In search of the severity dimension of traffic events: Extended Delta-V as a traffic conflict indicator.

Most existing traffic conflict indicators do not sufficiently take into account the severity of the injuries resulting from a collision had it occurred. Thus far, most of the indicators that have been developed express the severity of a traffic encounter as their proximity to a collision in terms of time or space. This paper presents the theoretical framework and the first implementation of Extended Delta-V as a measure of traffic conflict severity in site-based observations. It is derived from the concept of Delta-V as it is applied in crash reconstructions, which refers to the change of velocity experienced by a road user during a crash. The concept of Delta-V is recognised as an important predictor of crash outcome severity. The paper explains how the measure is operationalised within the context of traffic conflict observations. The Extended Delta-V traffic conflict measure integrates the proximity to a crash as well as the outcome severity in the event a crash would have taken place, which are both important dimensions in defining the severity of a traffic event. The results from a case study are presented in which a number of traffic conflict indicators are calculated for interactions between left turning vehicles and vehicles driving straight through a signalised intersection. The results suggest that the Extended Delta-V indicator seems to perform well at selecting the most severe traffic events. The paper discusses how the indicator overcomes a number of limitations of traditional measures of conflict severity. While this is a promising first step towards operationalising an improved measure of traffic conflict severity, additional research is needed to further develop and validate the indicator.

Drivers' behavioral responses to combined speed and red light cameras.

BACKGROUND: Numerous signalized intersections worldwide have been equipped with enforcement cameras in order to tackle red light running and often also to enforce speed limits. However, various impact evaluation studies of red light cameras (RLCs) showed an increase of rear-end collisions (up to 44%). OBJECTIVE: The principal objective of this study is to provide a better insight in possible explaining factors for the increase in rear-end collisions that is caused by placing combined speed and red light cameras (SRLCs). METHOD: Real-world observations and driving simulator-based observations are combined. Video recordings at two signalized intersections where SRLCs were about to be installed are used to analyze rear-end conflicts, interactions and driver behavior in two conditions (i.e., with and without SRLC). Furthermore, one of these intersections was rebuilt in a driving simulator equipped with an eye tracking system. At this location, two test conditions (i.e., SRLC and SRLC with a warning sign) and one control condition (i.e., no SRLC) are examined. The data of 63 participants were used to estimate the risk of rear-end collisions by means of a Monte Carlo Simulation. RESULTS: The results of the on-site observation study reveal decreases in the number of red and yellow light violations, a shift (i.e., closer to the stop line) in the dilemma zone and a time headway reduction after the installation of the SRLC. Based on the driving simulator data, the odds of rear-end collisions (compared to the control condition) for the conditions with SRLC and SRLC+warning sign is 6.42 and 4.01, respectively. CONCLUSION: The real-world and driving simulator observations indicate that the risk of rear-end collisions increases when SRLCs are installed. However, this risk might decrease when a warning sign is placed upstream.

"Crashing the gates" - selection criteria for television news reporting of traffic crashes.

This study investigates which crash characteristics influence the probability that the crash is reported in the television news. To this purpose, all news items from the period 2006-2012 about traffic crashes from the prime time news of two Belgian television channels are linked to the official injury crash database. Logistic regression models are built for the database of all injury crashes and for the subset of fatal crashes to identify crash characteristics that correlate with a lower or higher probability of being reported in the news. A number of significant biases in terms of crash severity, time, place, types of involved road users and victims' personal characteristics are found in the media reporting of crashes. More severe crashes are reported in the media more easily than less severe crashes. Significant fluctuations in media reporting probability through time are found in terms of the year and month in which the crash took place. Crashes during week days are generally less reported in the news. The geographical area (province) in which the crash takes place also has a significant impact on the probability of being reported in the news. Crashes on motorways are significantly more represented in the news. Regarding the age of the involved victims, a clear trend of higher media reporting rates of crashes involving young victims or young fatalities is observed. Crashes involving female fatalities are also more frequently reported in the news. Furthermore, crashes involving a bus have a significantly higher probability of being reported in the news, while crashes involving a motorcycle have a significantly lower probability. Some models also indicate a lower reporting rate of crashes involving a moped, and a higher reporting rate of crashes involving heavy goods vehicles. These biases in media reporting can create skewed perceptions in the general public about the prevalence of traffic crashes and eventually may influence people's behaviour.