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• We identify co-occurrence patterns of attributes related to crashes and interventions • Latent Dirichlet allocation was applied to analyse the topics of RSI reports • Results reveal associations among conditions that have previously gone unreported • Results aid to determine if road authorities can implement compliance with standards Under the Safe System framework, Road Authorities have a responsibility to deliver inherently safe roads and streets. Addressing this problem depends on knowledge of the road network safety conditions and the number of funds available for new road safety interventions. It also requires the prioritisation of the various interventions that may generate benefits, increasing safety, while ensuring that reasonable steps are taken to remedy the deficiencies detected within a reasonable timeframe. In this context, Road Safety Inspections (RSI) are a proactive tool for identifying safety issues, consisting of a regular, systematic, on-site inspection of existing roads, covering the whole road network, carried out by trained safety expert teams. This paper aims to describe how topic modelling can be effectively used to identify co-occurrence patterns of attributes related to the run-off-road crashes, as well as the corresponding patterns of road safety interventions, as described in the RSI reports. We apply latent Dirichlet allocation (LDA), a widespread method for fitting a topic model, to analyse the topics mentioned in RSI reports, divided into two groups: problems found; and proposed solutions. For this study, 54 RSI gathered over six years (2012–2017) were analysed, covering 4011 km of Irish roads. The results indicate that important keywords relating to the "forgiving roadside" and "clear zone" concepts, as well as the relevant European technical standards (CEN-EN1317 and EN 12,767), are absent from the extracted latent topics. We also found that the frequency of topics related to roadside safety is higher in the problems record set than in the solutions record set, meaning that problems are more easily identified and related to the roadside area than interventions may be. This paper presents methodological empirical evidence that the LDA is appropriate for identifying the co-occurrence patterns of attributes related to the ROR crashes in road safety inspections' reports, as well as the interventions' patterns associated with these crashes. Also, it provides valuable information aimed to determine the extent to which national road authorities in Europe and their contractors are currently capable of implementing and maintaining compliance with roadside standards and guidelines throughout the life cycle of roads. [ABSTRACT FROM AUTHOR]

• AV disengagement on public roads dominated by causes due to a planning issue. • AV disengagement induced by lacking certain numbers of radar and LiDAR sensors. • Drivers' take-over time impacted by disengagement cause and roadway characteristics. • To improve AV disengagements five or more radar sensors are needed. • Optimal number of LiDAR sensors to be installed is 3 or 4. Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016–2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads. [ABSTRACT FROM AUTHOR]

• Fatal train accidents per train-kilometre in Europe fell at the rate of 6% per year from 1990 until 2019. • There were 0.85 fatal accidents per billion train-kilometres in 2019. • There was a mean of 4.2 fatalities per fatal accident in 1990–2019. • the proportion of accidents caused by signals passed at danger fell from 40 % in the 1990s to 21 % in the 2010s. This may be due to the increasing deployment of train protection systems. 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. [ABSTRACT FROM AUTHOR]