Life cycle assessment (LCA) is becoming increasingly important for structures in the built environment, but more development is still required. This paper proposes an augmented life quality index (LQI) for social and environmental life cycle assessment (S-LCA and E-LCA) of engineering structures. The original LQI has previously been applied in norms and literature on structural safety, but not yet in social life cycle assessment, nor with inclusion of environmental aspects. Its main application is to determine the societally acceptable failure probability of a structure by weighing costs and benefits of improvement in life safety, making the LQI an ideal candidate for further development of S-LCA and LCA in general.This paper describes how the LQI can be used while adhering to state-of-the-art S-LCA guidelines and norms. A novel augmentation based on E-LCA is then proposed, where shadow costs of embodied emissions are added to the cost of structural safety, combining social, economic and environmental sustainability. This is demonstrated through a case study based on a typical structural design, showing that the LQI can be used in S-LCA to establish the acceptable failure probability of a structure, and that the E-LCA augmented LQI-based criterion allows the acceptable reliability index to be reduced by approximately 0.1 to 0.3. This reduction is dependent on the ratio of the shadow price and material costs.

Perceived safety and comfort influence cycling mode choice and behaviour. While roundabouts are associated with a decreased severity of motor vehicle crashes, recent crash data in the Netherlands suggests that this is not the case for bicycle crashes, with 12% of all bicycle crashes between 2014 and 2021 occurring at roundabouts. Previous studies have mainly focused on intersection type and bicycle facilities, and overlooked how different design elements of dedicated bicycle facilities on roundabouts affect cyclists' perceived safety. Furthermore, previous studies did not investigate the relationship between perceived safety and comfort. To address these gaps, this study aims to better understand the factors contributing to cyclists' perceived safety and comfort at roundabouts. A total of 239 complete responses from cyclists to a stated preference survey were collected. A bivariate random effect ordered probit model was used to simultaneously model cyclist's perceived safety and comfort as a function of behavioural factors and infrastructural design elements. The results revealed that roundabouts where cars must yield to cyclists and with fewer vehicular entrance points were perceived by cyclists as safer and more comfortable. Also, cyclists' place of residence (in or outside the Netherlands), their likelihood to commit traffic violations, their recent crash history, and the type of bicycle they use, significantly affect their perceived safety. To improve cyclists' perceived safety and comfort in urban environments, it is recommended to ensure bicycle yielding priority, design dedicated bicycle facilities on roundabouts and maintain uniformity in bicycle infrastructure design.

Speeding can be conceptualised as a social dilemma shaped by road and traffic conditions, enforcement, and individual psychological differences. Yet most studies have overlooked how such differences condition drivers' responses to speed-management cues. We examined drivers' speeding propensity across urban scenarios in the West Midlands (WM), Athens, and Valencia, focusing on Social Value Orientation (SVO) and its moderating role. Random-parameters ordered probit models with heterogeneity in the means were used to capture unobserved heterogeneity and systematic variation linked to driver traits. Results show that road design, traffic density, and speed enforcement are associated with self-reported speeding, but these associations vary across contexts and drivers. SVO-related moderation was clearest in the WM, where stronger prosocial orientation weakened the links between behavioural-risk constructs and self-reported speeding and increased sensitivity to traffic and road-design conditions. In Athens, the pattern differed, with stronger prosocial orientation mainly strengthening the link between speeding tendencies and speeding propensity. In Valencia, SVO-related evidence was more selective and less pervasive. Age also moderated these relationships: in Athens, speed cameras were more strongly associated with lower stated speeding among older drivers, while in the WM and Athens the links between behavioural-risk constructs and stated speeding generally weakened with age. Overall, engineering and enforcement measures were not associated with stated speeding uniformly across drivers or study contexts. Speed-management strategies may therefore be more effective when physical design and enforcement are paired with communication that presents speed limits as credible, locally relevant, and clearly connected to the safety of other road users.

