WiFi Hot Spot Service Business for the Automotive and Oil Industries: A Competitive Analysis

While you refuel for gas, why not refuel for information or upload vehicle data, using a cheap wireless technology as WiFi? This paper analyzes in extensive detail the user segmentation by vehicle usage, service offering, and full business models from WiFi hot spot services delivered to and from vehicles (private, professional, public) around gas stations. Are also analyzed the parties which play a role in such services: authorization, provisioning and delivery, with all the dependencies modelled by attributed digraphs. Account is made of WiFi base station technical capabilities and costs. Five year financial models (CAPEX, OPEX), and data pertain to two possible service suppliers: multi-service oil companies, and mobile service operators (or MVNOs). Model optimization on the return-on-investment (R.O.I.) is carried out for different deployment scenarios, geographical coverage assumptions, as well as tariff structures. Comparison is also being made with public GPRS and 3G data services, as precursors to HSPA/LTE, and the effect of WiFi roaming is analyzed. Regulatory implications, including those dealing with public safety, are addressed. Analysis shows that due to manpower costs and marketing costs, suitable R.O.I. will not be achieved unless externalities are accounted for and innovative tariff structures are introduced. Open issues and further research are outlined. Further work is currently carried out with automotive electronics sector, wireless systems providers, wireless terminals platform suppliers, and vehicle manufacturers. Future relevance of this work is also discussed for the emerging electrical reloading grids for electrical vehicles.WiFi, Fuel Stations, Business Models, Oil Company, Mobile Operator, WiFi Services, Regulations, Professional Vehicles

Data-Driven Evaluation of In-Vehicle Information Systems

Today’s In-Vehicle Information Systems (IVISs) are featurerich systems that provide the driver with numerous options for entertainment, information, comfort, and communication. Drivers can stream their favorite songs, read reviews of nearby restaurants, or change the ambient lighting to their liking. To do so, they interact with large center stack touchscreens that have become the main interface between the driver and IVISs. To interact with these systems, drivers must take their eyes off the road which can impair their driving performance. This makes IVIS evaluation critical not only to meet customer needs but also to ensure road safety. The growing number of features, the distraction caused by large touchscreens, and the impact of driving automation on driver behavior pose significant challenges for the design and evaluation of IVISs. Traditionally, IVISs are evaluated qualitatively or through small-scale user studies using driving simulators. However, these methods are not scalable to the growing number of features and the variety of driving scenarios that influence driver interaction behavior. We argue that data-driven methods can be a viable solution to these challenges and can assist automotive User Experience (UX) experts in evaluating IVISs. Therefore, we need to understand how data-driven methods can facilitate the design and evaluation of IVISs, how large amounts of usage data need to be visualized, and how drivers allocate their visual attention when interacting with center stack touchscreens. In Part I, we present the results of two empirical studies and create a comprehensive understanding of the role that data-driven methods currently play in the automotive UX design process. We found that automotive UX experts face two main conflicts: First, results from qualitative or small-scale empirical studies are often not valued in the decision-making process. Second, UX experts often do not have access to customer data and lack the means and tools to analyze it appropriately. As a result, design decisions are often not user-centered and are based on subjective judgments rather than evidence-based customer insights. Our results show that automotive UX experts need data-driven methods that leverage large amounts of telematics data collected from customer vehicles. They need tools to help them visualize and analyze customer usage data and computational methods to automatically evaluate IVIS designs. In Part II, we present ICEBOAT, an interactive user behavior analysis tool for automotive user interfaces. ICEBOAT processes interaction data, driving data, and glance data, collected over-the-air from customer vehicles and visualizes it on different levels of granularity. Leveraging our multi-level user behavior analysis framework, it enables UX experts to effectively and efficiently evaluate driver interactions with touchscreen-based IVISs concerning performance and safety-related metrics. In Part III, we investigate drivers’ multitasking behavior and visual attention allocation when interacting with center stack touchscreens while driving. We present the first naturalistic driving study to assess drivers’ tactical and operational self-regulation with center stack touchscreens. Our results show significant differences in drivers’ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. These results emphasize the importance of context-dependent driver distraction assessment of driver interactions with IVISs. Motivated by this we present a machine learning-based approach to predict and explain the visual demand of in-vehicle touchscreen interactions based on customer data. By predicting the visual demand of yet unseen touchscreen interactions, our method lays the foundation for automated data-driven evaluation of early-stage IVIS prototypes. The local and global explanations provide additional insights into how design artifacts and driving context affect drivers’ glance behavior. Overall, this thesis identifies current shortcomings in the evaluation of IVISs and proposes novel solutions based on visual analytics and statistical and computational modeling that generate insights into driver interaction behavior and assist UX experts in making user-centered design decisions

