Tim De Ceunynck

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.

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.

Road Safety Differences Between Priority-Controlled Intersections and Right-Hand Priority Intersections Behavioral Analysis of Vehicle-Vehicle Interactions

This study analyzes interactions between two vehicles at right-hand priority intersections and priority-controlled intersections and will help to gain a better insight into safety differences between both types of intersections. Data about yielding, looking behavior, drivers’ age and gender, approaching behavior, type of maneuver, order of arrival, and communication between road users are collected by on-site observations. Logistic regression models are built to identify variables that affect the probability that a violation against the priority rules will occur and the probability that a driver will look to the side when entering the intersection. The number of right-of-way violations is significantly higher at the observed right-hand priority intersection (27% of all interactions) than at the priority-controlled intersection (8%). Furthermore, at the right-hand priority intersection, the behavior of drivers on the lower-volume road is more cautious than the behavior of drivers on the higher-volume road, and violations are more likely when the driver from the lower-volume road has priority. This situation indicates that the higher-volume road is considered as an implicit main road. At both intersection types, there is a higher probability of a right-of-way violation when the no-priority vehicle arrives first: this condition indicates that yielding is partly a matter of first come, first served. For both intersections, the way a driver approaches the intersection (i.e., stopping, decelerating, or holding the same speed) is highly relevant for the occurrence of a right-of-way violation and the probability that the driver will look to the sides on his or her approach to the intersection.

Characteristics and Profiles of Moped Crashes in Urban Areas: An In-Depth Study

In this in-depth study, 167 severe injury crashes involving a moped in urban areas in Belgium in 2013 are analyzed. The study provides an overview of the general crash characteristics of and contributing factors to these moped crashes, and identifies typical crash profiles and their characteristics. The representation of class A mopeds (maximum speed 25 km/h [16 mph]; no driving license needed) and class B mopeds (maximum speed 45 km/h [28 mph]; driving license required) in these crashes is approximately fifty-fifty, which seems to imply an over-representation of mopeds class A. The involved moped riders have an average age of 33 and three-quarters are men. Human factors are by far the largest category of contributing factors in moped crashes, factors related to infrastructure and environment play a moderate role, and vehicle-related factors play only a minor role. The most important human factors are psychological factors and risk assessment errors, the main non-human contributing factors are sight obstructions owing to infrastructure or other vehicles. The following crash profiles, grouping similar types of crashes, were identified: crashes between a vehicle turning off and a moped going straight through (18%); crashes caused by the moped rider’s risky behavior (17%); crashes between a moped and another vulnerable road user (13%); crashes resulting from entering or exiting destinations alongside the road (12%); crashes at intersections (other than vehicles turning off) (11%); single-vehicle crashes with loss of control (9%); crashes caused by an error during overtaking (8%); rear-end crashes (3%); and other (9%).

In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators

ABSTRACT Surrogate indicators are meant to be alternatives or complements of safety analyses based on accident records. These indicators are used to study critical traffic events that occur more frequently, making such incidents easier to analyse. This article provides an overview of existing surrogate indicators and specifically focuses on their merit for the analyses of vulnerable road users and the extent to which they have been validated by previous research. Each indicator is evaluated based on its ability to consider the collision risk, which can be further divided into the initial conditions of an event, the magnitude of any evasive action and the injury risk in any traffic event. The results show that various indicators and their combinations can reflect different aspects of any traffic event. However, no existing indicator seems to capture all aspects. Various studies have also focused on the validity of different indicators. However, due to the use of diverse approaches to validation, the large difference in how many locations were investigated and variations in the duration of observation at each location, it is difficult to compare and discuss the validity of the different surrogate safety indicators. Since no current indicator can properly reflect all the important aspects underlined in this article, the authors suggest that the choice of a suitable indicator in future surrogate safety studies should be made with considerations of the context-dependent suitability of the respective indicator.