Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

Introduction: Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

Method: In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions).

Optimal planning of safety improvements on road sites belonging to different categories within large networks: An integrated multi-layer framework.

Planning road safety interventions on large road networks implies several layers of complexity in the decision-making process. In fact, the following simultaneous problems should be addressed: estimating safety performances on the different road elements of the network, identifying sites showing high potential for improvement with respect to reference values, defining the possible types of safety measures to be implemented and their anticipated effect on traffic safety, limiting the number of interventions given fixed budget constraints. This study proposes an integrated multi-layer framework which takes into account the above-defined problems into a single optimization procedure which provides the number and type of safety interventions to be implemented over a wide road network composed of different categories of road elements.

Exploring the relationships between drivers' familiarity and two-lane rural road accidents. A multi-level study.

Previous research has suggested that drivers' route familiarity/unfamiliarity (using different definitions of familiarity), and the interactions between familiar and unfamiliar drivers, may affect both the driving performances and the likelihood of road crashes. The purpose of this study is to provide a contribution in the search for relationships between familiarity and crashes by: 1) introducing a measure of familiarity based on the distance from residence; 2) analyzing a traffic and accident dataset referred to rural two-lane sections of the Norwegian highways E6 and E39; 3) using a multi-level approach, based on different perspectives, from a macro analysis to more detailed levels. In the macro analyses, the accident rates computed for different seasons and for different summer traffic variation rates (used as indicators of the share of familiar drivers in the flow) were performed.