In response to the automobile-oriented urban street designs, this study explores the implementation of "Fair Streets" in school zones in the Netherlands. This research aims to create a transition from only vehicle prioritization to also a focus on social, environmental, and community functions on streets around school zones. Currently, up to 50% of public space in cities is allocated to car infrastructure, leading to significant social and environmental drawbacks. This research uses a multi-criteria decision-making method, applying the Best-Worst Method (BWM) for interviews and surveys amongst key stakeholders at the Fuutlaan in Delft to evaluate stakeholder perspectives and integrate various needs into renewed street designs. The interviews reveal a strong preference for a more sustainable design, emphasizing the need for resistance to rainfall and heat, which aligns with the municipality's redevelopment goals. Also, designs that reduce car parking space in favor of inclusivity and traffic safety are desired, reflecting a shift towards a more fair use of street space. However, the acceptance of this redevelopment among some car users remains low, causing resistance. Therefore, this study advocates for policy adaptations that increase the feasibility and acceptance of such transformative urban designs. Recommendations for practice include involving the stakeholders early in the planning process and keeping them well-informed and educated about changes in the neighborhood. Also, creating trials with space usage changes and providing sufficient transportation alternatives are recommended. For further research, it is important to focus mainly on this topic's social aspect. To increase acceptance and participation, methods are needed to approach and convince people to support a fairer street.

Electric vehicles (EVs) are required according to the EU legislation from 2014 to install Acoustic Vehicle Alerting Systems (AVAS) to convey information about the EVs through sound signals to other road users, ensuring their safety. Meanwhile,autonomous vehicles (AVs) are also recommended to be equipped with an External Human-Machine Interface (eHMI) system, enabling better interaction with other road users. . However, for the special group of autonomous electric vehicles (AEVs), considering that equipping both eHMI and AVAS, given their overlapping functionality in conveying information, might result in a waste of resources, it remains unclear whether it's feasible to use only eHMI for communication with road users as a cost-saving measure, thereby omitting AVAS or perhaps adding only a simple sound signal to replace AVAS. This study aims to explore whether autonomous electric vehicles need to add extra noise to provide more information to other vulnerable road users, such as cyclists and pedestrians. In this study, we will use Virtual Reality (VR) technology for simulation experiments. Utilizing VR, as opposed to real-world experiments, allows for precise control over the variables in each experiment, ensuring that experiments can be conducted under the same conditions multiple times, thus enhancing reliability. On the other hand, VR can ensure the safety of experiment participants while providing them with an immersive experience, especially in experiments related to traffic safety research.

In this experiment, we will utilize the Unreal Engine to create a simulated testing environment, focusing on observing participants' (acting as cyclists) reactions to a series of variables. These include different environmental noises, the warning distance of sound signals emitted by autonomous vehicles, and the autonomous vehicles themselves. The sound signal warning distance specifically refers to a mechanism where, when pedestrians or cyclists enter a predefined range of the vehicle, it automatically emits a warning signal. This signal is to alert the approaching individuals that the vehicle is in a ready state and may proceed to the next action, thereby enhancing safety interaction and awareness on the road.

The data collected from this experiment will be divided into two parts: first, the participants' reaction times, speed adjustments, and distances obtained from VR devices; second, their perceptions of the trust, perceived safety, and comfort of interactions with autonomous electric vehicles gathered through post-experiment surveys.

The results of this experiment will assist policymakers in refining relevant laws and regulations, as the existing legislation concerning electric vehicles and AVAS do not take autonomous vehicles into account. It will also provide theoretical support for car manufacturers in the design of autonomous electric vehicles.

Enhancing and expanding public transportation is becoming a crucial solution to the high costs of congestion and the escalating environmental impacts of car-centric transportation systems observed in numerous cities today. One approach to enhance the quality of public transportation, and thereby boosting its ridership, is through the intelligent design of stop locations. Strategically locating stops can increase coverage of transit and reduce overall trip times. However, selecting stop locations is a relatively intricate task as it involves striking a balance between two competing objectives; accessibility and efficiency.

In this thesis, a multi-objective optimisation model is presented to make the trade-offs between several factors influencing stop locations explicit. The model enables a comprehensive assessment of transit objectives by evaluating alternatives, determining demand and running times in detail, and examining network effects. Relevant factors such as sociodemographic characteristics within catchment areas, travel patterns, transit alignments, and transfer locations are modelled for different areas of the system.

