Recommendations for a large-scale European naturalistic driving observation study. PROLOGUE Deliverable D4.1.

Naturalistic driving observation is a relatively new research method using advanced technology for in-vehicle unobtrusive recording of driver (or rider) behaviour during ordinary driving in traffic. This method yields unprecedented knowledge primarily related to road safety, but also to environmentally friendly driving/riding and to traffic management. Distraction, inattention and sleepiness are examples of important safety-related topics where naturalistic driving is expected to provide great added value compared to traditional research methods. In order to exploit the full benefits of the naturalistic driving approach it is recommended to carry out a large-scale European naturalistic driving study. The EU project PROLOGUE has investigated the feasibility and value of carrying out such a study, and the present deliverable summarises recommendations based on the PROLOGUE project

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

A Research Approach to Study Human Factors in Transportation Systems

This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment.This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment

A Review of Research on Driving Styles and Road Safety

Objective: To outline a conceptual framework for understanding driving style and, based on this, review the state-of-the-art research on driving styles in relation to road safety.</br></br> Background: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. </br></br> Method: A systematic literature search was conducted, including empirical, theoretical and methodological research on driving styles related to road safety. </br></br> Results: A conceptual framework was proposed where driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorising and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and socio-cultural factors influencing driving style were reviewed. </br></br> Conclusion: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesise these results and make useful generalisations. There is a considerable potential for increasing road safety by means of behaviour modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. </br></br> Application: Knowledge about driving styles can be applied in programmes for modifying driver behaviour and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems

Safety impacts of using smartphone voice control interfaces on driving performance

Distraction from the use of mobile phones has been identified as one of the causes of road traffic crashes. Voice control technology has been suggested as a potential solution to driver distraction by the manual use of mobile phones. However, new evidence has shown that using voice control interfaces while driving could require more from drivers in terms of cognitive load and visual attention compared to using a mobile phone manually. Further, several factors that moderate the use of voice control interfaces, for example, usability and acceptance are poorly understood. Thus, the current study aims to investigate the safety impact of using voice control interfaces on driving performance. A preliminary study, an online survey and a driving experiment were conducted to investigate how drivers interact with smartphone voice control interfaces and their effects on driving performance. First, the usage pattern of voice control interfaces while driving was explored using focus groups and interviews (preliminary study) and an online survey. Next, 55 participants completed a simulated driving task that utilises a valid and standardised method called the Lane Change Test (LCT). The purpose was to measure degradation of driving performance due to the concurrent performance of secondary tasks; either contact calling, playing music or text messaging task. These secondary tasks were identified as common tasks in the survey of the pattern of use of voice control interfaces while driving. Secondary tasks were performed in both visual-manual and voice control modes with either an Apple or a Samsung smartphone. Data on eye glance behaviour, workload and, usability and acceptance of the voice control interfaces were also collected. Findings support the view that interacting with voice control interfaces while driving reduces distraction from visual-manual interfaces but is still distracting compared to driving without using any devices. Texting was found to degrade task and driving performance regardless of control modes and phone type. Moreover, poor system performance leads to low acceptance of voice control technology. Smartphone voice control interfaces have an apparent advantage over visual-manual interfaces. However, they still can impose some elements of distraction that may have negative implications for road safety