This paper presents a comprehensive and quantitative framework for enhancing the safety of autonomous vehicles by integrating sensor detection performance with braking dynamics under extreme weather conditions and variable road slopes. Using high-fidelity simulations in CARLA and PyChrono, extreme weather datasets are generated, sensor detection distances are evaluated and braking distances are simulated under varying friction coefficients and slopes. This study quantifies the relationship between detection distance and safe braking performance, establishing objective metrics for the dynamic Operational Design Domain of AVs. Furthermore, an adaptive speed control strategy based on reinforcement learning — implemented via the Soft Actor-Critic algorithm — is proposed, which continuously adjusts vehicle speed to ensure that braking distances remain within sensor detection limits. Experimental results demonstrate that models trained with extreme weather data significantly outperform general-purpose detectors and that integrating real-time braking distance predictions into the control strategy improves safety margins and operational efficiency across diverse driving scenarios.

Recent studies have highlighted the significant impact of built environments (BE) in residential neighbourhoods on well-being, focusing on correlations between micro-scale BE features—such as trees, grass, fences, and bikes—and specific well-being aspects like physical health, social interaction, or perceptions of safety. However, these studies often fail to comprehensively explore how these features influence residential preferences or provide clear design guidance. To address this gap, examining residential location choice (RLC) datasets can offer valuable insights into how various micro-scale BE features affect the attractiveness of residential neighbourhoods and residents' well-being. This thesis proposes a semantic Computer Vision-enriched Discrete Choice Model (CV-DCM), which uses a panoptic segmentation model to quantify micro-scale BE features and integrates with traditional discrete choice models.

A manual evaluation of 400 masks generated by the semantic computer vision model ensured accurate quantification of BE features, enhancing choice modelling interpretation. The choice modelling results highlight the specific impacts of various micro-scale BE features on RLC, providing valuable insights for urban planners. For instance, restricting unattractive features like motorcycles in residential neighbourhoods and planting or maintaining more trees are the most attractive among vegetation. By pinpointing the most influential elements, the study facilitates the cost-effective restructuring of residential neighbourhoods to boost their attractiveness and enhance residents' well-being.

To ensure transport models are effective tools for planning, they must not only adequately explain current travel choices but also maintain predictive accuracy over forecast horizons while being computationally feasible for practical use. One simple approach to achieving this is by including behavioral theories in transport models to improve the behavioral representation of models.
One such theory is Spatial Competition and Agglomeration Effects (SC&AE). This theory examines how opportunities present nearby influence the attractiveness of a destination. This influence can be either positive (Agglomeration) or negative (Spatial Competition). Although SC&AE is widely recognized in the literature for enhancing the explanatory power of computationally simple Multinomial Logit (MNL) destination choice models, its impact on the temporal transferability of these models remains unexplored.
This study assesses the impact of SC&AE on MNL destination choice models for home-based maintenance, work, and education trips in the Metropolitan Region of Amsterdam on a 5-year short-term forecast horizon (2018-2022) using Dutch National Travel Survey Data (ODiN). The findings indicate that SC&AE has a positive but limited effect on the temporal transferability of these models. This impact decreases with decreasing traveler autonomy and ease of switching to alternate destinations. In terms of percentage improvement, Transfer index metric shows an exaggerated impact (35.2% - 286.36%) while the other performance indicators, such as Fitting Factor (1.1% 3.6%) and % Correct predictions (0.14%-2.05%) confirm that the impact is limited.

Amsterdam's extensive network of bridges and quay walls is facing a critical maintenance backlog due to structural underfunded infrastructure maintenance. The many upcoming bridge maintenance projects to ensure safety are expected to cause significant more hindrance to road users. However, complex road networks with high traffic density in urban areas like Amsterdam make it difficult to foresee how the effects of different maintenance projects interact and how the projects can be planned efficiently to reduce hindrance.
This study aims to optimise the planning of a given set of bridge maintenance projects to reduce hindrance to road users, combining a genetic algorithm with traffic simulations using a new estimation method to improve the assessment of simultaneous closures. The genetic algorithm selects the optimal starting time and execution duration for each bridge while minimising maintenance cost and additional travel times for the modalities car, freight and bicycle. Using the fast static traffic simulation of the Urban Strategy Tool of TNO, a new estimation method for evaluating simultaneous closures is proposed, accounting for interdependencies between bridges. Results of a case study on four urban bridges in Amsterdam show that the new method provides more accurate estimates of additional travel time compared to previous methods and generates better solutions. Moreover, the fast proposed optimisation framework makes it possible to evaluate multiple scenarios in reasonable time to assist in decision making of maintenance planning.

