Objective: Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score. Method: In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006–2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS. Results: This study showed the impact of variables, such as the presence of blood alcohol, the use of protection devices, the type of crash, and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analyzed as risk factors, showing that elderly and male road users are highly associated with MAIS 3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the magnitude of impact of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification. Conclusions: The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.

Today, and probably for a long time to come, humans will remain an integral part of vehicles for driving tasks. Therefore, it is essential to understand how vehicles and drivers interact with each other and how drivers’ behavior and physical and mental states affect vehicle performance and traffic safety. This article explores the relationship between driver and vehicle in real-world driving conditions by analyzing large-scale naturalistic data collected from cars and drivers. Specifically, more than 800 hours of driving data from 16 drivers were collected using three different data sources (telematics data, video frames, and physiological data) and two types of analysis were done. The first one analyzes different types of driver-vehicle interactions during driving. The second one investigates the effect of different driving conditions on drivers’ stress and explores the relationship between driver and vehicle in different driving conditions. Our experimental results show that drivers’ physiological signals are correlated with some variables from vehicle kinematics and influenced by drivers’ behavior inside the vehicle. These findings could be used to help manage comfort-related in-vehicle intervention systems and could provide a continuous measure of how different external conditions (traffic, road, weather, etc.) affect drivers.

The COVID-19 pandemic has changed the way how the people live, work and move, and naturally the transport sector became one of the most affected by this global crisis. Beyond the sudden fall of mobility at the beginning of the pandemic, it is important to understand how people are regaining trust in travelling, even if it is still unpredictable if and when the transport sector will recover to the pre-pandemic levels. This study focuses on the analysis of commuting trips and the changes of travel mode preferences over the first eight months of the pandemic in Germany. A survey with an orthogonal design based on sets of cards containing different transport mode alternatives and attributes was conducted in three waves (April, June, and October 2020). The individual characteristics and the preferences of around 4800 commuters were collected through the survey and modelled using a conditional logit approach. The results show that commuters have regained some trust on public transport since the April-May 2020 lockdown, but this has occurred at a slow pace. The reduction of public transport ticket fares can be the most effective strategy to recover some of the users lost to other modes.