The potential of naturalistic driving studies with simple data acquisition systems (DAS) for monitoring driver behaviour

This report addresses the important question regarding the potential of simple and low-cost technologies to address research questions such as the ones dealt with in UDrive. The resources and efforts associated with big naturalistic studies, such as the American SHRP II and the European UDrive, are tremendous and can not be repeated and supported frequently, or even more than once in a decade (or a life time..). Naturally, the wealth and richness of the integrated data, gathered by such substantial studies and elaborated DAS, cannot be compared to data collected via simpler, nomadic data collection technologies. The question that needs to be asked is how many Research Questions (RQs) can be addressed, at least to some extent, by other low-cost and simple technologies? This discussion is important, not only in order to replace the honourable place (and cost!) of naturalistic studies, but also to complement and enable their continuity after their completion. Technology is rapidly evolving and almost any attempt to provide a comprehensive and complete state of the art of existing technologies (as well as their features and cost) is doomed to fail. Hence, in chapter 1 of this report, we have created a framework for presentation, on which the various important parameters associated with the question at hand, are illustrated, positioned and discussed. This framework is denoted by “Framework for Naturalistic Studies” (FNS) and serves as the back bone of this report. The framework is a conceptual framework and hence, is flexible in the sense that its dimensions, categories and presentation mode are not rigid and can be adjusted to new features and new technologies as they become available. The framework is gradually built using two main dimensions: data collection technology type and sample size. The categories and features of the main dimensions are not rigidly fixed, and their values can be ordinal, quantitative or qualitative. When referring to parameters that are not numerical –even the order relation among categories is not always clear. In this way –the FNS can be, at times, viewed as a matrix rather than a figure with order relation among categories presented along its axes. On the two main dimensions of the FNS –data collection technology type and sample size –other dimensions are incorporated. These dimensions include: cost, data access, specific technologies and research questions that can be addressed by the various technologies. These other dimensions are mapped and positioned in the plot area of the FNS. Other presentations, in which the axes and the plot area are interchanged, or 3 -dimensional presentations are performed, can be incorporated to highlight specific angles of the involved dimensions. The various technologies for data collection were mapped on the FNS. The technology groups include: mobile phone location services, mobile phone applications, telematics devices, built -in data loggers, dash cameras and enhanced dash cameras, wearable technologies, compound systems, eye trackers and Mobileyetype technologies. After this detailed illustrations of analyses that can be conducted using simple low-cost technologies are described. It is demonstrated how temporal and spatial analysis can reveal important aspects on the behavioural patterns of risky drivers. Also one stand alone smartphone app can be used to monitor and evaluate smartphone us age while driving. Most of the simple systems relate to specific behaviour that is monitored (i.e. speeding , lane keeping etc.). Additionally, certain thresholds or triggers are used to single out risky situations, which are related to that behaviour. However, once those instances are detected, no information on the circumstances leading or accompanying this behaviour are available. Typically, visual information (discrete or preferably continuous) is needed in order to fully understand the circumstances. Hence, upgrading simple (single-task oriented) technologies by other technologies (most typically by cameras), can significantly improve researchers' ability to obtain information on the circumstances, which accompany the detected risky behaviour. One of the most conceptually straightforward integrated systems is a system, for which the basic technology detects the desired behaviour (e.g. harsh braking) and triggers a simple continuous dashboard camera to save the relevant information, which occurs together with that behaviour. Many RQs can be addressed using this type of combined systems

Design of a data-driven communication framework as personalized support for users of ADAS

Recently the automotive industry has made a huge leap forward in Automated Driver Assistance Systems (ADAS) development, increasing the level of driving processes automation. However, ADAS design does not imply any individual support to the driver; this results in a poor understanding of how the ADAS works and its limitations. This type of driver uncertainty regarding ADAS performance can erode the user\u27s trust in the system and result in decreasing situations when the system is in use. This paper presents the design of a data-driven communication framework that can utilize historical and real-time vehicle data to support ADAS users. The data-driven communication framework aims to illustrate the ADAS capabilities and limitations and suggests effective use of the system in real-time driving situations. This type of assistance can improve a driver\u27s understanding of ADAS functionality and encourage its usage

A Descriptive Research of drivers and stumbling blocks in the Sustainable supply chain: (Specially focused on the Indian Automotive Industries).