The growing demand for parcel deliveries is causing significant problems for freight logistics, particularly in cities. The increasing number of delivery vehicles leads to traffic congestion, high emissions, and rising costs. To address these problems, new and sustainable delivery methods must be implemented for
the parcel last-mile. However, estimating the impact of a different logistics system is complex, as it depends on consumer adoption of these new delivery methods.
This paper presents a new simulation model that captures and explores the interconnections between multiple delivery methods and consumer preferences for those delivery methods. Consumers’ reaction to the performance and availability of the delivery methods is simulated, next to knowledge progress via
word of mouth and familiarisation. The developed hybrid model uses system dynamics to simulate the evolution of consumer preferences and the last-mile delivery via van, self-collection, crowdshipping and drone delivery at an aggregate level, while an agent-based model provides input on the operational performance of the delivery methods. With this structure, consecutive
interactions can be simulated, and by that, data on consumer preferences and the delivery method operations can be obtained at multiple time points.
Results of a case study on the province of South Holland show that consumers change their preferences due to the introduction of new delivery methods. However, the vehicle kilometres and emissions of van delivery do not reduce at the same rate as the demand. The study also highlights zonal differences in last-mile services and the importance of delivery quality to consumers.

Nowadays, electric vehicles (EVs) are becoming one of the most popular traffic modes in urban mobility system. As a new mode, it has its unique characteristics but also have many features in common with conventional transportation tools. One main advantage of the EV is that it uses the clean energy as the power of driving, which can significantly improve the sustainability of the current mobility system. Therefore, this new traffic mode has been widely and strongly supported by all sectors of society. Many governments around the world even plan to totally forbid the sale of conventional fuel vehicles to promote people’s acceptance towards the EV. However, a critical obstacle to continued progress in this technology is the range anxiety and the inconvenience of the current charging schemes, which to a great extent, limit the flexibility of drivers’ trip scheduling. In the case of this anxiety, the concept of Battery Swapping Station (BSS) becomes popular at present. This infrastructure is expected to provide battery swapping services to the EV like a fuel station, which can greatly eliminate the range anxiety and enhance the flexibility of trip scheduling for EV drivers.
This thesis aims to make the construction planning for this future infrastructure. To be specific, it is to find the optimal spatial distribution of BSSs in the urban traffic network. For solving this type of problem, there have already been many studies that developed optimization models with objectives such as minimizing the overall construction and operation costs of the system or maximizing the overall operational profits. There are also a few studies take the routing problem into consideration as well. However, another important stakeholder in this problem is completely missed in existing research, that is the urban resident. Urban residents now are becoming more and more concerned about the living environments in the city, so they should not be neglected when trying to solve the locating problem of BSSs because the construction and operation of this infrastructure will have a significant impact on urban livability. On the basis of this consideration, this research develops a model that simultaneously calculates the interests of three stakeholders in the optimization, which are system investors, urban residents, and system users (EV drivers) so that a more balanced solution to this optimal locating problem can be obtained, which is more realistic and practical in the future.
The developed model is applied to a real traffic network in this research. Delft City in The Netherlands is chosen to be the experiment site. In general, four types of concerns are tested in this study. The first concern is about the uncertainty of the optimal solution brought by the randomness of EV’s initial SOC. Since there are not mature statistics on collecting EV’s initial SOC distribution at present, a probability density function is used to randomly generate initial SOC for vehicles during experimenting. In this case, it is possible that the optimal locations can be quite uncertain in the city under different draws. Therefore, this uncertainty is specifically tested first. Then the second concern is about the future development of EV-related technologies. To be specific, the optimization is conducted under different maximum traveling ranges of the EV to see what will be the case if the traveling range of the EV becomes wider and wider in the future. The third concern is an optimization strategy that might be applied by the decision-maker to ensure a good living environment in the residential area in the city, so a station size limit is set in this area to see how the optimal solution will change under this circumstance. The last concern is about some possible optimization preferences of the decision-maker, for example, the preference for a better travel time in the traffic network, fewer stations in the residential area, less economic expenditure on station construction. The impacts of these three preferences are examined in this study.
A series of performance indicators are designed to comprehensively reflect the impact of a certain concern on the optimization. And it is found that each of the concern will significantly influence the optimal decision-making in this problem. Much information can be inferred from experiment results, which will be specifically exhibited and explained in later parts of this thesis.
At the end of this research, the limitations of the developed model are analyzed in detail, and recommendations for future research on this topic is formulated based on these limitations.

