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.

Surrogate measures of safety (SMoS) are meant to be an alternative/complement to crash data that enables a proactive approach to traffic safety. Validity is a fundamental property of SMoS that describes how well they reflect the quality of interest - traffic safety. However, typical validation studies are extremely resource demanding. Therefore, the aim of this study was to explore a relative approach to validity that uses fewer resources and does not rely on crash records from the observed locations. The core idea of a relative approach to the validation of SMoS is that while SMoS might not accurately estimate the expected number of crashes, they still work well for comparisons between different sites or between conditions at the same site. Based on this concept, we propose a method in which a ground truth based on literature can be used as a baseline that can then be compared to the safety analysis of an SMoS study. Using the proposed method, a case study of six intersections in northern Europe was conducted, focusing on bicycle infrastructure. The results indicate that the minimum time-to-collision indicator is in line with the ground truth, whereas the post-encroachment time indicator is not.

Surrogate measures of safety (SMoS) can enable quick, pro-active, and detailed safety evaluations by studying near-crashes. A critical concern regarding SMoS is their validity. This study focused on the validity of two commonly used SMoS indicators-minimum time to collision and post-encroachment time-with a specific focus on bicyclist-motor vehicle interactions. The study was divided into two main parts; the first part focused on observations at intersections in Scandinavia, and the second part focused on developing a crash model using data from 166 similar intersections. Nine signalised intersections in Scandinavia were observed for at least 24 h. During this time, each interaction between a bicyclist and a right- or left-turning motor vehicle was identified and trajectories for the road users were created. The corresponding SMoS values were then calculated. Three main results were found when comparing the results of the crash model with the SMoS analysis. First, there is a significant relationship between the expected number of crashes and both indicators. However, the results also suggest that this correlation might originate from the inherent connection between the indicators and the number of interactions between the studied road users. Finally, when the number of interactions is considered, the results show that the minimum time to collision with a threshold of 3-4 s produces the best results.

The current paper presents the results of behavioural observations in a field experiment with automated shuttles in Oslo, Norway. Video observations were conducted at five fixed locations along a challenging 1.2 km automated shuttle line with varying traffic conditions. Observed interactions between vulnerable road users and automated shuttles were coded using a predefined codebook, which allowed a structured quantitative analysis. The paper identified several potentially risky types of situations in which the automated shuttles did not always behave according to the traffic rules. Generally, the automated shuttles failed to give way to pedestrians at pedestrian crossings in 26%–50% of the interactions. Right-turning shuttles failed to yield to cyclists going straight in 38% of the interactions at observation Site 1 (the only location where the automated shuttle takes a right turn). In majority of same direction interactions between cyclists and automated shuttles, the interactions resulted in the cyclist overtaking the automated shuttle, usually on the left-hand side. Generally, the paper found little evidence of road users trying to bully or otherwise take advantage of the defensive driving style of the automated shuttles and identified only a limited number of interactions in which a vulnerable road user behaved ignorant or aggressive towards the automated shuttles. In addition, the paper found very little indication of temporal effects that suggest changes in the interaction patterns over time.

Space Syntax theory is a methodological framework for describing and analysing the relationships between spaces and buildings. The fundamental building blocks of Space Syntax are called axial lines, which represent lines of sight from a specific point in space. These lines can then be used to quantitatively analyse the integration (or attractiveness) of an area. However, many public squares and transport hubs are crossed by roads which prove a hinderance when walking although not impairing the sightlines of pedestrians. The aim of this paper is to test how this limitation affects the concept and whether Space Syntax is still useful to understand how people move in such areas. Seven public squares and transport hubs in Malmö (Sweden) were selected and their Space Syntax integration was calculated. These values were compared with pedestrian detections from the same areas based on video recordings from a drone. When aggregating the areas into several levels based on their integration value, the result shows a clear link between Space Syntax integration and the number of pedestrian detections made at each location, demonstrating how this measure could be useful for understanding micromobility usage on a small scale.

Extreme Value Theory (EVT) is the state-of-the-art method for proactive prediction of accident frequency from traffic interactions on a microscopic scale. The main advantage of using EVT is to predict unobserved critical events based on one or more Surrogate Measures of Safety (SMoS) (single- or multivariate EVT) through a mathematical extrapolation of extreme interactions. Such interactions are quantitatively described by SMoS, which commonly measure the proximity of two road users, increasing the probability of a collision as the proximity decreases. Those events with a higher likelihood of turning into an accident are defined as severe interactions, and they are considered extremes and are used in the EVT model. Since EVT analysis focuses on the upper tail of the distribution, decreasing transformations are a prerequisite, without which it is impossible to model the extremes. However, prediction results depend on the shape of the indicators' distributions. Some studies use simple transformations, such as negation, while others employ nonlinear methods that adjust the relationship between proximity and severity. In the present study, the theory of tail analysis has been used to rigorously formulate the effect of a set of conventional linear and nonlinear transformations of SMoS. The approach was tested on a Swedish dataset, and the effects of the transformations on the prediction of extreme events were evaluated based on an accident model built on local data and Empirical Byes correction. The novelty of this study is that one of the most fundamental concepts in traffic conflict theory, such as conflict-crash relationships, has been examined with mathematical interpretation. The results of this study can be further extended to become a standard procedure in modelling traffic conflicts using EVT.