Conditionally automated driving systems issue takeover requests (TORs) in situations that exceed their operational capabilities, requiring drivers to promptly resume manual control and maintain safe vehicle operation. A key factor in ensuring the smoothness of such control transitions is the time budget, i.e., the time offered by automation for control transitions. When the time budget is too short to accommodate the required takeover time (ToT, the time drivers need to regain manual vehicle control after receiving a TOR), the risk of accidents increases as drivers may lack adequate time to perceive, assess, and respond to the situation. Conversely, time budgets that substantially exceed the required ToT may also introduce risks: such TORs can be perceived as false alarms, leading to reduced driver attention and potential dangers, particularly when the out-of-capability situations are not readily perceivable to drivers. Therefore, defining and allocating sufficient time budgets is essential to ensure driving safety and user experience in vehicle control transitions.

This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.

The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles. ... Read more

Road traffic crashes cause over a million deaths and tens of millions of injuries annually, with the majority occurring in complex multi-directional urban traffic interactions such as merging, turning, and crossing, rather than on high-speed motorways. These collisions rarely stem from a single error, but emerge from escalating conflicts, leaving a time window in which proactive intervention is possible. This thesis systematically develops a data-driven methodology to quantify collision risk in multi-directional urban traffic interactions, in a way that is context-aware, generalisable across scenarios, and scalable without relying on crash labels.

The research progresses from foundational measurement to large-scale risk modelling. First, a two-dimensional coordinate transformation is introduced to normalise longitudinal and lateral spacing between road users. This enables consistent microscopic measurement of interactions and macroscopic analysis of required road space via an interaction Fundamental Diagram (iFD). Building on this representation, a unified probabilistic framework for conflict detection is formulated. It conditions collision risk on interaction context, including motion kinematics and environmental factors. A statistical learning pipeline is then proposed to estimate continuous risk scores that generalise across scenarios and capture a long-tailed spectrum from mild conflicts to near-crashes. To scale up without annotated crash or near-crash events, the Generalised Surrogate Safety Measure (GSSM) is developed as a self-supervised approach that learns collision risk from abundant naturalistic driving data. Further, contrastive learning is explored to more effectively exploit fine-grained interaction patterns.

Experiments on real-world datasets show that lateral interactions utilise road space more efficiently than longitudinal ones, and that collision risk forms a continuum without a universal boundary between safe and unsafe interactions. The proposed context-aware methods achieve state-of-the-art risk detection accuracy and alert timeliness. Environmental factors such as rain, lighting, and surface conditions are shown to significantly impact collision risk. With increasing data in training and factors in consideration, extreme conflicts can be inferred more effectively from everyday interactions.

The proposed methods enable consistent measurement of road user interactions, adaptive conflict detection, unified collision risk scoring, and scalable learning in multi-directional traffic. In practice, the results can support applications in traffic management, advanced driving assistance and automated vehicles, real-time risk monitoring, and accelerated road safety policymaking. All these contribute to a broader shift from reactive to proactive road safety, aligning with the vision of eliminating traffic fatalities and creating more resilient urban transportation systems. ... Read more

Urban mobility systems are intrinsically complex, requiring robust data-driven insights for effective planning. This thesis uses GSM mobile phone data to examine how data quality, spatio-temporal resolution, and socio-spatial factors influence travel demand in metropolitan networks. By linking land-use and demographic attributes to trip production patterns, it offers valuable guidance for tailoring transportation policies to local needs, ultimately fostering more efficient and responsive transport systems. ... Read more

This PhD dissertation examines the societal implications of newly emerged, two-sided ridesourcing platforms that connect travellers with self-employed drivers. It uses an agent-based approach to model the dynamic interaction between ridesourcing supply and demand, exploring how market outcomes are influenced by travel demand, labour market conditions, and platform strategies. The findings inform policymakers on how to enhance the welfare effects of these markets for travellers, drivers, and the broader public. ... Read more

