The introduction of Highly Automated Vehicles (HAVs) presents challenges for everyday transport, particularly impacting cyclists, who are part of what is called vulnerable road users. While HAVs promise to optimise traffic flow and enhance safety, concerns about their safety remain. This research addresses the gap in understanding cyclists' expectations and perceptions of social driving behaviour in The Netherlands. By developing a conceptual framework and a questionnaire, the study explores the interactions between cyclists and HAVs, focusing on trust, perceived safety and perceived social behaviour of cyclists in urban settings like The Hague. Findings reveal that cyclists' comfortability with sharing the roads with HAVs and their belief that HAVs are more likely to follow the rules influence their opinions. Additionally, the use of electronic Human-Machine Interface (eHMI) in HAVs, seems to positively affect cyclists’ trust, perceived safety and perceived social behaviour. Notably, it is found that demographic factors have little influence on cyclists' attitudes towards HAVs. The study underscores the importance of predictability in fostering higher safety ratings and demonstrates how the characteristics and driving behaviour of HAVs affect cyclist perceptions. These insights can guide policymakers and developers in devising solutions that enhance the safety and well-being of all road users, ensuring the effective integration of HAV technology while minimising potential drawbacks.

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.

Interest in driving simulators has risen due to new technologies that better resemble real-world driving and the cost reduction of driving simulator development. In recent years, safety researchers commonly use driving simulators to examine complex driving behaviors in a controlled and safe environment. However, the question remains whether or not a driving simulator could be used to assess novice drivers’ driving skills performance. These drivers are overrepresented in crashes after passing their driving license test. To the best of the researcher’s knowledge, there is little known about to what extent driving simulators could add value to the traditional on-road car-driving test to assess human driving performance. A framework was used to structure the information available in the literature and practice. A staged research approach consisting of literature research, a questionnaire, and in-depth interviews resulted in an information inventory. The Dutch car-driving test is used as a case study. The results showed a remaining knowledge gap in the literature research about the usefulness of the driving simulator for assessing the set of required driving skills for each part of the Dutch car-driving test. Useful driving simulator training results were found for overtaking & side-way movements. The information from practice showed promising results on the driving simulator’s hazard perception training for human car driving performance. This thesis concludes that driving simulators could lead to a more comprehensive driving test regarding hazard perception assessment. The potential use of a transportable simulator without moving base would be worth further investigating. A fixed-base driving simulator with a blind spot seems promising for overtaking & side-way movements driving movements. Both findings require follow-up research since this thesis provided the first step of research into the opportunities regarding driving simulator usage for human car driving performance assessment.

Mobility of disabled people has as a basic guarantee the traffic safety. Walking for disabled may have improved considerably compared to the past, but there are stillissues such as the safe crossing at signalized crosswalks. Also, the availability of studies on safe crossing for disabled pedestrians is limited. While it has been understood how pedestrians perceive safety at crosswalks, no similar study has been done for disabled pedestrians. The main purpose of this study is to fill the gap and make it more understandable how disabled pedestrians perceive traffic safety at signalized crosswalks. The literature showed significant factors that influence the pedestrian crossing behavior, such as the gender, the kind of disability, the presence of traffic lights at crosswalks, and weather conditions. Most ofthese factors are significantly related to the perception of safety.
The main methodology included the interviews, and the survey, using the statistical analysis with the hypothesis tests and the Generalized Linear Model for the process of the data. The results proved that factors such as gender, age, kind of disability, the walking frequency had a significant impact on the perceived safety of disabled pedestrians. Yet, the study brought up important solutions that can improve safety perception, such as technological solutions like artificial intelligence traffic lights and infrastructure solutions such as the construction of underground crosswalks and refuges islands at long distance crosswalks. A recommendation is that the authorities and transport planners should emphasize to the solutions proposed to improve safety. A further scientific recommendation would the study of the objective safety in the future, comparing it with the perceived safety. This study has contributed significantly to the understanding of the issues faced by disabled pedestrians at crosswalks and it can be a starting point for further improvements in the mobility of disabled pedestrians and their lives in the future.

The research of connected automated vehicles (CAVs) is an emerging topic within the field of transport & planning. It is not a question of whether the vehicles will be available for commercial use, but rather a question of when they will arrive. The safety of these vehicles is a necessary and ongoing discussion. In current research, a consensus is reached that crashes occurmainly due to human error. This human error is either due to negligence of the driving activity, like drunk driving or texting while driving, or due to incorrect decision making at critical moments. This study focuses on the topic of traffic safety with the principle of herd immunity in mind. It is theorised that crash risk can behave similarly to how a virus behaves (where crash risk is the chance that a crash occurs at a certain point in time). It spreads and infects vulnerable members of the population. The aim of this study is to determine whether the principle of herd immunity can be applied to car traffic, and if so, to what extent they can be compared through an impact assessment. The research question attached to this is: "How is traffic safety influenced by connected (automated) vehicles considering the concept of herd immunity?"

