A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

EXPLORING THREAT-SPECIFIC PRIVACY ASSURANCES IN THE CONTEXT OF CONNECTED VEHICLE APPLICATIONS

Connected vehicles enable a wide range of use cases, often facilitated by smartphone apps and involving extensive processing of driving-related data. Since information about actual driving behavior or even daily routines can be derived from this data, the question of privacy arises. We explore the impact of privacy assurances on driving data sharing concerns. Specifically, we consider two data-intensive cases: usage-based insurance and traffic hazard warning apps. We conducted two experimental comparisons to investigate whether and how privacy-related perceptions about vehicle data sharing can be altered by different types of text-based privacy assurances on fictional app store pages. Our results are largely inconclusive, and we did not find clear evidence that text-based privacy guarantees can significantly alter privacy concerns and download intentions. Our results suggest that general and threat-specific privacy assurance statements likely yield no or only negligible benefits for providers of connected vehicle apps regarding user perceptions

Factors influencing drivers’ acceptance of in-vehicle monitoring

This study investigates the influencing factors that affect drivers’ acceptance of products or services, that implement in-vehicle monitoring. The rapid growth of the sharing economy, aided by smartphones, results in many innovative automotive applications from commercial service providers, such as Uber, MaaS or carsharing applications. However, many projects that try to introduce new business models, using in-vehicle monitoring, ultimately were not received favorably. To investigate the factors, a qualitative analysis and ecosystem approach were used; 19 stakeholders, consisting of regular and professional drivers, as well as automotive-related organisations, unions, transport and research agencies, were interviewed and their inputs were analysed to provide a starting reference of the influencing factors. The study found that there are 9 factors that influence driver’s acceptance of in-vehicle monitoring: (1) Comparing benefits and costs, (2) Privacy, (3) Autonomy of driver, (4) Driver’s ideals and morale, (5) Ownership of vehicle, (6) Trust, (7) Design of system, (8) Awareness of technology, and (9) Media and marketing. Organisations are encouraged to consider these influencing factors when designing their products and services. The study recommends that organisations design products and services that appeals to the drivers’ motivation and perspective of what is important to them during their drive. In addition, technical considerations for data privacy, security and trust are presented. Finally, the overall design and marketing recommendations for organisations are presented

The cognitive and affective antecedents to consumer behavior towards on-demand transportation services in Egypt

In the recent few years, smartphones have shaped and assisted in the creation of new business models to formulate and develop some additional dimensions such as shared-economy or shared-mobility. Since transportation is one of the most essential aspects of shared-economy, it is vital to this study to focus and investigate the consumers’ intention to use the new commuting services provided by Transportation Network Companies (TNCs) in Egypt. Consequently, this research aims to examine and understand the cognitive and affective antecedents to consumers’ behavior towards TNCs in Egypt. Therefore, the model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) has been applied to understand and explain the factors that influence the behavioral intention (BI) to use TNCs services. The factors of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT) tested through surveying 200 respondents thru online (Google Forms) and offline (Self-Administered Questionnaires) techniques. The results showed that consumers’ intention to use TNCs services in Egypt, was positively affected by the factors of (performance expectancy, social influence, price value, and habit). However, the variables of (effort expectancy, facilitating conditions, and hedonic motivation) showed a negative influence on the intention to use TNCs services in Egypt. Thus, upon the evaluation of the gathered data and discovered findings, the market acceptance and share of TNCs services can be increased if these services considered the factors affecting the consumers\u27 intention that mentioned earlier

Designing an On-Demand Dynamic Crowdshipping Model and Evaluating its Ability to Serve Local Retail Delivery in New York City