Modern societies are heavily relied on efficient transportation systems for mobilizing people and goods. These systems are mainly constituted by road traffic networks. Currently, traffic demand is immense and perpetually increasing with unprecedented rates that traffic congestion has become an imminent subsequent. Over time, all this human activity that has established the status-quo of modern societies has been negatively influenced by climate change. Climate deviations are prominent in urban environments with a higher frequency and elongated time scales. Therefore, road traffic systems jeopardizing their robustness, and their resilience is at stake. A fundamental component in road traffic systems is the human factor. Nevertheless, human factors, to the contribution in traffic, are largely neglected. Some other times we consider that humans act rationally. Consequently, engineers seek answers to questions of how to incorporate the human factor into the system to explain the behaviour of human drivers under adverse weather conditions. In this contribution, an exploratory simulation study was used to put into perspective the derived conceptual frameworks and assess their performance in terms of efficiency and safety. Various psycho-cognitive mechanisms were utilized to address the human factor and rationally connected with the vehicle motion to reproduce the traffic phenomena that we observe under the conditions of rain and fog. ... Read more

A new generation of intelligent Traffic Light Controllers uses Model Predictive Control to minimise delay at signalised intersections. The conventional cycle of set sequences of green phases is dropped for optimisation purposes. This research uses the ethical theories utilitarianism, egalitarianism, sufficientarianism & deontology to define equity. An explicit connection of these ethical theories and technical principles of conventional traffic light controller CCOL & new generation traffic light controller Flowtack, has been provided to identify the change in ethical landscape, from predominantly egalitarian towards relatively utilitarian. Performance indicators for equity in traffic control are defined based on earlier research. Multiple setting changes in Flowtack are proposed and tested in simulation experiments with Aimsun. These experiments show that adjusting Flowtack's objective function can improve the equity scores according to egalitarianism & sufficientarianism, at the cost of the equity score of utilitarianism. The best results are validated using various flow compositions on an alternative intersection. As a result, the gain in equity by the proposed settings becomes greater, as the flow composition becomes more uneven.  ... Read more

This thesis proposes a truck arrival shift (TAS) policy to control truck arrivals at seaport terminals. The aim is to reduce congestion at terminal gates which is caused by a lack of port-hinterland alignment. We proposed, developed, and applied a modeling framework to assesses the impact of the TAS policy for the use case of the Port of Rotterdam. This policy is designed for the implementation of a Time Slot Management System (TSMS) and takes the behavioural aspect of Truck Operating Companies (TOC) into account. The time of day preferences of TOC for container pick-ups are inferred from the exchange of information between port and hinterland stakeholders using discrete choice modelling (DCM). These preferences are used to shift truck arrivals and consequently reduce the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TAS policy, we developed a simulation platform that resembles terminal operations using discrete-event simulation (DES). For the allocation of trucks to a certain time period, a choice-based heuristic is designed to approximate the optimum configuration of the TAS policy. The optimum TAS policy design shows that significant gain can be obtained at a low shift rate. Moreover, a measurable amount of waiting time gain can be achieved by the application of the designed TAS policy. ... Read more

In the future, Human Driven Vehicles (HDV's) are expected to interact with Automated Vehicles (AV’s) and Connected AV’s (CAV’s). Due to the differences in the expected driving behavior of AV’s (ACC) and CAV’s (CACC) compared to HDV’s, the nature of traffic breakdown phenomena in the future can be expected to change. AV’s (ACC) and CAV’s (CACC) refer to AV’s enabled with Adaptive Cruise Control functionality and CAV’s enabled with Co-operative ACC functionality respectively. Currently, there are traffic management measures which address traffic breakdown for the current situation. With the expected changes in breakdown phenomena in the future, will the current measures be effective in addressing the different nature of breakdown in the future? This research answered this question through simulation (Vissim), by focusing into the effectiveness of one of the current measures. The current measure whose effectiveness was analyzed is Variable Speed limits (VSL) applied through the concept of feedback Mainstream Traffic Flow Control, MTFC-VSL, at on-ramp merge sections. Before conducting simulations, it was hypothesized that MTFC-VSL control effectiveness in addressing traffic breakdown increases as CAV’s (with ACC and CACC functionality) penetration rate increases in mixed traffic, because CAV’s can be expected to precisely follow the speed limits. Mixed traffic in this research comprised of HDV’s and CAV’s (with ACC and CACC functionality). CAV’s (with ACC and CACC functionality) implies that CAV’s majorly differ with that of HDV’s in car following behavior and not in lane change behavior. Simulation results and analysis revealed that the hypothesis doesn’t hold good. Improvements in average Travel Time (TT) of mainline vehicles and average network speed due to the presence of MTFC-VSL control compared to the absence of it, deteriorated as penetration rate of CAV’s (with ACC and CACC functionality) increased until 20% in mixed traffic. For further penetration rates the improvements fluctuates. On-ramp vehicles for most of the scenarios of mixed traffic, are better off without MTFC-VSL control as the presence of it increases the vehicles average TT. MTFC-VSL control doesn’t effectively address the capacity drop phenomenon for various scenarios of mixed traffic. Lastly, it was found that for less than 20% CAV’s (with ACC and CACC functionality) penetration rate in mixed traffic, MTFC-VSL control effectiveness can be expected to overall increase if Intelligent Speed Adaptation is installed as an On-Board Unit in HDV’s, as it limits HDV’s exceeding the speed limits.It must be noted that, MTFC-VSL control was set up considering the practical considerations of implementing in real life, which can also be expected to play a significant role in hypothesis not being valid. Given the ineffectiveness of MTFC-VSL control for various scenarios of mixed traffic, the future traffic management measures should focus on the causes of breakdown phenomena which aren’t addressed by MTFC-VSL control. One of the proposed measures is a combination of a merging assistant strategy & MTFC-VSL control to better address traffic breakdown than MTFC-VSL alone. Merging assistant strategy utilizes the connectivity feature of CAV’s to foster smoother merging of on-ramp vehicles which isn’t addressed by MTFC-VSL control. ... Read more