Electric vehicles (EVs) take advantage of reducing fossil-based environmental pollution and developing a more sustainable logistics network. Compared with internal combustion engine vehicles (ICEVs), EVs have new technical characteristics like limited battery capacity and long charging time at charging stations. With the diffusion of EVs, vehicle routing problems (VRPs) of EVs draw transportation service providers’ attention, which extends VRPs with intra-route charging considerations. In real-life practice, the punctuality of preplanned
vehicle routing may be affected by uncertain travel time caused by traffic congestion, which derives undesirable penalty costs for violating time windows. Besides, long intra-route charging time at charging stations presents an even greater challenge to trade-off between completing tours with enough electricity and providing delivery service on time. This assignment aims to investigate the impact of travel time uncertainty and electric vehicle characteristics on planning fleet size, vehicle routing, and charging schedules. A pickup and delivery
problem of electric vehicles is studied in this assignment, which considered flexible fleet size, partial charging policy, soft time windows and uncertain travel time. The problem is formulated as a two-stage stochastic linear programming model. The fleet size, vehicle routing, and charging decisions are determined in the first stage. After the realization of travel times, the second stage determines specific charging times at charging stations. The objective is to minimize the total operational cost, which consists of travel costs of vehicle usage and charging,
and the expected penalty cost of earliness, delay and overtime. The sample average approximation method is applied to model the stochastic programming to the deterministic equivalent and the Gurobi Optimizer is used to solve this mixed-integer programming. A computational experiment is conducted based on a small data set with one depot, 9 pickup and delivery requests, 5 charging stations at service vertices and 3 available electric trucks. The travel times along the delivery tour are assumed to follow Gamma distribution. To investigate the trade-off between travel costs and penalty costs, 12 instances are conducted in the experiment by tuning parameters of time windows, the uncertainty of travel time, and the importance of punctuality. The experiment results showed an increasing vehicle fleet size could improve the level of service but also derive more vehicle
usage costs. Besides, the intra-route charging operations impact vehicles’ departure times at each charging station, which subsequently impacts service start time at customer vertices. Longer intra-route charging time has the potential to enhance punctuality in instances with tight time windows or congested traffic conditions. Moreover, different operator inclination leads to different fleet size and charging time preference. Instances attaching more importance to punctuality have a larger fleet size and longer intra-route charging time.

In light of growing environmental challenges, the need to reconsider how we approach personal transportation is becoming increasingly evident. A shift from a private car-focused mobility system towards a more sustainable and equitable transportation system is desired. Car sharing is considered a means to achieve this. However, car sharing use and its related impact are not entirely understood, as many studies do not consider the car sharing motives of individuals and treat the car sharing population as a homogeneous group. This study aims to reveal distinct car sharing usage profiles to gain a thorough understanding of car sharing utilisation and its related impact. Six distinct user profiles are uncovered using a Latent Class Cluster Analysis (LCCA) based on station-based car sharer data gathered through an online survey (N=1281). This segmentation underscores the diversity in car sharing motives and reveals that the impacts associated with car sharing use differ across the identified user groups. The cluster profiles show that environmentally motivated car sharers use the shared car as a complete replacement for their private car, causing a substantial decrease in car ownership and use. More utility-focused car sharers, and especially initially carless individuals, show a less substantial decrease in car ownership and sometimes even an increase in car use. This study concludes with recommendations based on these findings and discusses its limitations.

This PhD thesis delves into the intricate realm of supply-side behavioural dynamics and operations within the ride-sourcing system. It investigates drivers' pivotal decisions, focusing on ride acceptance and relocation choices at the operational level. By leveraging a unique dataset, novel theories on ride-sourcing drivers’ behaviour are developed and thoughtfully integrated into a system operation model, shedding light on their far-reaching impacts on the overall ride-sourcing system performance.

Automated Mobility-on-Demand (AMoD) systems are expected to revolutionize urban mobility systems. However, there are uncertainties in the planning and operations of AMoD systems. We deem the agent-based approach as being well suited for modeling new phenomena in future AMoD systems and therefore shed some light on the uncertainties about the operation and the impacts of such systems. Recommendations to various stakeholders are provided through the different contributions.

The performance of multiple AMoD operators under congestion pricing strategies remains unexplored. We propose two congestion pricing strategies: Link-based congestion pricing strategy and Delay-based congestion pricing strategy to regulate AMoD services. We aim to demonstrate the effectiveness of such strategies using an agent-based modeling framework in a case study of the city of Hague, the Netherlands. Simulation results suggest that congestion pricing strategies could effectively reduce congestion, and congestion could be significantly reduced even if pricing strategies are in place in a limited area. Moreover, we found that the delay-based pricing scheme is more flexible and more capable of reducing congestion. It is recommended that some road sections may be tolled to reduce delays. Passengers, however, will have to accept higher fares because of the additional congestion fee (as we hypothesized), and the impact needs to be further investigated in future research.