This study outlined the main drivers and barriers of sustainability in the supply chain in the context of the automobile industry. For the profound understanding of how these practices are implemented, scholar proposed two research objectives to understand the measures that companies have taken on implementing sustainability and how farther are their implementation of different sustainable practices. This study involved five automobile companies with operations in different cities of India. After systematic investigation of current literature in sustainable supply chain management, scholar noticed that there is a gap in research since there are significantly fewer studies on the drivers and barriers for the implementation of the sustainable supply chain. To achieve the objectives, the scholar selected qualitative research approach. By using a qualitative research technique, the scholar focused on getting complete information related to the sense of different firms’ employees, such as purchasing manager or head engineer. With the help of semi-structured interviews, scholar obtained a thorough understanding of the drivers and barriers that firms face when an attempt to implement sustainable practices. The results of the study disclosed that there are various motivators and barriers to sustainable supply chain implementation. Stakeholders such as customers and government were referred to by participants as driving forces for the association of sustainable practices in the automobile firms. Government policies and regulations are a powerful driver for enhancing sustainable practices for firms. However, the lack of policies may diminish the pace of sustainability. To be competitive in the global market, sustainable relations with all kinds of internal and external stakeholders is essential when implementing sustainability in the automotive supply chain

Supplier collaboration for sustainability: a study of UK food supply chains

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.Achieving sustainability in the supply chain is not a choice but an inevitable necessity for the organisation to survive and thrive in the long run. Supplier collaboration to achieve sustainability is widely recognised but poorly studied phenomena. While there is a handful of studies that focused on collaboration for sustainability in food supply chains, only a few considered sustainable (i.e. environmental, cost and social) or Triple Bottom Line (TBL) performance, and in the context of UK food industry, there is hardly any study. Building on previous studies, this thesis addressed these concerns conceptually and empirically by: a) examining supplier collaboration for sustainable performance; b) assessing supplier collaboration for environment friendly and socially responsible practices; c) measuring environment friendly and socially responsible practices for sustainable performance; and d) validating environment friendly and socially responsible practices as the mediators for supplier collaboration and sustainable performance. To achieve these objectives, first, a structured literature review was performed and identified 61 studies that documented supplier collaboration for sustainability, and a comprehensive review was also conducted to expand the research domain. Second, underpinned by Relational View (RV) theory, a set of 17 testable hypotheses (including sub-hypotheses) were developed, and a survey method was used to collect 203 useable data from UK based food businesses who maintain collaborative relationships with their suppliers. Finally, for data analysis, Partial Least Squared- Structural Equations Modelling (PLS-SEM) technique was used with SmartPLS3 software. The empirical findings validated that: a) supplier collaboration improves environmental, cost and social performance; b) supplier collaboration contributes to improved environment friendly and socially responsible practices; c) environment friendly practices enhance environmentally, cost and social performance; d) socially responsible practices have an impact on environmental and social performance, however socially responsible practices do not have an impact on cost performance; e) environment friendly and socially responsible practices mediate the relationship between supplier collaboration and sustainable performance. The results suggest that supplier collaboration enhances environment-friendly and socially responsible practices which will lead to enhanced environmental, cost and social performance. The contributions of this research to supply chain management literature are: a) to achieve sustainable performance in the food supply chain, collaboration with the suppliers is essential; b) collaborating with the suppliers, firms can improve their environment friendly and socially responsible practices; c) socially responsible practices in the supply chain enhance environmental and social performance but do not improve cost performance; c) this study extends the Relational View theory (RV) from the relation-specific assets for sustainable performance to the relation-specific assets for environmentally friendly and socially responsible practices which lead to sustainable performance. This study found that inter-organisational relationship facilitates environment-friendly and socially responsible practices which will lead to improved sustainable performance. For practitioners, this study offers the sustainability framework that suggests for greater collaboration with the suppliers to improve environment-friendly and socially responsible practices which should lead to a sustainable performance in the food industry. For the policymakers, this study offers a unique proposition to encourage a collaborative environment in the supply chain to achieve sustainable performance in the food industry