The number of passengers using public transport has decreased drastically as a consequence of the global coronavirus pandemic in 2020. This study aimed to retrieve the importance of the perceived risk of getting infected with the coronavirus in choosing to go by train in the Netherlands. With a Hierarchical Information Integration approach it was possible to retrieve the perceived importance of different risk factors on the likelihood to get infected and evaluate this with respect to the taste for travel time and travel costs. After collecting 408 responses, a multiple linear regression revealed that on-board crowdedness was perceived as the most important risk factor. With discrete choice modelling we were able to calculate that an average traveller is willing to pay around 0,88 euros to reduce the seating occupancy with 10%. Furthermore, we were able to conclude that both the obligation to use face masks and extra cleansing of contact surfaces negatively influence the perceived risk and thereby increase the chances of going by train. Since extra cleansing is not extensively done yet in Dutch trains, it could, together with reducing crowding levels, be the key to nudge people back into the train. ... Read more

Dynamic traffic management (DTM) plays an important role from Dutch policy perspective to prevent road congestion and has been developed from control strategies to services. Five traffic control centers, 22 different DTM systems with 35 functions and over 50,000 DTM components make up the national traffic management network in the Netherlands. The malfunctioning of the DTM systems is expected to create negative impacts to the traffic, proper maintenance planning is necessary to ensure their availabilities. However, there is less knowledge about the DTM malfunctions, which makes it difficult to monetize the malfunction effects and therefore to optimally deploy the maintenance budget. In this research, a macroscopic dynamic traffic assignment model “MARPLE” is used to evaluate the social costs of the DTM malfunctions according to the failure function, failure duration, and failure location.
The motorway network around Amsterdam is chosen as the study area in this research, and four DTM systems and measures were evaluated, including the rush hour lane (RHL), the motorway traffic management (MTM) system, the dynamic route information panels (DRIPs) and the ramp metering (RM) system. By conversing the DTM malfunctions into the motorway network, the introduced impacts to the traffic both in local and network levels are identified.
This research made the first attempt to modify DTM malfunctions in a macroscopic dynamic traffic assignment model, and a methodology was developed to calculate the malfunction costs both in traffic flow and safety aspects. The outcome of this research answered what-if questions with regarding to DTM malfunctions, it also proved the feasibility of the ambition to translate the DTM malfunction impacts at a network level into its social costs, according to which the maintenance strategy for the DTM systems can be better deployed. Overall, the initial goal of calculating the malfunction costs of the DTM systems with a newly developed methodology is met. Through the identified limitations and improvement strategies, the framework developed in this study could offer the possibility to refine the analysis, and/or easily be applied to other DTM systems and road parts. ... Read more

