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The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.

A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions.

Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.

The results provide support to guide countermeasures and vehicle design.

This paper aims to examine driving retirement and its impact on the well-being of older citizens. The concepts of value creation and destruction are used to understand older consumers’ experiences with the self-service consumption activity of driving. This paper formally introduces the concept of value re-creation, as a means of restoring the overall value lost from the destruction of certain components of previous value structures. In doing so, this paper explores the different ways that resources across the micro, meso and macro levels of the ecosystem can be re-aligned, in order for older citizens to maintain their well-being after driving retirement.

A qualitative, individual-depth interview approach was undertaken with 26 participants living in New South Wales, Australia. The participants comprised of both drivers approaching driving retirement age, as well as driving retirees. Thematic analysis was undertaken to analyse the data.

The findings identified that emotional value in the forms of freedom, independence/autonomy and enjoyment, functional value in the forms of convenience and mobility and community value are created from driving. Driving retirement destroys certain components of this value (e.g. enjoyment and convenience) irrevocably, however freedom, independence/autonomy, mobility and social connectedness can still be maintained through re-aligning resources across the micro, meso and macro levels of the ecosystem. New components of value are also created from driving retirement. These include peace of mind, which contributes to the re-creation of the emotional value dimension, and cost savings, which creates the new value dimension of economic value. These changes to the value structure effectively re-create the overall value obtained by individuals when they retire from driving.

The main contribution of this work is the formal introduction of the concept of value re-creation at the overall and value dimension level, and development of a conceptual model that explains how this value re-creation can occur. The model shows the resource contributions required across all levels of the ecosystem, expanding on existing conceptualisations that have predominantly focussed on resource contributions at the individual and service levels.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.