Observed accidents have been the main resource for road safety analysis over the past decades. Although such reliance seems quite straightforward, the rare nature of these events has made safety difficult to assess, especially for new and innovative traffic treatments. Surrogate measures of safety have allowed to step away from traditional safety performance functions and analyze safety performance without relying on accident records. In recent years, the use of extreme value theory (EV) models in combination with surrogate safety measures to estimate accident probabilities has gained popularity within the safety community. In this paper we extend existing efforts on EV for accident probability estimation for two dependent surrogate measures. Using detailed trajectory data from a driving simulator, we model the joint probability of head-on and rear-end collisions in passing maneuvers. We apply the Block Maxima method and estimate several extremal univariate and bivariate models, including the logistic copula. In our estimation we account for driver specific characteristics and road infrastructure variables. We show that accounting for these factors improve the head-on and rear-end collision probabilities estimation. This work highlights the importance of considering driver and road heterogeneity in evaluating related safety events, of relevance to interventions both for in-vehicle and infrastructure-based solutions. Such features are essential to keep up with the expectations from surrogate safety measures for the integrated analysis of accident phenomena, which show to significantly improve from the best known stationary extreme value models.

Current public lighting is predominately directed to reducing energy and often is understood as a technical issue rather than a human one, mostly based on photometric visual performance. By taking advantage of the inherent flexibility of current lighting technologies, this research aimed to contribute to a more sustainable lighting design practice, through the design of adaptive lighting solutions that improve the relationship between users, the space they inhabit and energy use. To confirm the hypothesis, it was developed a user-oriented method that considers a specific user-space relationship and the user’s perception of well-being. Tested via two outdoor field experiment in an urban space in the south of Portugal, qualitative and quantitative statistical analysis of the collected data, suggest that the method can provide data to aid the design of more tailored and flexible public lighting solutions that can balance the user-energy relationship, improving the overall sustainability of our cities.