Overtaking on two-lane roads can cause dangerous situations, due to drivers’ errors during the gap acceptance decision. Understanding gap acceptance decisions can help mitigate these situations. Response time (i.e. the time it takes the driver to evaluate the gap and make a decision) has been shown to provide valuable insights into the cognitive processes during gap acceptance decisions in pedestrian crossing and left turn decisions. However, for overtaking, previous studies have not investigated the response time.
I investigated how factors that play a role in gap acceptance decisions during overtaking (namely, size of the gap and the drivers’ velocity) influence the response time. I also studied the change of the drivers' velocity during the decision process. I proposed a novel method to measure the response time in drivers' overtaking decisions, and conducted an experiment to demonstrate the usefulness of the method. 25 participants were presented with multiple overtaking situations in a driving simulator experiment, with varying distance gaps. I analyzed how the probability of gap acceptance varied with the distance gap and participants’ velocity at the start of the overtaking situation using a generalized logistic mixed model. I also analyzed how the response time varied with the distance gap, participants’ velocity and the decision outcome using a linear mixed model. Lastly, I analyzed if the velocity changed between the start and the end of the decision process and whether there was a relation between the decision outcome and the distance gap and the velocity change, using a linear mixed model.
The probability of accepting a gap increased significantly with the distance gap and the velocity of the participant. The response times for rejected gaps were on average 0.7s longer than accepted gaps. The response time increased with the distance gap (42ms per 10m), but decreased with the velocity (-92ms per 1m/s). The velocity changed differently between the decision processes leading to either decisions, with an average difference of 4 m/s.
Using the proposed method, I found that the factors which influence the outcome of the decision, also influence the response time. The dependence of response time on the distance gap and participants’ velocity could be explained by the speed-accuracy tradeoff or the difficulty of the decision. Furthermore, it was shown that the drivers already adapt their velocity during the decision process, instead of after they have made their decision. So, I can conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. My results provide basis for cognitive process models that can help further understand the results and are capable of predicting decision outcomes and response times.

The COVID-19 global pandemic has a great negative influence on the event industry. Decreasing number of festivals poses threats to cultural and economical development. This research aims to close this research gap by developing a SARS-CoV-2 transmission risk analysis method for large events by modelling crowd interactions at different types of event spaces and quantifying the SARS-CoV-2 transmission risks in the process. A method is proposed to connect activity scheduling, pedestrian route choice and movement modeling, virus spread modeling, and infection risk identification to determine the transmission risks throughout the events by probability method.

A case study applies the proposed method in a pre-pandemic music festival and shows its capability of revealing the general infection risk and the relation of influence factors to the transmission scale, and it also identifies risk-prone areas in an event. Comparing the different scenarios at each activity space, the general trend is identified that the transmission of SARS-CoV-2 is limited when the facility is located outdoor, the queue distance is increased, the density is lowered, and the respiratory activities are calmer. Compared to the real-life experimental events, the simulated results tend to be underestimating the risks due to assumptions of the event scale, infrastructure, compliance to measures, heterogeneity in the emission rate, activity schedules, group behavior, number of infectious individuals, and such factors that directly influence the amount of virus transmitted in the event. Nevertheless, this research has shown when and where major risks can occur during an event. It also gives indications on crowd management measures and interventions that can help reduce the virus transmission scale.

The proposed method allows future exploration and comparison of the transmission scales of large events, without posing ethical controversy of exposing people in infection risks. Yet, it has its limitations of not considering group behavior at the event, which is commonly observed at large events and may potentially increase the transmission scale. Another major limitation is its heavy dependency on detailed virus transmission parameters and activity patterns. The former needs to be validated under different scenarios, and the latter needs to be identified from the data collected at the same type of event in future research.

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.

