This paper investigates pedestrians' traffic gap acceptance for mid-block street crossing in urban areas. A field survey was carried out at an uncontrolled mid-block location in Athens, Greece. Pedestrians' decisions and traffic conditions were videotaped in terms of the size of traffic gaps rejected or accepted, waiting times and crossing attempts and vehicle speeds. A lognormal regression model was developed to examine pedestrian gap acceptance. It was found that gap acceptance was better explained by the distance from the incoming vehicle, rather than its speed. Other significant effects included illegal parking, presence of other pedestrians and incoming vehicles’ size. A binary logistic regression model was developed to examine the effect of traffic gaps and other parameters on pedestrians' decisions to cross the street or not. The results reveal that this decision is affected by the distance from the incoming vehicles and the waiting times of pedestrians.

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as "hot topics" of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 'Synopses' (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors).

Objectives: This research aims to identify and analyze the factors affecting accident severity through a macroscopic analysis, with a focus on the comparison between inside and outside urban areas. Disaggregate road accident data for Greece for the year 2008 were used. Methods: Two models were developed, one for inside and one for outside urban areas. Because the dependent variable had 2 categories, killed/severely injured (KSI) and slightly injured (SI), the binary logistic regression analysis was selected. Furthermore, this research aims to estimate the probability of fatality/severe injury versus slight injury as well as to calculate the odds ratios (relative probabilities) for various road accident configurations. The Hosmer and Lemeshow statistic and other diagnostic tests were conducted in order to assess the goodness-of-fit of the model. Results: From the application of the models, it appears that inside urban areas 3 types of collisions (sideswipe, rear-end, with fixed object/parked car), as well as involvement of motorcycles, bicycles, buses, 2 age groups (18–30 and older than 60 years old), time of accident, and location of the accident, seem to affect accident severity. Outside urban areas, 4 types of collisions (head-on, rear-end, side, sideswipe), weather conditions, time of accident, one age group (older than 60 years old), and involvement of motorcycles and buses were found to be significant. Conclusions: Factors affecting road accident severity only inside urban areas include young driver age, bicycles, intersections, and collision with fixed objects, whereas factors affecting severity only outside urban areas are weather conditions and head-on and side collisions, demonstrating the particular road users and traffic situations that should be focused on for road safety interventions for the 2 different types of networks (inside and outside urban areas). The methodology and the results of this research may provide a promising tool to prioritize programs and measures to improve road safety in Greece and worldwide.