokset ovat linjassa edellisten tutkimusten kanssa: erityisesti satunnaismetsät ('random forests') tarjosivat toimitavarmoja ennusteita kaikissa tehtävissä, mutta gradienttivahvisteiset puut ('gradient boosting trees'), Gaussiset prosessit ('Gaussian processes') ja tukiverkkokoneet ('support vector machines') toimivat tehtävissä

Using and Interacting with AI-Based Intelligent Technologies: Practical Applications on Autonomous Cars and Chatbots

L'intelligence artificielle (IA) est souvent considérée comme l'une des innovations les plus prometteuses et perturbatrices de notre époque. Malgré son développement rapide, il existe encore un haut niveau d'incertitude quant à la manière dont les consommateurs vont adopter l'IA. Dans ce contexte, cette thèse de quatre articles vise à comprendre comment les consommateurs utilisent et interagissent avec les technologies intelligentes, en se concentrant en particulier sur deux applications: les chatbots et les véhicules autonomes (VA). Dans un premier temps, nous effectuons une analyse approfondie de la littérature marketing existante en adoptant les approches scientométriques et la méthode Theory-Context-Characteristics-Methodology. Ainsi, nous définissons nos questions de recherche concernant 1) les réactions cognitives et émotionnelles des consommateurs lorsqu'ils interagissent avec des technologies basées sur l'IA capables de simuler des conversations de type humain ; 2) les facteurs affectant l'intention des consommateurs d'utiliser des technologies basées sur l'IA, et leur évolution à travers les niveaux d'automatisation ; 3) les préoccupations éthiques des consommateurs envers les produits IA et leur effet sur la confiance et les intentions d'utilisation. En mettant en œuvre trois plans expérimentaux inter-sujets, nous répondons à notre première question de recherche en comparant les interactions humain-humain et humain-chatbot et les interactions avec des chatbots hautement anthropomorphes et faiblement anthropomorphes. Nous nous appuyons principalement sur la Théorie de l'Evaluation Cognitive des Emotions (Roseman et al. 1990), la Théorie de l'Attribution (Weiner 2000) et la Théorie de l'Anthropomorphisme (Aggarwal and McGill 2007 ; Epley et al. 2018), en montrant que les réponses des consommateurs diffèrent lorsqu'ils interagissent avec un humain et un chatbot, en fonction des différentes attributions de responsabilité et des différents niveaux d'anthropomorphisme. Ensuite, nous étudions la manière dont l'expérience des consommateurs avec différents niveaux d'automatisation affecte les perceptions des technologies basées sur l'IA. Nous utilisons les VA comme unité d'analyse, en intégrant le cadre UTAUT avec la Théorie de la Confiance (Mcknight et al. 2011), la Théorie du Calcul de la Vie Privée (Dinev et Hart 2006) et la Théorie du Bien-être (Diener 1999). Après la mise en œuvre d'un design intra-sujet avec des études sur le terrain et sur simulateur, les résultats suggèrent que la différenciation entre les différents niveaux d'automatisation joue un rôle clé pour mieux comprendre les facteurs d’adoption ainsi que les réactions cognitives lors de l'utilisation d'applications intelligentes. Enfin, nous étudions les préoccupations éthiques des consommateurs concernant les chatbots et les VA. Nous utilisons une approche mixte, en utilisant la modélisation thématique et la modélisation par équation structurelle. Nous montrons que pour les chatbots, la composante interactionnelle et émotionnelle de la technologie est prédominante, les consommateurs soulignant, entre autres, le design émotionnel et le manque d'adaptabilité comme principaux soucis éthiques. En revanche, pour les VA, les préoccupations éthiques concernent plutôt des perceptions cognitives liées à la transparence des algorithmes, à la sécurité de la technologie et à l'accessibilité. Notre recherche offre des contributions à la littérature émergente sur les comportements des consommateurs liés aux produits intelligents en soulignant la nécessité de prendre en compte la complexité des technologies d'IA à travers leurs différents niveaux d'automatisation et en fonction de leurs caractéristiques. Nous offrons également des contributions méthodologiques grâce à la mise en œuvre de plans de recherche expérimentaux innovants, utilisant des outils avancés et combinant des approches qualitatives et quantitatives. […]Artificial Intelligence (AI) is often considered as one of the most promising and disruptive innovation of our times. Despite its rapid development, there is still a high level of uncertainty about how consumers are going to adopt AI. In this context, this four-article dissertation aims to comprehend how consumers use and interact with intelligent technologies, in particular focusing on two