Current indicators for measuring traffic safety such as fatalities and severe injuries are lagging indicators, meaning that an accident first needs to happen before it can be measured. With the rise of automation in vehicles, the opportunity may arise to measure leading indicators. These can proactively measure traffic safety by measuring traffic conflicts, interactions that are dangerous but do not necessarily result in an accident. This research uses the knowledge of experts on traffic safety and/or vehicle sensor data to evaluate the suitability of five types of indicators to measure traffic safety based on vehicle sensor data. It also evaluates ten potential barriers for using vehicle sensor data in practice. While limited differences are found between the types of indicators, none of the potential barriers is rated as unsurmountable by the experts. In doing so, this research proofs that it would be possible to measure traffic safety with vehicle sensor data and provides a starting point for further research

Railways are facing the challenge to simultaneously increase the capacity and the operational performance of their network. Automatic train operation (ATO) can be one of the technologies to increase the capacity of the railway network. The specifications for ATO over the European Train Control System (ETCS) automatic train protection system have been defined. However, testing is taking place over legacy automatic train protection systems , such as ATBNG, as well. ATO requires information from the automatic train protection system. The goal of this master thesis is to determine the data gap between ETCS and ATBNG in relation to ATO. First a generic ATO model is developed from literature. This model presents the information required and produced by ATO. The model is used as a framework for the analysis of ATO over ETCS and ATO over ATBNG. This analysis resulted in a conceptual model of both ATO over ETCS and ATO over ATBNG. The conceptual model shows that ATBNG can provide the required information if the ATBNG operational envelope is used as the relevant safety envelope. However, currently the operational envelope of the NS’54 signalling system is the relevant safety envelope. Additional information is required to allow ATO to determine the NS’54 operational envelope. Furthermore, ATBNG is not capable of presenting ATO information to the train driver. A new ATO DMI is required. Moreover, the ProRail traffic management system is not yet capable of providing ATO with the required information for both ATO over ETCS and ATO over ATBNG. To validate the conceptual ATO over ATBNG model three case studies have been worked out. The study performed for this master thesis is an exploratory study, therefore validation of the findings and assumptions is required.

Microsimulation can substantiate traffic safety analysis. The questions is how to use the information from microsimulation to say something about the general safety at network level. The objective of this research is to find out how microsimulation can be used to quantify traffic safety at a network level. For this, the surrogate safety assessment module was used to identify the conflicts that occur on a large scale network consisting of a distributor road with 20 intersections. These conflicts are indicated by the Time to collision (TTC) and the post encroachment time (PET). Five years of historical crash data of this same network was compared to the conflicts from the SSAM both visually and statistically. The statistical comparison used the spearman rank correlation to check the correlation for different threshold values of bot the TTC and the PET. An AM-peak and PM-peak simulation scenario were run and compared to the crashes within the same time interval during the day. The AM-peak conflicts showed a reasonable fit with the AM-peak crashes with a spearman coefficient of 0.58 and a corresponding P-value of 0.008 for TTC and PET values up to 1.0. Which is significant within a 95% confidence interval. The PM-peak conflicts showed a bad correlation to the crashes on the network with a spearman rank coefficient of 0.275 and a corresponding P-value of 0.240. To see if there was a connection with other surrogate safety methods, a safety performance function (SPF) from the federal highway administration was used to predict the crashes on the network using the traffic volume. The use of this SPF did not lead to reliable results. The last stage of this research was the application of a newly proposed framework that consists of a linear model. Within this linear model the number of conflicts for each of the conflict indicators of the SSAM was compared to the number of crashes for each intersection at the network. By solving the linear model with a least square estimate the influence of each individual conflict indicator was estimated for different number of intersections. From these estimations, it could be seen that the TTC has the best predictive value out of the conflict indicators present in the SSAM program.

The Dutch rail sector is one of the most heavily used rail networks in Europe. It plays a critical role in the domestic transportation of passengers. Disruptions in the operation of one organisation can have a cascading effect on others within the sector. Risk management needs to identify and address these weaknesses to prevent largescale disruptions. The risks a rail organisation faces are diverse, ranging from financial risks to strategic risks. The enterprise risk management methodology addresses all the risks of an organisation to reduce the negative effect and seize opportunities. In recent years, there has been increased emphasis on data-driven work, including within the risk management domain. This research explores the implementation of data-driven work in the enterprise risk management of the Dutch passenger transporting rail sector. This research uses a comparative case study to explore the novel research field. The case study data is collected from interviews and desk research. This research concludes that datadriven work adds value to the enterprise risk management of Dutch rail organisations. Data-driven enterprise risk management improves the predictive capabilities of rail organisations. In addition, it enables real-time monitoring of risks. Hence, it supports the decision-making process more precisely and accurately.

The human-machine interfaces (HMIs) in passenger cars have become more complex over the years, with touch screens replacing physical buttons and more options becoming available. This can result in multiple layers of menus that have to be navigated to perform simple tasks and therefore increase distraction. In addition to this, there is no standardisation for passenger car HMIs. To face these problems, new standards will have to be created. However, knowledge regarding the prevalence of different internal HMI tasks is missing. This complicates efforts to create new standards since it is unclear which internal HMI tasks are most important to assess. It also complicates efforts to create an assessment methodology to rate the safety of different internal HMI interfaces, as tasks that are used more often could be seen as more important. This thesis fills the knowledge gap by identifying the most used internal HMI tasks and the factors that influence their frequencies based on real-world driving observations in a naturalistic setting. In particular, this study especially looks at the influence of car familiarity. Multiple data analysis methods, including Poisson regressions, ANOVA’s, paired t-tests and factor analysis are used to obtain the required knowledge. In general, people seem to perform around 12 tasks per hour in familiar cars versus 9 tasks per hour in unfamiliar cars, but significant differences between tasks exist. The results indicate that the most performed is using the indicator light. Other frequently performed tasks fall into the windshield, radio and media and climate control categories. The factors that influenced the task frequencies included car familiarity, gender, age and weather conditions. The type of car also seems to impact the task frequencies, but more research on this is needed.