Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Mobility as a Service (MaaS) has been proposed as a user-centric, data-driven and personalised ser-vice. However, full personalisation is not available yet. Customisation settings are developed in mobile appli-cations, and several semi-personalised functionalities are also involved. The quantitative analysis of relation between these two could be the reference for further de-velopment tendency of interface functions in mobile ap-plications. Thus, the research objective is identified as: the quantitative correlation analysis between semi-per-sonalisation functionalities and customisation settings. Accordingly, the multi-criteria qualitative analysis method is applied to identify the assessment aspects regarding mobile applications. The scoring method is also introduced. Then the correlation quantitative anal-ysis method is applied to calculate the correlation coef-ficient. We have assessed 25 MaaS applications regard-ing determined aspects. The correlation coefficients for each application together with the overall coefficient are calculated, the assessment results are summarised, and the correlation tendency is interpreted. According to assessment results, the correlation between custom-isation settings and semi-personalisation is not strong at current stage. Selected MaaS mobile applications are customisation setting oriented applications. Fewer manual selections are expected in further personalised services. Our results facilitate development of further personalised functions in MaaS mobile applications

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

The future of industries : how personalization of insurance policies using artificial intelligence will disrupt the insurance status-quo

In the future, the concept of insurance will change and Artificial Intelligence (AI) is already disrupting the state of this industry. Insurers worldwide are using AI to automatize processes and tasks, such as fraud detention, underwriting and claims processing. Additionally, there has been a rise of new competitors in the market, such as InsurTechs, that are bringing innovative solutions for insurance, responding to the new trends in customers’ lifestyles and behaviours, that are more demanding for services directed for their needs. This study aims to understand how personalization of insurance policies, created with Artificial Intelligence, will disrupt this industry in the future and what will be the impact in the European market. Personalization of an insurance policy with AI would encompass the definition of the coverages and premiums more appropriate for an individual customer and do the risk evaluation, in a market of one strategy. This innovation would take advantage of the accrual of Big Data from customers, as people are each time more connected and information about them is constantly being shared, allowing companies to use it to know consumers better. Some limitations that might arise are related to the regulation applied to the insurance industry in Europe regarding customer’s data privacy, with the GDPR and regulation against discrimination in insurance.No futuro, o conceito dos seguros irá alterar-se e a Inteligência Artificial (IA) já está a causar disrupção no estado desta indústria. Seguradoras por todo o mundo já utilizam IA para automatizar processos e tarefas, como detetar fraudes, na subscrição de seguros ou no processamento de sinistros. Adicionalmente, tem-se assistido a um aumento de concorrentes no mercado, como as InsurTechs, que trazem soluções de seguro inovadoras como resposta às novas tendências nos estilos de vida e comportamentos dos consumidores, que são cada vez mais exigentes e procuram serviços mais direcionados às suas necessidades. A presente dissertação visa estudar como a personalização de apólices de seguro, criadas a partir de Inteligência Artificial, irá disruptar a indústria no futuro, e qual será o impacto no mercado europeu. A personalização de apólices inclui a definição das coberturas e prémio mais apropriados para o consumidor individual, assim como a avaliação do risco, numa estratégia de market of one. Esta inovação tiraria partido da acumulação de Big Data dos clientes, uma vez que os consumidores estão cada vez mais conectados e informação acerca deles está constantemente a ser partilhada, permitindo às empresas conhecê-los melhor. Algumas limitações que poderão surgir estão relacionadas à regulamentação da indústria dos seguros na Europa, relativa à proteção de dados, com o RGPD e com a regulamentação contra a discriminação nos seguros