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.

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.

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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.

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