Objectives: Roundabouts are a type of circular intersection control generally associated with a favorable influence on traffic safety. International studies of intersections converted to roundabouts indicate a strong reduction in injury crashes, particularly for crashes with fatal or serious injuries. Nevertheless, some crashes still occur at roundabouts. The present study aims to improve the understanding of roundabout safety by identifying crash types, locations, and factors that are associated with roundabout crashes. Methods: An analysis of 399 crashes on 28 roundabouts in Flanders, Belgium, was carried out based on detailed crash descriptions; that is, crash data and collision diagrams. The crashes are sampled from police-reported crashes at roundabouts in the region of Flanders, Belgium. Collision diagrams of the registered crashes were used to distinguish 8 different crash types. The roundabout itself is divided into 11 detailed and different typical segments, according to previously established knowledge on the occurrence of crashes at roundabouts. The 8 roundabout crash types are examined by injury severity, crash location within the roundabout, type of roundabout, type of cycle facility, and type of involved road user. Results: Four dominant crash types are identified: rear-end crashes, collisions with vulnerable road users, entering–circulating crashes, and single-vehicle collisions with the central island. Crashes with vulnerable road users and collisions with the central island are characterized by significantly higher proportions of injury crashes. About 80% of the crashes occurred on the entry lanes and the circulatory road (segments 1–4). Road users who are the most at risk to be involved in serious injury crashes at roundabouts are cyclists and moped riders. Conclusions: The main goal of this study was to identify and analyze dominant crash types at roundabouts by taking into account detailed information on the crash location. Some connections between certain roundabout crash types, their crash location, and roundabout design characteristics have been found.

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.

Traffic signals are often implemented to provide for efficient movement and to improve traffic safety. Nevertheless, severe crashes still occur at signalized intersections. This study aims to improve understanding of signalized intersection safety by identifying crash types, locations, and factors associated with signalized intersections. For this purpose, 1,295 police-reported crashes at 87 signalized intersections were analyzed on the basis of detailed crash descriptions, that is, crash data and collision diagrams. The information from the collision diagrams was used to distinguish six crash types and to create a crash location typology to divide the signalized intersection into 13 detailed typical segments. Logistic regression modeling techniques were used to identify relations between crash types, their crash location on certain signalized intersection segments, the crash severity, and the different features that affected crash occurrence. Four dominant crash types were identified: rear-end, side (i.e., left-turn plus right-angle), head-on, and vulnerable road user crashes. The results of the logistic regression models showed that the location of these crash types was related to specific signalized intersection segments. The results also revealed important signalized intersection features that affected the crash occurrence. As a result, connections between certain signalized intersection crash types, their crash location, and signalized intersection design characteristics were found. The combination of intersection features with detailed signalized intersection segments provided valuable insights into the nature of signalized intersection crashes and the safety impact of signalized intersection design.

Road traffic accidents are the primary cause of injuries and fatalities among children. The current study focuses on children’s (un)safe crossing behaviour in a real traffic situation accompanied by an adult at a crosswalk in front of their school. The study aims to investigate if there are differences in crossing behaviour related to road infrastructure (i.e., one-way and two-way street, elevated and non-elevated street crossing), the gender of the child, and the effect of the accompanying adult’s behaviour on the child’s crossing behaviour. Primary school children from two urban schools in Flanders (Belgium) were observed for three days while crossing the street in front of their school in the morning and afternoon. A total of 241 child–adult pairs were observed. Descriptive analysis, Pearson chi-square tests, and binary logistic regression models were used to find differences between groups. More than half of the crossings exhibited two or more unsafe behaviours. Not stopping at the curb before crossing was the most unsafe behaviour, exhibited by 47.7% of children; not looking for oncoming traffic before and during the crossing was the second most unsafe behaviour, exhibited by 39.4% of the children. The only difference between boys’ and girls’ crossing behaviour was in stopping at the curb with girls 1.901 times more likely to stop before crossing as compared to boys. Adults holding hands of the child resulted in safer behaviours by children. The children not holding hands displayed significantly riskier behaviour in running or hopping while crossing the street and being distracted. The study reinforces the need to improve the transportation system through infrastructural interventions (elevated crosswalks), as well as educating and training children and the parents on safe crossing behaviour in traffic.

Road crashes are the sixth leading cause of death in India. There has been a fourfold increase in the number of road traffic crashes in India in the last four decades and an increase of 9.8 times in the fatalities associated with that exponential increase. Manipal is a coastal place with a population of approximately 50,000 inhabitants lying in between the western Mountain range and the Arabian sea. The study’s objective is to explore the characteristics of fatal road crashes in Manipal from 2008–2018 using the data pertaining to fatal crashes retrieved from the office of the superintendent of police. Furthermore, it aims to forecast crashes by time series analysis prediction. The results show that most of the fatal crashes are due to exceeding the lawful speed limit, followed by driving under the influence of alcohol and going ahead and overtaking. The time series analysis forecasted the number of fatal crashes until the year 2025 and predicted that there will be an increase in the number of fatal road crashes by 4.5%. The results also provide essential leads for initiating specific intervention programmes targeting the causes of fatal road crashes.