Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones.

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers.

On 11 March 2020, the World Health Organization made the assessment that a new disease (COVID-19) caused by a novel coronavirus (SARS-CoV-2) could be characterized as a pandemic. From that point, a chain reaction of events and difficult decisions requiring action was launched. National governments all over the world announced partial or total quarantine lockdowns in an effort to control the virus’ spreading in order to save as many lives as possible. The effects of the pandemic were multifaceted and transport was not excluded. The current paper examines data regarding the level of usage (provided by the administrator) of bike-sharing systems in three case studies/medium-sized Greek cities (Igoumenitsa, Chania, and Rhodes) and through a statistical analysis identifies if there is a correlation between the implemented measures and the modal choice of the residents. The main results and conclusions of this analysis reveal that the level of usage of these specific bike-sharing systems was significantly increased during the lockdown period compared to the situation before the lockdown and the pandemic in general.

One significant and simultaneously interesting problem in urban mobility has to do with the study of shared spaces where various categories of users coexist and act together. This paper aims to examine the behavior and preferences of pedestrians and cyclists, who both coexist in a shared space infrastructure along the seafront (which has a length of around 4.0 km) of the city of Thessaloniki, Greece. Furthermore, the problems caused by the coexistence, such as at the locations where there are pedestrian crossings on the bicycle lane, are recorded and evaluated. Traffic calming measures aimed at improving the existing situation in terms of safety and comfort for both pedestrians and cyclists are also explored. Data were collected through a web-based questionnaire survey, which was distributed via email to students and employees of Aristotle University of Thessaloniki. A total of 1194 questionnaires were collected in the framework of the survey during the year 2021, including responses from both pedestrians and cyclists. The questionnaires were analyzed through the use of descriptive and inferential statistics; the latter method suggested several significant differences in how each group of users (pedestrians or cyclists) perceived their behavior compared with the other. Latent variable and path models were estimated to investigate the behavior and attitude of users towards the crossings, examined as a function of their perception towards the other group; perception about the benefits of the infrastructure; preference for additional interventions; and overall opinion about the quality of the shared space area. The results suggest that forms of aggressive behavior, preference towards using the crossings, and the perceived safety are affected by the aforementioned factors. The results of this study can inform decision takers and decision makers in the area of land use regarding policy recommendations for facilitating interactions between pedestrians and cyclists in shared spaces.