While mobility and safety of drivers are challenged by behavioral changes, the increasingly complex road environment has placed a higher demand on their adaptability. The ultimate goal of this paper was to identify the impact that the balance between task complexity and coping capacity had on crash risk. Towards that aim, an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk was developed. A vast library of data from a naturalistic driving experiment was created in three countries (i.e., Belgium, UK and Germany) to investigate the most prominent driving behavior indicators available, including speeding, headway, overtaking, duration, distance and harsh events. In order to fulfil the aforementioned objectives, exploratory analysis, such as Generalized Linear Models (GLMs) were developed, and the most appropriate variables associated to the latent variable “task complexity” and “coping capacity” were estimated from the various indicators. Additionally, Structural Equation Models (SEMs) were used to explore how the model variables were inter-related, allowing for both direct and indirect relationships to be modelled. The analyses revealed that higher task complexity levels lead to higher coping capacity by drivers. Additionally, the effect of task complexity on risk was greater than the impact of coping capacity in Belgium and Germany, while mixed results were observed in the UK.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

In recent years, the relationship between academia and the fossil fuel industry has become a focal point of intense debate. This concern arises from the fear that corporate funding might skew research activities. A significant development in this area is the adoption of policies by a Dutch university, and discussions in several others, prohibiting research funded by the fossil fuel industry. These policies aim to safeguard academic freedom and integrity. Despite this, there has been little discussion on the myriad challenges, implications, and possible unintended consequences, particularly in the realm of safety-and-security research. As such, this manuscript delves into the complex transition towards a fossil-fuel-free society, examining it through the lenses of safety science and sociotechnical systems. It emphasizes the vital importance of collective responsibility in ensuring systemic safety and security as we navigate towards achieving the sustainable development goals. This journey requires a delicate balance between the objectives of safety and sustainability, along with a deep understanding of the security implications of decreasing our dependence on the fossil fuel industry. The strategy of distancing academic research from fossil fuel industries, commonly seen as a positive step, also demands a nuanced consideration of its broader impacts, including the setting of precedents for addressing other existential and systemic risks. Instead, we argue for the establishment of robust governance structures rooted in restorative justice principles. Such frameworks can facilitate productive dialogue with underrepresented groups, motivate the fossil fuel industry towards sustainable practices, and safeguard the integrity of scholarly research. This approach not only addresses immediate concerns related to fossil fuels but also lays the groundwork for a more inclusive and equitable model of climate risk research, essential for tackling the multifaceted challenges of our era.

Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

This paper analyses strategies for platform owners to increase complementor participation on the platform. Specifically, it draws on open innovation (OI) and the resource-based view (RBV) to isolate three drivers of complementor participation, namely breadth of content offerings and boundary resources (related to OI), and exclusive content (associated with RBV). We hypothesize that higher levels of each of these drivers increase the platform's attractiveness to future complementors and increase complementor participation. Based on negative binomial fixed effects regressions in the context of video game consoles, we find that boundary resources and exclusive content, but not breadth of content offerings, are positively related to complementor participation. This shows that drivers from both OI and RBV relate to complementor participation. The results have implications for the orchestration of platform ecosystems.

The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

Shared spaces for active mobility aim to offer safe and comfortable mobility for vulnerable road users by separating them from motorised vehicles. However, the distinct navigation characteristics of these users may increase the complexity of their interactions. The emergence of e-bikes which are faster and heavier than regular bikes has further increased this complexity. This study aims to shed light on the interdependency of e-bikes and pedestrians behaviours in shared spaces, and investigate how they influence each other's navigation. Through a controlled experiment in Lund, Sweden, data were collected on a total of 1520 trajectories of e-bike and pedestrians, their demographics and cycling experience. A simultaneous equation model was used to quantify the interactions between the participants. Results demonstrate significant correlations among variables, highlighting the model's capacity to effectively capturing the hypothesized interdependencies. The findings can inform the development of level-of-service indices and surrogate safety measures for shared spaces.