The developed model is employed in a case study that focuses on the tram network of The Hague in the Netherlands. Various objectives are evaluated to determine the optimal stop locations and the factors that affect them. The results reveal that the ideal stop locations are contingent on the objectives established. Despite this, the number of stops in the system of The Hague is reduced by at least 6% for all considered objectives. When the user costs are minimised, an increase in ridership of 4% can be achieved, whilst saving 3% of operator costs. Moreover, it is concluded that optimising the network for either the operator or society results in 17% fewer stops, and a subsequent estimated cost saving of 9%, without any negative impact on ridership.

Road traffic safety is a pressing global concern, with millions of yearly fatalities and injuries. This study aims to address the detection of abnormal driving behaviour. Traditional supervised approaches face limitations due to the need for labelled abnormal driving data. To overcome this challenge, semi-supervised machine learning models are explored and developed in this research.
Machine learning is utilized for abnormal driving behaviour detection because it offers a data-driven approach that adapts to different scenarios and captures subtle patterns. Furthermore, its scalability allows for efficient analysis of large datasets, leading to accurate identification of abnormal driving behaviour and valuable insights for enhancing road safety measures. Most existing machine learning (ML) based abnormal driving detectors rely on (fully) supervised ML methods, which require substantial labelled data. However, in the real world, labels are only sometimes available, and labelling large amounts of data is tedious. Thus, there is a need to employ unsupervised or semi-supervised methods to make the detection process more feasible and efficient. Luckily, it is possible with the advent of deep neural networks, especially autoencoder-based ones. This thesis develops and compares three ML methods: supervised (e.g. XGBoost and Random Forest), unsupervised ML (e.g. Isolation Forest and Robust Covariance), and semi-supervised ML (Hierarchical Extreme Learning Machines). Comparison results show that the semi-supervised deep learning model outperforms unsupervised methods exhibiting higher prediction accuracy and delivering acceptable results compared to the fully supervised models.
Moreover, previous ML-based approaches predominantly utilize basic car motion features (such as velocity and acceleration) to label and predict abnormal driving behaviours. In contrast, this thesis introduces Surrogate Measures of Safety (SMOS) as features for ML models to identify abnormal driving behaviour.
The results indicate that the supervised model performs best under the same conditions. However, relying on a large amount of labelled data in supervised models can pose challenges in real-life scenarios or when dealing with massive datasets. The study highlights the significance of Surrogate Measures of Safety (SMOS) and demonstrates the potential of HELM in effectively identifying abnormal driving behaviour. The introduction of SMOS significantly improves the performance of both unsupervised and semi-supervised models. The unsupervised model shows the most substantial improvement, increasing accuracy from less than 50% to over 90%.
While the Isolation Forest and Robust Covariance models fail to detect abnormal driving behaviour without including SMOS, the semi-supervised HELM model exhibits promising results even without SMOS. However, further research is necessary to address limitations and enhance the findings. While valuable, the current dataset used in this study may only encompass some types of abnormal driving behaviour. Future research should incorporate a more diverse dataset that covers a broader range of abnormal driving behaviours. The analysis should include multiple SMOS features, such as Post Encroachment Time (PET), to comprehensively understand abnormal driving behaviour and improve safety measures.

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.

In the face of urbanization and limited space, traffic management grapples with challenges, especially heavy left-turn volumes at signalized intersections. The pre-signal strategy, proposed by Li et al. (2014), emerges as a promising solution. It involves an additional traffic light upstream of the main intersection, creating a "sorted area" where vehicles distribute before reaching the main intersection. Although simulation studies show increased intersection capacity with pre-signals, behavioral assumptions about drivers' spread behavior in the sorted area lack validation from behavioral science.

This study investigates drivers' behavior in pre-signalized intersections, addressing how their lane selection downstream of pre-signals impacts efficacy. The main research question is: How does drivers' lane selection downstream of pre-signals impact the efficacy of pre-signal implementation for regular traffic? A stated-choice experiment involved over 1000 respondents making preferred lane choices in diverse traffic conditions. Analysis using a MultiNominal Logit (MNL) choice model reveals crucial insights.

Results indicate that pre-signals don't alter drivers' perceptions of traffic crowdedness. However, discomfort with lane changes emerges as a critical factor influencing efficiency. Drivers strongly hesitate to execute multiple lane changes, posing challenges for achieving an equal spread in the sorted area.

Two implementation options are discussed: using the entire sorted area for all traffic or dedicating parts to specific traffic directions. Each option has efficiency and safety trade-offs. Designing a pre-signal system achieving both an equal spread and using the entire sorted area for all traffic proves unfeasible.

The study emphasizes integrating behavioral science into pre-signal studies, offering valuable insights for traffic engineers. It lays the groundwork for understanding pre-signals, highlighting the imperative for continued research and a holistic approach to their implementation.