An ideal road management system requires a real-time assessment of pavement performance. But there are many root causes of the status quo that the expectation and the system have not been achieved yet. One of them is the monitoring system. In the Netherlands, the measurement is carried out once a year because of the budget and also because it is time-costly. To set up a real-time assessment system of pavement condition, this thesis proposes to use the real-time traffic data as the indirect way of monitoring the road, and applies three performance models to verify the correlation between traffic flows and road performance. The applied models are the regression models, the survival model, and the decision tree classifier. As a result, according to the test data of A15 in the Netherlands from 2015 to 2018, all the models indicate that the traffic characteristics influenced road performance, particularly, the traffic flow could worsen the pavement condition which already performed badly. The model result confirms the feasibility of establishing a real-time assessment tool of pavement condition by using the traffic data as the monitoring method. The specific establishment process of the tool is designed in the final. ... Read more

Public transport (PT) plays an increasingly important role in solving mobility challenges, especially in densely populated metropolitan areas. Further improving PT systems requires more advanced planning and operations. Fortunately, the considerable amount of data that have become increasingly available for PT systems offer an opportunity to address this challenge. However, how these data can be effectively used to achieve this goal still remains as an unresolved question in the scientific literature. More research is therefore needed to bridge this gap in order to advance PT systems for addressing mobility challenges. To this end, this dissertation is focused on developing methods and models for translating high-volume data from various sources into novel knowledge and insights that can be used to improve PT planning and operations. This dissertation first examines how to obtain onboard occupancy of PT vehicles by integrating all the three different data sources mentioned above. Second, this dissertation deals with the issue of high-dimensionality in large-scale passenger flows. Third, we propose a k-means-based method to cluster PT stops for constructing zone-to-zone OD matrices. Fourth, this dissertation presents a new method for analyzing the accessibility of PT service networks based on a novel network science approach. Last, we investigate whether passenger flow distribution can be estimated solely based on network properties in PT systems. ... Read more

Over the past decade, the development of ICT and online platforms has provided the infrastructure for new ways of sharing on a scale never seen before which are causing a shift from ownership to access-based- consumption. This trend offers promising prospects for the case of mobility but the true magnitude of impact that the increasing popularity of shared mobility services will have on the total transportation system remains uncertain. For NS, as largest railway operator in the Netherlands, it is therefore relevant to investigate how these new services can contribute to better first and last mile transportation within the multimodal train trip, as most of these types of shared mobility operate on an urban scale. Accordingly, this study aims to explore and measure the factors that affect people’s willingness to use shared mobility services as access or egress transport in multimodal train trips. A series of stated choice experiments was developed in which respondents were asked to choose their preferred mode from a set of alternatives for a given access- or egress trip. Next to conventional modes, included shared modes were bike, (standing) e-scooter, and car. By applying discrete choice modelling, separate mixed logit models were estimated for the home-based side trip (origin to railway station) and the activity based side trip (railway station to final destination) in order to assess the impact of choice factors related to characteristics of the available modes, trip, and traveler. Results show that the willingness to use shared modes is in the first place strongly affected by familiarity with these modes. As the overall observed familiarity and in particular experience with shared modes was low, intrinsic (negative) mode preferences were found to be the dominating choice factors. This was especially the cases for shared e-scooter and to a lesser extent also for the shared car. Traveler characteristics were found affect the magnitude of the fixed mode preference in a sense that young and higher educated travelers significantly appeared to be more open to try shared modes. Contrary to the e-scooter and car, the shared bike exemplifies a more familiar option which was found to results in a different hierarchy of mode related factors: the general fixed mode preference becomes less dominant and usage costs gains more importance. ... Read more

With the increasing use of big data in varied applications to improve decision making and provide new insights, the research explores the potential of the Uber Movement data set released by Uber comprising of travel times from one zone to the other. A better understanding of the potential of the dataset could lead to the addition of the existing tool kit of Transport planners and city officials at the municipality of Amsterdam. Moreover, it would be the first of a kind data set enabling an understanding of taxi movement in the city. The Uber Movement Travel Time comprises of the average travel time between two wijken, where the ‘sourceid’ and ‘dstid’ do not correspond to the origin and destination of a trip but simply represent the directionality of the travel time measured. The data is aggregated across different levels of temporal detail and the number of data points directly corresponds to the level of temporal aggregation. For instance, if the quarterly aggregated data for the different days of the week is downloaded, the number of data points between a ‘sourceid’ and ‘dstid’ cannot exceed seven.
Three aspects of the data set were explored: 1) ability to capture the demand for Ubers 2) ability to capture recurrent congestion and 3) ability to capture non-recurrent congestion. While the data according to the Uber Movement and previously used instances, the data is suited for performance (recurrent congestion and non-recurrent congestion) and impact-related studies of the network. The absence of route related information limits the applications of the data. The potential of the data is also limited by the data sparsity. The potential of the data was best revealed through demand studies which indicated a skewed user group of tourists, airport users (to and fro), work-related trips and users using Ubers late at night. In addition, with respect to the goals of the municipality in managing traffic activity across different zones and time periods, by implementing and extending an existing model in the form of adding ‘occupancy related measures’ and ‘shortest path’. Thus, based on the data penetration levels and travel time data, the model developed offers insights at a strategic level to the city in the form of Spatio-temporal concentration of Uber vehicles, occupancy levels through the day. The potential of the data lies in its ability to offer strategic insights to the city of Amsterdam and the greater Amsterdam region in the form of the unique Spatio-temporal spread of Uber vehicles across different hours of the day. ... Read more