To tackle challenges such as climate change, air pollution, traffic accidents, or the lack of space in cities, our urban transportation systems must become sustainable, emission-free, safer, and more efficient. The introduction of shared mobility is seen as a critical component in facilitating a mobility transition in large cities, as shared mobility can promote multimodal travel behavior, leading to reduced ownership and usage of the private car.
Shared mobility requires charging solutions and parking space within the existing urban fabric. Additionally, it must have digital and physical integration into existing transportation systems. These requirements should be jointly addressed within the concept of shared mobility hubs. Recently, cities have moved from pilot testing standalone hubs to the scaling of city-wide hub networks. Planning the locations of these hubs remains a challenge for cities trying to optimize their distribution.
This research suggests an improved location planning method for shared mobility hubs, combining Multiple Criteria Decision Analysis (MCDA) and Network Analysis. Different prioritizations at the municipal decision-making level can be translated into placement strategies through MCDA. If necessary, multiple stakeholders can also be involved through a Multi-actor Multi-criteria Analysis (MAMCA). The resulting MCDA score for each spatial unit converts a multivariate problem into a single variable location-optimization problem. Utilizing single variable location-optimization tools, such as ArcGIS location allocation, specific location suggestions can be computed along with their respective catchment areas. This also allows a comparison of different placement strategies based on city-wide Key Performance Indicators (KPIs). In turn, decision-makers are enabled to compare different placement strategies in terms of their potential impacts on their objectives. This method holds the potential to accelerate micro-planning processes with defined target scenarios and data-based insights per hub location.
The improved method is developed upon the case study of Munich, considering various location planning objectives. 600 locations composed of 3 hub types are suggested in order to achieve citywide accessibility within 5 minutes of walking time. 1000 locations of an additional hub type are suggested to achieve city-wide accessibility within 3 minutes of walking time.

Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned problems by contributing to a more sustainable and equitable transport system. This thesis defines mobility hubs as places where shared cars, mopeds, and e-bikes are offered to improve connectivity and ameliorate mobility options. Given a limited budget, cities would like to optimize the locations of mobility hubs to maximize benefits. This problem is solved in this thesis by presenting an optimization model that allows the distribution of mobility hubs and allocation of shared cars, mopeds, and e-bikes to maximize social welfare. The algorithm can provide the optimal locations for the hubs and their respective capacity in terms of vehicles, while accounting for multimodal trips. It focuses on maximizing the utility of the population rather than the operators’ profits. The model is divided into several modules: computational modules that calculate the number of people that would like to use a mobility hub; a mathematical optimization module to optimize the capacity, availability, and relocation of shared vehicles; and finally, a genetic algorithm that performs several iterations to find the optimal distribution of hubs. The model developed is applied in a case study for the city of Amsterdam. Several scenarios are performed to assess how the distribution of hubs varies depending on the budget provided to construct them. The results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer with more vehicles. Areas with higher population density are prioritized when lower budgets are invested in building the hubs. Additionally, the shift towards shared modes and the travel time savings are minimal. The benefits increase considerably when investments lead to complete coverage of the area by the network of mobility hubs. A modal split of 5% for the shared modes is expected when Amsterdam is fully covered by 288 hubs. From an environmental point of view, only 32 % of the shared trips replace trips previously made by car, leading to a limited CO2 emissions reduction of 1.27%. To conclude, the model developed is one of the first models that optimizes the location and capacity of multimodal hubs to maximize social welfare by considering multimodal trips. It has the ability to quantify the benefits of introducing mobility hubs depending on the investments made.

Installing wireless sensors on a network of waste containers, that monitor at regular intervals their waste fill levels and transmit the data to the cloud of a waste management operator over the internet, describes the Internet of Things (IoT). With this constant stream of data, the dynamic organization of waste collection schedules is enabled as containers are visited for collection only when it is necessary, which consequently leads to demand-responsive services. Domain experts attest that operating a demand-responsive service brings financial and environmental gains to a waste collection service, but it simultaneously introduces complete variability in the system which is undesirable in real-life operations. To solve this problem and consequently optimize the waste collection service’s performance, domain experts stress the need for a balanced trade-off between dispatch consistency and flexibility. This means, being able to exploit to the highest degree possible the benefits of demand-responsive operations, while also maintaining a certain level of dispatch consistency when demand varies from day to day. This research focused on developing a solution approach to solve the IoT-based waste collection problem, which is derived from the knowledge and requirements of the domain. The use of the knowledge of the domain is significant as it ensures that the model is tailored and applicable to a real-life IoT-based waste collection service. The overarching objective of the approach is to maintain dispatch consistency and flexibility when the containers’ location and demands vary from day to day, as well as to attain an economically and environmentally enhanced waste collection performance.