Traffic congestion and undeveloped public transport define the modus operandi in Kampala, the capital city of Uganda. Most people see motorcycles as a solution to escape the growing traffic congestion and poor public transport. This study thus aimed to determine the traffic efficiency and safety effectiveness of introducing motorcycle dedicated lanes in non–lane based and mixed traffic. These effects were studied by microsimulation of unsignalized intersections without priority markings in urban areas of developing countries. The study centered on an intersection commonly known as “Spear Motors” in Kampala. In addition, off-peak traffic was simulated since traffic police controls the intersection during peak time and microsimulation model cannot simulate traffic while a policeman (or two) is on duty.
Video images of the intersection were officially obtained from Uganda Police and processed to traffic data such as traffic volume, trajectories, composition, and conflicts. In addition, geometric data (lane width, slope, intersection dimensions, coordinates, and aerial pictures) about the intersection was physically collected during a daytime site visit.
A representation of the current traffic conditions at the intersection, referred to as the base model, was developed in VISSIM 21. The base model was calibrated using Particle Swarm Optimization (PSO). Thereafter, the model was microscopically and macroscopically validated.
Three scenarios were considered in this study, which are: scenario 1 with straight crossings of motorcycles in the intersection, scenario 2 with deflected crossings and scenario 3 with a roundabout separated for the main road traffic and motorcycles.
Simulations with driver behavior parameters set to optimum values were performed to replicate the actual behavior. Consequently, traffic conflicts were generated from vehicle trajectories per scenario. Post Encroachment Time (PET) and Time to Collision (TTC) were calculated for the conflicts. The use of conflicts is proactive compared to reactive approaches that use crashes.
The use of deflected crossings for motorcycles and then a roundabout separated for main traffic and motorcycles increased the flow of the intersection by 31% and 20%, respectively. Additionally, the implementation of deflected motorcycle crossings and roundabout reduced critical density by 38.6% and 63.7%, respectively.
Regarding safety, more benefits are expected by using either deflected crossings or roundabout than straight crossings. The number of severe critical conflicts reduced by 87.9% with the application of a roundabout separated for main road traffic and motorcycles. This is higher than the decrease of 48.5% after the application of dedicated motorcycle lanes with deflected crossings. To determine the conflict rates per scenario, the number of severe critical conflicts was divided by total traffic simulated. Deflected crossings for motorcycles reduced severe critical conflict rate by 40% whilst it decreased by 75% with intersection improvement to a roundabout separated for main traffic and motorcycles.
With the high motorcycle demand of over 50% of traffic, implementation of dedicated motorcycle lanes at unsignalized intersections as well as urban roads in general will result into better flow of traffic. In addition, the motorcycle lanes will contribute to the general road safety in Kampala.

The European Commission has mandated the requirement of advanced safety features in all vehicles sold in the EU from 2022 onwards. This will help drivers get gradually used to ADS, eventually transitioning to fully autonomous driving in the distant future. There are more vehicles on road now equipped with lane-keeping systems and other driver assistance systems due to the added benefit of safety. One such Advanced Driver Assistance System (ADAS) feature is the lane-keeping system which has plenty of performance evaluation research being done on it but lacks research from an Operational Design Domain (ODD) perspective in simulation. ODD is the set of conditions for which the vehicle is designed to function. There are many ODD taxonomies present in academic literature for the classification of ODD attributes in the ODD definition. These are recommended to be used by Original Equipment Manufacturers (OEM) to classify their ODD definition of the system for effective comparison between the same ADAS feature offered by two different OEMs. However, this is not seen to be done by the OEMs and causes a disparity between the ODD definitions, leading to misunderstanding or misinterpretation of the defined ODD. The OEMs do not exactly specify the ODD boundaries in publicly available instruction manuals of the vehicles. The conditions where the system cannot function properly are very vaguely mentioned. Furthermore, the different OEMs provides the same ADAS feature with different names with differently defined ODD boundaries. Bringing these ODD definitions of all these OEMs under one umbrella is currently being attempted by policymakers and regulatory organisations. A standard way of testing the impact of the different ODD attributes on the performance of a lane-keeping system is still absent. Therefore, this research aims to provide a method to assess the ODD boundaries of a lane-keeping system using the simulation software Prescan by Siemens and is intended to be used by researchers, policymakers and OEMs to refine the ODD and to test the effect of the ODD attributes on the performance of the system. It was found in this research that there is a strong interdependency between the tested ODD attributes. This can lead to a possibility that the ODD definition may not exactly be a straightforward set of specific values in the form of a table, but instead can be dynamic considering this interdependency. It was also observed that some test cases classified inside ODD from the initial ODD assessment had poor lane-keeping performance and some test cases classified outside ODD had good lane-keeping performance. Another observation from the test cases was the ability of the test vehicle to move at higher speeds in narrow lane widths with good lane-keeping performance compared to wider lane widths. This falls in line with findings from previous literature and will require validation. The research approach of this study is used to develop a conceptual framework to assess the ODD boundaries of an ADAS feature in a simulation environment.

