Providing a sufficiently appropriate route environment is crucial to ensuring fair and safe biking, thus encouraging cycling as a sustainable mode of transport. At the same time, better understanding of cyclists’ preferences regarding the features of their routes and their infrastructure requirements is fundamental to evaluating improvement of the current infrastructure or the development of new infrastructure. The present study has two objectives. The first is to investigate cyclists’ route preferences by means of a choice experiment based on a stated preference survey. Subsequently, the second objective is to compare cyclist preferences in two countries with different cycling characteristics (both in infrastructure as well as cyclists’ behavior). For this purpose, a graphical online stated preferences survey was conducted in Greece and Germany. Within the framework of statistical analyses, multinomial mixed logit discrete choice models were developed that allow us to quantify the trade-offs of interest, while distinguishing between the preferences of different user groups. In addition, user requirements in Greece, as a country with a low cycling share and very little dedicated bike infrastructure, were compared to the requirements in Germany, where cycling is popular and the infrastructure is well developed. The results over the whole sample indicate that subgroups value infrastructure differently according to their specific needs. When looking at country specifics, users from Greece are significantly more willing to accept longer travel times in return for higher-quality facilities. The utility of low speed limits in mixed traffic is also different. In Germany, low speed limits offset the disturbance caused by motorized traffic, but in Greece they do not. Consequently, the results help to asses which types of infrastructure are most sustainable from a user perspective and help to set priorities when the aim is to adapt the road infrastructure efficiently in a stable strategy.

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures. Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures. Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance. Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.

Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

Considering that unexpected events are a major contributory factor of road accidents the main objective of this article is to investigate the effect of several parameters including overall driving performance, distraction sources, driver characteristics, as well as road and traffic environment on accident probability at unexpected incidents. For this purpose, a driving simulator experiment was carried out, in which 95 participants from all age groups were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in different road and traffic conditions. Then, in the framework of the statistical analysis, driving performance is estimated as a new unobserved (latent) variable based on several individual driving simulator parameters while a structural equation model is developed investigating which factors lead to increased accident probability at unexpected incidents. Regarding driver distraction, results indicate that cell phone use has a negative effect on accident risk confirming the initial hypothesis that when talking on the cell phone drivers find it difficult to handle an unexpected incident and as a result are more likely to commit an accident. Overall, a risky driving profile is developed, completing the puzzle of the effect of driver distraction on driver behavior and road safety.

Different methodologies are being used to study the effects of autonomous vehicles (AVs) in mixed traffic to exhibit the interactions between autonomous and human-driven vehicles (HVs). Microscopic simulation tools are popular in such an assessment, as they offer the possibility to experiment in economical, robust, and optimistic ways. A lack of reliable real-world data (also known as natural data) to calibrate and evaluate the connected autonomous vehicle (CAV) simulation model is a major challenge. To deal with this situation, one interesting methodology could be to deal with the CAVs as conventional human-driven vehicles and predict their possible characteristics based on the simulation inputs. The conventional human-driven vehicles from the real world, in this methodology, come to act as a benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving behaviors, a sensitivity analysis of the behaviors of AVs and an effective assessment of CAVs in a mixed traffic environment were performed to explore the humanlike behaviors of the autonomous technology. The findings show that, up to a certain point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study validates the approach and supports past studies by showing that CAVs perform better than AVs in terms of their traffic performance and safety aspects. On top of that, the sensitivity analysis shows that enhancements in the technology are required to obtain the maximum advantages.