The propensity score (PS) based method has been increasingly used in road safety evaluation studies. However, several major considerations regarding its implementation arise when using the PS method. First, as is well known, the PS method is 'data hungry' in terms of the number of treated and control units, however, it is sometimes difficult and time-consuming to construct a large sample in road safety studies. It would be helpful to better understand how to choose a proper sample size, as well as the ratio of the number of treated units to the control ones. Second, the criteria used for covariates selection of the PS model were not fully consistent across the existing road safety evaluation studies. Due to the complicated mechanisms behind the implementation of road safety measures and policies, including all relevant covariates that affect both the selection into treatment (i.e., implementation of road safety measures) and the outcomes (i.e., road accidents) is impossible. In this paper, we conduct a simulation study to investigate such issues and provide some practical suggestions for using PS methods in road safety evaluations. The estimator considered in this study is the inverse probability weighting estimator based on the PS. Our results suggest that the bias and variance of the estimated treatment effect will remain stable when the sample size reaches a certain level. A proper sample size is the one that ensures relevant covariates achieve acceptable balance. Regarding the issue of covariates selection, including the covariates that significantly affect the road accidents is recommended, regardless of whether they affect the implementation of road safety measures. This study also proposes practical procedures for using the weighting approach to evaluate the effects of road safety treatments.Speed-pedelecs -fast electric bicycles offering pedal support up to a speed of 45 km/h- are a recent, environmentally friendly, and mobility efficient innovation. However, their high travel speed may increase crash and injury risk. Due to their recent introduction accurate crash data are not available yet. Since near-crashes may serve as a proxy for crashes this study analyzed traffic conflicts (i.e., near-crashes and minor crashes) in the Netherlands with the aim to proactively identify potential crash partners, crash patterns, and crash risk increasing factors. To this end, twenty-eight participants used a speed-pedelec in daily traffic, equipped with a forward and a backward facing camera, for two to three consecutive weeks. In a total of 227 h of video footage in which a distance of 6584 km was travelled, 115 conflicts were identified of which 114 were near-crashes in which evasive actions were performed to avert a crash, and one was a minor crash. The most frequent conflict partner were bicycles (51 %), (4) speed-pedelecs rode near or at an intersection, OR = 3.94, 95 % CI = [2.42-6.43]. These findings suggest that conflict risks are higher when speed-pedelec riders make use of bicycle facilities than when they ride on the roadway for cars. https://www.selleckchem.com/products/jh-x-119-01.html However, the consequences of crashes with motorized vehicles on the roadway will probably be more severe for speed-pedelec riders than with bicycles on the cycle path. This study further illustrates the value of naturalistic conflict observations for assessing the safety implications of innovations proactively.This paper presents an analysis of fatal train accident rates and trends on Europe's main line railways from 1990-2019. It is a sequel to the paper Fatal train accidents on Europe's railways 1980-2009 (Evans (2011), which covers the three decades 1980-2009. The present paper discards the data for the 1980s, but adds the data for 2010-2019. The data cover the 28 countries of the European Union as in 2019, together with Norway and Switzerland. The source of the recent data is largely the European Union Agency for Railways. The estimated overall trend in the number of fatal train collisions and derailments per train-kilometre was -5.6 % per year from 1990-2019, with a 95 % confidence interval of -7.1 % to -4.2 %. The estimated accident rate in 2019 was 0.85 fatal collisions or derailments per billion train-kilometres, which represents a fall of 78 % since 1990. This gives an estimated mean number of fatal accidents in Europe in 2019 of 3.89. The data and results for 2010-2019 closely match the extrapolation of the results for 1990-2009, so that in 2009 extrapolation would have given a good forward projection for 2019. By the same argument this paper gives a forward projection of the mean number of accidents in 2029 of 2.12, assuming no change in train-kilometres, or pro-rata changes with changes in train-kilometres. The paper investigates the causes of accidents. A notable finding is that the proportion of accidents caused by signals passed at danger (SPADs) fell from 40 % in 1990-1999 to 21 % in 2010-2019. This is probably due to the increasing deployment of train protection systems. The number of fatalities in individual accidents has a skew distribution most accidents have a small number of fatalities, but a few have a large number. The overall observed number of fatalities per accident is 4.23, and there is no indication that this mean changes with time. This implies that the mean number of fatalities per year has the same downward trend of 5.6 % per year as the mean number of accidents per year.Understanding how marine fish early-life history is affected in the long-term by environmental and oceanographic factors is fundamental given its importance to population dynamics and connectivity. This work aimed at determining the influence of these processes on the interannual variability in hatch day and early-life growth patterns of European seabass, over a seven-year period (2011-2017) in the Atlantic Iberian coast. To accomplish this, otolith microstructure analysis was used to determine seabass hatch day and to develop early-growth correlations. In most years, hatching occurred from February to April, with two exceptions in 2012, hatching started in early-January, and in 2016 an exceptionally long hatching period was registered. Using generalized additive models (GAM), we observed that sea surface temperature (SST), the North Atlantic Oscillation index (NAOi) and Chlorophyll-a (Chla) were the main drivers behind the inter-annual variability in seabass hatch day. Analysis of correlations between growth increments allowed assessing important periods of seabass growth and how future growth is affected.