In the Netherlands, the bicycle is a popular means of transport. One of the benefits of cycling is improved health due to more physical activity. However, cycling also causes a health burden due to the many cycling accidents in the Netherlands. The share of single bicycle accidents, where no collision with another road user is involved, has increased over the past years. In 2000 only 10% of all bicycle accidents were single bicycle accidents, while in 2018 this increased to 35%. One of the potential causes for single bicycle accidents is narrow bicycle paths. This research focussed on the influence of the bicycle path width of narrow bidirectional bicycle paths on steering behaviour, which can support a discussion on the required minimum width for bicycle paths in the guidelines. A controlled experiment is conducted to collect data of steering behaviour variables on different bicycle path widths while controlling other influencing factors. The results of the experiment show that cyclists cycle significantly closer to the path edge on narrower bicycle paths (55.6 centimeter on a 2-meter wide path and 37 centimeter on a 1-meter wide path), while there are no differences in average steering angle. An oncoming cyclist, which is in the experiment represented by a parked bicycle, forces cyclists to cycle even closer to the path edge, which makes the minimum distance to the path edge smaller. The minimum distance to the path edge becomes 15.1 centimeter on a 1.5-meter wide path. This smaller distance to the path edge increases the risk of riding off the path, which makes the narrower paths less safe. A questionnaire was used to examine the perception of the different path widths. The perceived safety of cyclists decreased on narrower bicycle paths. A 2-meter wide bidirectional bicycle path is considered safe to pass others by all participants, but on a 1.5-meter wide path this is only considered safe by half of the participants. The main research question was “What is the influence of the width of bidirectional bicycle paths on cyclists’ steering behaviour?”. It can be concluded that lateral position is related to the width of bidirectional bicycle paths, while steering angle rotation is not related to the width of bicycle paths. The results of the experiment are transferable to real-life traffic conditions, but do not include the variety in infrastructural conditions, cyclist ages and bicycle types. These varieties in real-life traffic conditions might increase the required bicycle path width.

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 question of how well in terms of safety can a driver take over control of an automated vehicle in response to an emergency situation is of crucial importance. Most of the studies performed so far focus on the drivers’ reaction times and the mechanisms behind the transition. In this study, an effort is made to incorporate the braking times that are required in order to finalize a safety maneuver, with the aim to assess the safety implications of the entire transition in control. For this purpose, a new methodology was developed and a simulation model was used in order to simulate platoons of CACC equipped vehicles. Two new KPIs were defined: the Time to Control and the Safe Time Budget. The results suggest that higher number of critical events and crashes are associated with higher market penetration rates. This reveals that despite the fact that AV can in general increase traffic efficiency and safety, when it comes to emergency situations where safety is inextricably linked to the combination of AV and driver performance, overall safety may be compromised under certain conditions. In addition, the results revealed a strong connection of the above-mentioned action times with the initial speed of the vehicles involved in a conflict. The findings of this research, point to new directions particularly in concern to the extension of the operational design domain of automated vehicles in order to minimize system deactivations, and also with regard to the need for better prediction models and safety assessment tools.

Tram is a sustainable mode of transport, which is able to transform the modern city centers through the various urban regeneration projects that usually come together with it. Tram tracks are often shared with either motorized traffic or pedestrians/cyclists. In this mixed traffic reality, tram accidents are very rare but severe at the same time. Tram driving is really a complex and very demanding task, since the driver should run on time, maintain his/her concentration, predict the behavior of other road users and protect the tram passengers from falls inside the vehicle cabin. In the past, a limited number of studies has attempted to examine tram safety, especially in urban areas, where trams interact with vulnerable road users (VRUs). Subjective notions of traffic safety, that are more connected with the behavior of tram drivers, such as: perceived safety and driving stress, have never been quantified. This thesis covers all the previously mentioned research gaps in order to explain tram safety problems. By utilizing the new knowledge, a list of practical recommendations, which can reinforce tram safety without downgrading system efficiency, is developed. For the development of statistical models related with perceived safety and driving stress, a stated preferences experiment was conducted in Athens. In the survey, tram drivers rated perceived safety and driving stress in a 7-point Likert scale. According to the estimated model of perceived safety, non-exclusive alignments, such as tram/pedestrian malls and mixed traffic operation, downgrade perceived safety. Furthermore, the existence of an unprotected pedestrian crossing and high volumes of VRUs influenced perceived safety negatively. Driving stress is affected mainly by arrival delay and load of standing passengers. Route familiarity is an additional important factor that influences driving stress. The existence of many random beta parameters in perceived safety and driving stress models confirms the subjective nature of these notions. No statistically significant correlation between these two previous notions was observed. Experienced tram drivers believe that they are ready to respond properly in a section that they perceive as unsafe, if they are familiar with it. If there is no familiarity, tram drivers lack confidence and therefore driving stress is increased. Objective safety was examined in the tram network of Amsterdam using accident records and spatial data, such as location of stations and pedestrian/cycling crossings, level of tram lines separation, cycling intensity, city attraction poles and city districts. In Amsterdam, more accidents per km appeared in tram/pedestrian malls; yet, most of the fatal accidents have occurred in tram tracks that are not shared with other road users. High concentrations of tram accidents were also observed around attraction poles and inside the city center, where the flow of VRUs is quite high. Lastly, as a practical recommendation, a consistent design of a tram line using the knowledge from the estimated perceived safety and driving stress models is developed for the first time.