Urban planning in the Global North has historically prioritised car infrastructure, leading to car-dependent cities with congestion and pollution. In recent years, especially in north-western Europe, there has been a policy shift toward healthier and more sustainable urban environments, with strategies such as reducing parking capacity gaining attention. While this measure can discourage car use and free up urban space for alternative purposes, its city-wide impacts—particularly on public transport and cycling—remain underexplored.

This thesis investigates how large-scale parking space removal affects travel behaviour in Rotterdam, a city with relatively high car use but suitable conditions for car-lite policies. Three intervention scenarios (20%, 40%, and 60% parking reductions) are analysed using the V-MRDH macroscopic, multimodal transport model. To address the model's limitations, a supplementary estimation method was developed to more realistically approximate shifts in transport demand.

Results show that a 20% reduction could lead to a peak-hour modal shift of up to 2,566 motorists, corresponding to increases of 2.8% in public transport and 2.2% in cycling. Although average loads remain within capacity, some tram and bus lines face overcrowding during the busiest 15 minutes. The findings highlight the need for strategic public transport planning and model enhancements to accurately assess behavioural responses to parking policy changes.

Governments worldwide are planning car-free policies to increase the livability of cities. However, car-free policies often face opposition from local stakeholders, and policy-makers often lack relevant model indicators to represent all stakeholder interests in their ex-ante car-free policy evaluations. Well-being (Dutch: Brede Welvaart) indicators can be more relevant for the various stakeholder interests in car-free policies by covering aspects in the domains of living environment, accessibility, safety and health. This thesis investigates how relevant well-being indicators can be selected and operationalised for the ex-ante evaluation of car-free policies. A well-being indicator selection process was developed and applied to a case study in the Oude Westen neighbourhood in Rotterdam. This process involved conducting thirteen semi-structured stakeholder interviews. The resulting stakeholder interests were analysed to determine the relevant aspects of well-being and the relevant indicators for these aspects. From the shortlist of relevant indicators, three indicators were selected and operationalised in this thesis. At last, two validation interviews were conducted to validate the results of this case study and the developed indicator selection process.

The most relevant stakeholders - municipality, local residents, and local business owners - were selected to be involved in the well-being indicator selection process in the case study. Based on their interests, the most relevant aspects of well-being were found to be the use of space, accessibility of mobility options, accessibility of activities, traffic safety, and noise pollution. This resulted in the operationalisation of three indicators: the use of space for car parking, the number of mobility options, and the traffic mix safety warning.

While the small sample size does not allow for generalisation of the case study results, the results of this thesis do show that the well-being indicator selection process can yield relevant indicators of well-being and that this process can be applied in practice. The operationalised indicators can be relevant to represent stakeholder interests in car-free policy evaluation and can be applied to other cases as well. However, the relevance of the indicators does depend on the type of policy and the stage of the planning process. Using well-being indicators in policy evaluation can enhance the relatability of the evaluation results for stakeholders and potentially increase stakeholder support for policies.

This dissertation discusses how mode and route choice behavior might change with the introduction of future transportation modes when potential users are unfamiliar with such systems. It uses discrete choice models and supernetwork models without mode-specific constants and parameters with revealed preference data of current modes to understand how future modes could impact mobility effects such as mode choice, travel times, and resistance.

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.

- There is increasing attention in the Netherlands in the topic of transport
poverty and accessibility, with several publications discussing the need for accessibility standards to indicate injustice in the transportation system. Numerous case studies can be found where accessibility has been measured and assessed for fairness in the transport system, assuming that low-income households rely on public transport. This research reveals that up to now, the accessibility by public transport for low-income households and the unemployed is overestimated. Transport costs do not only have a diminishing effect on the accessibility by car, but also limits the accessibility by public transport. By means of the methodology ’Designing fair transportation systems’ it was possible to evaluate the job accessibility in the Parkstad region, a region where income on average is the lowest in the Netherlands and the unemployment rates the highest. The limited job accessibility by both car and public transport raises the question to what extent transport poverty contributes to the high unemployment rates in this region. Municipalities are recommended to use these results to further explore what the population groups suffering from transport poverty need and propose interventions to improve job accessibility for those who need this the most.

At present, many studies have begun to focus on evaluating urban accessibility and livability, which can be a series of great guidance for future urban planning considering residents’ personal perceptions of various living aspects. It can be observed from the existing literature that urban accessibility has a mature evaluation system, considering travel distance, trip purpose, travel time, individual’s perceived utility, and other constraints. However, the existing studies review urban livability from many very different directions, and no consolidated concept is proposed or concluded. This makes the evaluation of urban livability hardly provide a comprehensive understanding of the concept of urban livability. In this case, this additional research aims to propose a general framework describing the urban livability concept and its connotation. Besides, several indicators shall be selected from this framework to be quantified and calculated so that there could be a specific quantitative method for evaluating urban livability based on this framework.