Proposed an MILP mathematical model to optimize the total profits for a EV sharing system considering dynamic usage price and V2G technology.

The outbreak of the COVID-19 pandemic and the resulting corona crisis has had a profound impact on mobility in the Netherlands and in the entire world. This study aims at predicting, modeling, and analyzing changes in the activity-travel behavior of individuals in the Rotterdam-The Hague Metropolitan Area, the Netherlands, during different stages of the COVID-19 pandemic and assess their effects on mobility. This is done by using the activity-based travel modeling approach. A modeling framework is developed and used for this study. The modeling framework provides an innovative approach to study the impacts of changes in activity-travel behavior caused by emergency situations such as the COVID-19 pandemic in a disaggregated manner. It combines the outputs of ABMs and a mix of aggregated and disaggregated data of changes in in-home and out-home activity frequencies and re-estimates the daily schedule of individuals considering factors and attributes that were not considered before, such as the weekly frequency at which agents do different activities and the sector where agents work. With the outcomes of the case study simulations, insights about the effects on mobility were identified and compared to real data counts and results of other studies. The conclusion is that the vast majority of the model outcomes proved to be in line with the results of other sources, and the modeling framework can be a useful tool to study the changes in activity-travel behavior of individuals during emergency situations and their effects on mobility.

Cities are growing, leading to challenges to keep cities sustainable, liveable and accessible. In Europe, passenger mobility is focused on personal car usage, making parking availability an essential and challenging subject in development projects. The required parking capacity is determined based on parking standards, indicating the minimum number of spots developers need to construct. High parking standards result in an oversupply of parking places in urban areas. Literature advocates to implement context-specific parking standards that depend on the local conditions of the residential area. However, these studies neglect the visitor parking standards while in practice an oversupply is often observed. This research aims to identify factors that explain the visitor parking demand and what this means for the visitor parking standards. The developed conceptual framework showed that visitor parking demand depends on the demographic characteristics of the visitor, the geographic and policy factors at the residential area of the visitor and the demographic, geographic and policy characteristics at the residential area of the host. The usage of visitor parking permits in Eindhoven was used as a proxy for visitor parking demand to estimate a multiple regression model. The results showed that age, population density, distance to the city centre, housing characteristics, accessibility to public transport, and parking supply at the residential area of the host influence visitor parking demand significantly. Characteristics of the visitor and policy factors at the parking area could not be considered due to lacking data. The results show that visitor parking standards could be determined based on the local conditions of the parking area rather than as a fixed standard applied in all development projects.

As a solution to the high greenhouse gas emissions and declining quality of life caused by the private vehicle, the mobility hub is introduced. The mobility hub is a place where multiple modalities come together, including public transport and shared mobility. As the mobility hub is a relatively new innovation, limited research is available on the topic, especially on finding potentially suitable locations for a mobility hub. In this research, the knowledge gap on the potential of locations for a mobility hub is addressed by developing a globally applicable methodology to determine the potential of locations for a specific mobility hub type: the regional mobility hub. The regional mobility hub is located outside of the city center, and for this type of hub, an area potential determination methodology has the highest added value. The developed methodology is a combination of the GIS-Multi-Criteria Analysis and Multi-Actor Multi-Criteria Analysis available in the literature, leading to all areas in the region being evaluated for their regional mobility hub potential, taking the end user, operator and government perspective into account. The result is presented in multiple heat maps based on scenarios with varying stakeholder influences. The methodology consists of 5 criteria (Potential demand, Costs, Generalized travel costs, Link to surroundings and Impact) measured by 9 attributes to incorporate all relevant influential factors found in the literature. The Analytic Hierarchy Process (AHP) is used to determine the criteria weights. The developed methodology is applied to the region of Rotterdam, to find out whether the methodology works as expected. From multiple analyzes, it appears that the methodology is suitable for tackling the location potential determination problem, as it works logically. In future research, the focus should be on extending the methodology to incorporate freight transportation. Besides, the methodology can be improved by incorporating revealed preferences of shared mobility users. This information can be collected by analyzing the usage of existing shared mobility systems.