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

Advanced Driver Assistance Systems (ADAS) are becoming more available and will become mandatory for all new vehicle models from 2022 onward. In order to achieve the highest safety benefits, it is important that these systems are available. Lane Keep Assist (LKA) is part of ADAS and assists the driver in the lateral control of the vehicle. Lane markings are used by both human drivers and machine vision to stay on the road, but factors contributing to lane marking detection in different driving conditions are mostly unknown. A field test was conducted on Dutch provincial roads to evaluate lane marking visibility properties in relation to the LKA detection performance of different sensor types. The LKA detection performance of the mono camera was found to be higher in most weather and illumination conditions than the detection performance of the mono camera with infrared. The mono camera with infrared had a higher detection performance during rain in nighttime conditions than during dry daytime conditions. The highest detection performance for the mono camera and the mono camera with infrared were 97% in dry nighttime conditions and 91,4% in sunset conditions, respectively. Binary logistic regression was used to determine the effect of lane marking properties on the lane detection performance. A profiled lane marking type was found to increase the detection likelihood by 6-8 times as opposed to a smooth lane marking type. Other visibility properties, such as retroreflectivity and contrast with the road surface, were not found to be a significant contributor to the detection performance.

The emergence of connected and automated vehicles (CAVs) could have a significant impact on traffic efficiency and safety. The effects of CAVs in regular highway traffic are relatively well represented in scientific research. How these effects change in exceptional situations, such as work zones, and how the infrastructure can influence these effects is unknown however. Therefore, the main goals of this study are (1) to better understand the potential impacts of CAVs on traffic efficiency and traffic safety in highway work zones under different circumstances, and (2) to make a well formulated estimation of how current communication in work zones will need to be changed in the future. Two highway work zones were simulated in VISSIM. Three different types of CAVs (cautious, normal & all knowing) were implemented, each at 5 different penetration rate levels (0-100% with steps of 25%) in combination with conventional human operated vehicles to assess the traffic efficiency and safety effects. The traffic demand was kept constant at a theoretical F/C-ratio of 1. Based on first observations two communication strategies were added to the networks aimed at CAVs. These were (1) an early merge strategy and (2) an increased headway strategy. It was concluded that cautious CAVs have a negative effect on traffic efficiency and safety. The magnitude of these effects increased as the penetration rate increases. As CAVs became more aggressive, the traffic efficiency was increased. The traffic safety was deteriorated however, as a result of short time headways. The two communication strategies showed great potential to relieve the congestion in cautious CAV scenarios and to increase safety when entering the work zone.

This research examines the incorporation of morality in discrete choice modelling, applied to the specific case of the choice for ADAS in lease cars. Implicitly, the decision to include or exclude ADAS in a car affects the safety of the driver himself and other road users. Therefore this decision has a moral dimension; a moral dilemma arises during the trade-off between safety, comfort, and investments in ADAS. This research describes a stated preference experiment in which respondents choose between different option packages for lease cars. The target respondents need to make trade-offs between costs, comfort, and safety provided by ADAS. The results show that an increased perceived safety and comfort for the driver/passengers are both more preferred than an increased perceived safety for other road users. Also, the results show that innate morality plays an important role in these preferences, which is tested regarding five moral foundations. Furthermore, a booster that aims at increasing moral choice behaviour is tested, which results to have the opposite effect.

This study aims to explore the preferences of citizens regarding fairness considerations related to the distribution of the effects of road safety policies in order to provide with policy recommendations that will help to promote more fair road safety policies. To achieve this aim the Discrete Choice Approach is going to be followed, using stated preference data. This requires a Stated Preference experiment to be conducted, which consists of two distinct parts. The first part is related to the citizen’s perception of fairness of different types of distribution of the effects of road safety policies, while the second one is related with the importance of this perceived fairness in the preference of citizens over specific road safety policy alternatives. In the first case, two Linear Regression models have been estimated, while for the second case, three Discrete Choice models. This study has shown that the Discrete Choice Approach can actually give some insight to moral dilemmas as it is suggested in literature. It also showed that low public acceptance can be a show-stopper for road safety policies, thus looking only at the aggregate effects of a road safety policy can be often misleading. The way that the effects of road safety policies are distributed among different groups of people can have a significant influence on the public acceptance of road safety policies.

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.