current applications: chatbots and autonomous vehicles (AVs). First, we conduct an in-depth analysis of the existing marketing literature adopting Scientometric and Theory-Context-Characteristics-Methodology approaches. Thus, we define our research questions related to 1) consumers ‘cognitive and emotional reactions when interacting with AI-based technologies that are able to simulate human-like conversations; 2) factors affecting consumers ‘intention to use AI-based technologies able to make decision in critical situations, and their evolution across levels of automation; 3) consumers ethical concerns towards AI products and their effect on trust and usage intentions. By applying three between-subject experimental designs, we answer our first research question comparing human–human versus human–chatbot interactions and highly anthropomorphic versus lowly anthropomorphic chatbots. We leverage insights mainly from Cognitive Appraisal Theory of Emotions (Roseman et al. 1990), Attribution Theory (Weiner 2000) and Theory of Anthropomorphism (Aggarwal and McGill 2007; Epley et al. 2018), showing that consumers’ responses differ when interacting with a human and a chatbot, according to the different attributions of responsibility and the different levels of anthropomorphism of the service agent. Next, we investigate the way consumers’ experience with different levels of automation affect perceptions of AI-based technologies. We use AVs as unit of analysis, integrating the UTAUT framework with Trust Theory (Mcknight et al. 2011), Privacy Calculus Theory (Dinev and Hart 2006) and Theory of Well-being (Diener 1999; Diener and Chan 2011). After implementing a within subject-design with field and simulator studies, results suggest that differentiating between the different automation levels play a key role to better understand the potential drivers of adoption as well as the cognitive reactions when using intelligent applications. Finally, we investigate consumers’ ethical concerns surrounding chatbots and AVs. We employ a mixed methods approach, using topic modeling and structural equation modeling. We show that for chatbots, the interactional and emotional component of the technology is predominant, as consumers highlight, between others, the emotional design and the lack of adaptability as main ethical issues. However, for autonomous cars, the ethical concerns rather involve cognitive perceptions related to the transparency of the algorithms, the ethical design, the safety of the technology and the accessibility. Our research offers contributions to the emerging literature on consumer behaviors related to intelligent products by highlighting the need to take into account the complexity of AI technologies across their different levels of automation and according to their intrinsic characteristics. We also offer methodological contributions thanks to the implementation of innovative experimental research designs, using advanced tools and combining qualitative and quantitative approaches. To conclude, we present implications for both managers and policymakers who want to implement AIbased disruptive technologies, such as chatbots and AVs

Worker and Public Health and Safety

This book on "Worker and Public Health and Safety: Current Views" brings together current scholarly work and opinions in the form of original papers and reviews related to this field of study. It provides important and recent scientific reading as well as topical medical and occupational information and research in areas of immediate relevance, such as chronic and occupational diseases, worker safety and performance, job strain, workload, injuries, accident and errors, risks and management, fitness, burnout, psychological and mental disorders including stress, therapy, job satisfaction, musculoskeletal symptoms and pain, socio-economic factors, dust pollution, pesticides, noise, pathogens, and related areas

Using GUHA Data Mining Method in Analyzing Road Traffic Accidents Occurred in the Years 2004–2008 in Finland

Abstract The suitability of the GUHA data mining method in analyzing a big data matrix is studied in this report in general, and, in particular, a data matrix containing more than 80,000 road traffic accidents occurred in Finland in 2004–2008 is examined by LISp-Miner, a software implementation of GUHA. The general principles of GUHA are first outlined, and then, the road accident data is analyzed. As a result, more than 10,000 associations and dependencies, called hypothesis in the GUHA language, were found; some easily understandable of them are presented here. Our conclusion is that the GUHA method is useful, in particular when one wants to explore relatively small size, but still significant dependencies in a given large data matrix