Automated vehicles are conventional vehicles equipped with advanced sensors, controller and actuators. They achieve intelligent information exchange with the environment through the onboard sensing and cooperative system. vehicles are possible to have situation awareness and automatically analyze the safety and dangerous state of journeys. Finally vehicles can reach destinations following drivers' willing. The ongoing research on intelligent vehicles is mainly about improving the safety, comfort, efficiency and provide an excellent human-car interface. As a self-organizing system, the traffic system is quite complicated. There are many disturbance factors to lead to various traffic problems. One of the daily occurring problems is congestion on the motorway. In order to reduce congestion, Rijkswaterstaat applies various dynamic traffic management (DTM) measures to guide the traffic. It works well nowadays in conventional traffic. However, automated vehicles entered the market recently and will start to play an essential role in future traffic. The automated vehicles' reaction to DTM measures may be different from conventional vehicles while the traffic problems still exist. Therefore, it is necessary to research the effectiveness of current Dutch traffic management in automated vehicles. This thesis aims to investigate the effectiveness of current Dutch DTM measures with driver assistant and partially automated vehicles. Due to the time limitation, only the ramp metering measure will be researched through a simulation study. Therefore the main research question is 'How partial automated driving influences the performance of current Dutch dynamic traffic management system and how can this be evaluated via simulation?'. Three methods are applied, including literature review, simulation and statistical analysis. The literature part reviews levels of automation, various longitudinal and lateral vehicle motion models, which are chosen and modified in the simulation. Many ramp metering algorithms are also introduced in the literature review. The ramp metering controller in the simulation follows RWS algorithm. Besides, the motorway demand and the penetration rate of level 1 and 2 vehicles are two input of the simulation.
From the simulation results, it is concluded that the level 2 automation consisting of Adaptive Cruise Control (ACC) and Lane Change Assistance (LCA) system brings a negative impact on the motorway capacity. The ramp metering measure remains efficient if the penetration rate of level 2 vehicles is low. However, when the capacity reduces to the critical flow set up in the ramp metering controller, Ramp metering loses its efficiency. The parameters in the ramp metering controller therefore, require an update. For further research, it is recommended to simulate the same scenarios with different ramp metering algorithms. Since the functions of the algorithms are different, there might be other robust control algorithms for automated vehicles. Besides, another limitation of this thesis is that the automation system in level 2 vehicles is defined as Adaptive Cruise Control (ACC) plus Lane Change Assistance (LCA) system. Other partial automation systems may have a different effect on the performance of ramp metering. This thesis can be expanded by research the ramp metering performance under various types of partial automation systems. ... Read more

Passenger travel characteristics (PTCs) are characteristics that are sought from the information contained in trips, such as travel frequency, travel mode, departure location, and etc. Understanding PTCs can help develop passenger-oriented planning and service policies, so analysis of PTCs is a topic of constant interest to researchers and transportation service providers. However, PTCs vary with the change of many factors, such as network, price policy, service. The process or trend of PTCs change, which is called the evolution of PTCs, are useful for assessing the impact of external factors changes on passengers. ... Read more

Based on incident characteristics and quality of loop data and floating car data, a new incident detection algorithm is designed based on floating car data. This new algorithm can detect incidents on lane level by comparing the number of lane changes for a situation without an incident with a situation with a possible incident. Floating car data can give information about the number of lane changes if the accurancy is high. The floating car data is used as input for the new algorithm. The results of this new algorithm are comparable or better than the current McMaster algorithm, depending on the available penetration rate of the floating car data. ... Read more