Sustainable development of operations: Actors’ involvement in the process of energy efficiency improvements

This study empirically investigates the involvement of actors in the process of energyefficiency improvements in operations to align strategic sustainability goals across and within operations. The study analyzes development efforts stemming from actors’ decisions and actions that contribute to the process of energy efficiency improvements using semi-structured interviews and secondary information. Data is analyzed using thematic coding. The study deepens the understanding of how firms undertake the transition towards integrating strategic goals for energy efficiency into operations by strategizing for energy efficiency improvements through actors’ involvement. By exploring actors at both strategic and operational levels, and their decisions and actions, the study includes examples of different approaches, namely, top-down vs. bottom-up and inside-out vs. outside-in, thereby conceptualizing the process of energy-efficiency improvements in terms of a framework that outlines the entities of this process. The study further provides an integrative framework for the development efforts by different actors and presents propositions for incorporating energy-efficiency improvements in daily strategic and operational decisions and actions instead of regarding it as a separate or an add-on process

Autonomous driving: are we ready to accept it? A study about information influences on technology acceptance

In this era of technology we live in, transportation is the subject of rapid developments with brands racing, with a focus on becoming the pioneer in launching a stand-alone car. The planned dates for public release are close, however today there are only prototypes in test with level of automation 4. Being a recent subject, little information exists for both consumers or companies, so the study proposes to identify the information and technology that can influence opinion on these cars. The Innovation Adoption in Robotics (IAR) model was selected as the study’s theorical reference to study the intention of adopting autonomous vehicles, to determine the variables and construct a questionnaire to collect opinions and quantify other variables related to car acceptance. This model is based on the Technology Acceptance Model (TAM), developed by Davis (1989) and the Diffusion of Innovations Model (DIM) developed by Rogers' (1983). It is noted through the study that the subject is known the inquirers, although information about it is scarce, being evoked by them the need to obtain further progress in technology, to experiment and to obtain more information. Only by mitigating the abovementioned faults, uncertainties as to the reliability of the system and the advantages could be better clarified. Absence of information currently available about this technology and uncertainties regarding price, preparation of countries’ preparation and of the population itself, together with the impossibility of experiencing the car make these technology’s acceptance difficult.Nesta era de tecnologia em que nos encontramos os transportes são alvo de rápidos desenvolvimentos e corridas à inovação pelas marcas, com o foco de se tornarem pioneiros no lançamento de um carro autónomo. As datas previstas para lançamento ao público estão próximas, ainda que hoje apenas existam ainda protótipos em teste com nível de automação 4. Sendo este um tema recente, pouca informação existe tanto para os consumidores ou empresas, assim este estudo propõe-se a identificar a informação e tecnologia atuais que podem influenciar a opinião sobre estes carros. Foi selecionado o modelo Innovation Adoption in Robotics (IAR) como referencial teórico, uma vez que este estuda a intenção de adoção de veículos autónomos, para determinação das variáveis. Optou-se pela construção de um questionário, de forma a permitir recolher opiniões e medir quantitativamente outras variáveis relacionadas com a aceitação do carro. Este modelo é baseado no Technology Acceptance Model (TAM) desenvolvido por Davis (1989) e no Diffusion of Innovations Model (DIM) desenvolvido por Rogers’ (1983). Afere-se através do estudo que o tema é conhecido, embora de acordo com os inquiridos a informação sobre este é pouca, sendo evocado por estes a necessidade de obter maior progresso na tecnologia, de experimentar e de obter mais informação. Apenas mitigando as faltas referidas anteriormente, as incertezas quanto à fiabilidade do sistema e as vantagens que poderiam ser melhor esclarecidas. Ausência de informação disponível atualmente sobre este tipo de tecnologia e as incertezas em relação ao preço, preparação dos países e da própria população, em conjunto com a impossibilidade de experienciar, o carro tornam a sua aceitação dificultada