Nowadays city mobility is challenging, mainly in populated metropolitan areas. Growing commute demands, increase in the number of for-hire vehicles, enormous escalation in several intra-city deliveries and limited infrastructure (road capacities), all contribute to mobility challenges. These challenges typically have significant impacts on residents’ quality-of-life particularly from an economic and environmental perspective. Decision-makers have to optimize transportation resources to minimize the system externalities (especially in large-scale metropolitan areas). This thesis focus on the intra-city mobility problems experienced by travelers (in the form of congestion and imbalance taxi resources) and businesses (in the form of last-mile delivery), while taking into consideration a measurement of potential adoption by citizens (in the form of a survey). To find solutions for this mobility problem this dissertation proposes three distinct and complementary methodological studies. First, taxi demand is predicted by employing a deep learning approach that leverages Long Short-Term Memory (LSTM) neural networks, trained over publicly available New York City taxi trip data. Taxi pickup data are binned based on geospatial and temporal informational tags, which are then clustered using a technique inspired by Principal Component Analysis. The spatiotemporal distribution of the taxi pickup demand is studied within short-term periods (for the next hour) as well as long-term periods (for the next 48 hours) within each data cluster. The performance and robustness of the LSTM model are evaluated through a comparison with Adaptive Boosting Regression and Decision Tree Regression models fitted to the same datasets. On the next study, an On-Demand Dynamic Crowdshipping system is designed to utilize excess transport capacity to serve parcel delivery tasks and passengers collectively. This method is general and could be expanded and used for all types of public transportation modes depending upon the availability of data. This system is evaluated for the case study of New York City and to assess the impacts of the crowdshipping system (by using taxis as carriers) on trip cost, vehicle miles traveled, and people travel behavior. Finally, a Stated Preference (SP) survey is presented, designed to collect information about people’s willingness to participate in a crowdshipping system. The survey is analyzed to determine the essential attributes and evaluate the likelihood of individuals participating in the service either as requesters or as carriers. The survey collects information on the preferences and important attributes of New York citizens, describing what segments of the population are willing to participate in a crowdshipping system. While the transportation problems are complex and approximations had to be done within the studies to achieve progress, this dissertation provides a comprehensive way to model and understand the potential impact of efficient utilization of existing resources on transportation systems. Generally, this study offer insights to decisions makers and academics about potential areas of opportunity and methodologies to optimize the transportation system of densely populated areas. This dissertation offers methods that can optimize taxi distribution based on the demand, optimize costs for retail delivery, while providing additional income for individuals. It also provides valuable insights for decision makers in terms of collecting population opinion about the service and analyzing the likelihood of participating in the service. The analysis provides an initial foundation for future modeling and assessment of crowdshipping

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Car Infotainment: An early analysis of driver perceptions towards apps in the car

Driven by technological advances, the vision of a Connected Car finally becomes reality. As one of the Connected Car innovations, Car Infotainment Systems now get an internet connection. Following the example of the mobile industry, app ecosystems are about to emerge in cars. In-Vehicle technology has already become the new differentiation battleground in the automotive industry. Being technologically possible, however, does not guarantee the success of app-based Car Infotainment Systems. It is not clear whether these systems are appreciated by car drivers, seeing that apps not necessarily provide assistance for driving, but in contrast can be a source of driver distraction and thus threaten traffic safety. It was therefore the purpose of this study to explain the perceptions of car drivers towards Car Infotainment Systems that provide access to an App ecosystem and thereby determine success factors from a user’s perspective. For this reason, a research model that extends the Technology Acceptance Model with hypothetical factors has been proposed based on a literature review on driver acceptance. By analyzing data collected through an online survey, perceptions have been measured and nine hypotheses among these factors have been tested. It could be shown that drivers’ perceptions of Car Infotainment Systems are slightly positive. Task-technology-fit, usefulness, ease of use, risk and costs could be approved as being influencing factors of the behavioral intention to use Car Infotainment Systems. However, the perceived risk seems to have no direct influence. Implications for both practice and academia could be drawn from these results

Perceived privacy risk in the Internet of Things: determinants, consequences, and contingencies in the case of connected cars

The Internet of Things (IoT) is permeating all areas of life. However, connected devices are associated with substantial risks to users’ privacy, as they rely on the collection and exploitation of personal data. The case of connected cars demonstrates that these risks may be more profound in the IoT than in extant contexts, as both a user's informational and physical space are intruded. We leverage this unique setting to collect rich context-immersive interview (n = 33) and large-scale survey data (n = 791). Our work extends prior theory by providing a better understanding of the formation of users’ privacy risk perceptions, the effect such perceptions have on users’ willingness to share data, and how these relationships in turn are affected by inter-individual differences in individuals’ regulatory focus, thinking style, and institutional trust