Nowadays, limited bike parking spaces have been a severe problem to be solved in the Netherlands. Shared bikes are considered ideal solutions, as well as other emerging shared micromobility modes, e.g., shared e-bikes, e-scooters and e-mopeds, since they can help utilise vehicles better and alleviate more occupying land space. Moreover, shared micromobility modes have a great potential to attract private car users and achieve a modal switch from private cars to them. In this case, this research aims to understand how the sum of parking areas changed by the modal switch from private cars and bikes to shared micromobility modes. A calculation approach of parking areas will be proposed and applied in an agent-based simulation model mimicking the mode choice of travellers in Delft. Moreover, shared micromobility modes regarding vehicle characteristics (speed and cost) will be differentiated, so their impacts on mode choice and land space will also be investigated. Additionally, this study made a sensitivity analysis of coefficients of utility functions to test the reliability and validity of the results.

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.

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.

This master thesis explores the potential influences of the COVID-19 pandemic on travel behavior change, and projects potential implications of long term travel behavior changes due to the COVID-19 pandemic on accessibility and the allocation of public space in the city of Amsterdam, the Netherlands for the year 2030. The first result of this thesis consists of the conceptualization of direct effects of the COVID-19 pandemic and policy measures on travel and activity patterns within a theoretical framework, based on travel behavior change theories. Reasoning from this framework it can be concluded that post-COVID-19 three general direct effects of the COVID-19 pandemic and policy measures on travel and activity patterns may be expected: (1) a shift from onsite to online work-related activities, (2) the respacing and retiming of work-related travel patterns and (3) a modal shift towards active modes of transport such as walking and cycling. The second result of this thesis are the creation of four exploratory post-COVID-19 travel behavior scenarios, based on participatory intuitive logic scenario planning methodology following an adaptation of the standard approach, including a novel scenario switchboard. The third and final result of this thesis are simulation results which project congestion rates and travel times by car, and the modal split within the city of Amsterdam in the year 2030 using a tour-based travel model. Based on the results as calculated within the travel model of the four post-COVID-19 travel behavior scenarios, it can be concluded that reduced work-related activities and its consequential changes to work-related travel patterns: (1) strongly alleviates congestion rates during the morning and evening rush hours, (2) decreases travel time from the city center of Amsterdam to certain other zones in Amsterdam with 1 to 2 minutes per person per trip, and (3) provides arguments to allocate more public space towards cycling infrastructure, especially within the district of Nieuw West.

Limited quantitative research exists on the effects of individual traffic management on four liveability aspects: travel time, air quality, noise, and accident risk. The aim of this research is to get an insight in how these effects on liveability can be improved using ITM, given road users willingness to comply with given advice. Literature research is used to provide an insight in factors influencing compliance. Furthermore, an optimisation model in Excel comprising LTM, individual route guidance, and effect estimation is built. The model is tested across three cases and multiple scenarios varying in compliance rate, objective, and distribution of unguided traffic. The achievability of effects is complex: depending on network haracteristics and compliance assumed different impacts can be found. In general, the best results are obtained with advice aiming at a more balanced distribution of traffic. However, the weight attached to local residents is of strong influence on the optimal distribution. An important limitation of this research is that the optimisation model only considers effects in upcoming time step, causing extreme advice with negative effects on liveability. For future research it is therefore important to consider the effects in multiple time steps ahead.

Service reliability is one of the most important performance measures to public transport users. Detecting disruptions helps to measure service reliability, which can be used by public transport operators to improve this reliability. In this thesis, a methodology is described to automatically detect disruptions offline, using smart card data. The day-to-day regularity of delays is investigated using hierarchical clustering on a training set, to distinguish between regular and irregular delays. The clustering result is used to create a probabilistic classifier. This classifier is applied to the test set to find days that do not correspond to a regular pattern: irregular days. After that, disruptions are detected within the irregular days. The outcomes of this study can be applied in multiple ways. Locations where disruptions have occurred can be found and the related passenger delay can be calculated. This can help public transport operators to prioritise which locations to focus on to reduce passenger delays. Furthermore, not only public transport networks, but also other networks can benefit from the outcomes of this study. Speed data of road networks could be used to find disruptions that are caused by accidents, instead of regular traffic jams. On top of that, this study could be used as a step towards real-time disruption detection, for both public transport and road networks.

Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure because it was found to be correlated to the operational performance, the total ridership, consumer experience, passenger flow distribution, and network resilience. A handful number of past studies characterized and compare PTNs structure, but little is known about broader or general classification worldwide. This study examined how PTNs can be clustered into groups when considering multiple features. Centralization, accessibility, robustness, service connectivity and directness are five main considered network features used in previous studies to analyze the network structure. K-means, hierarchical clustering and principal component analysis were performed to identify the cluster of PTNs defined from those five features. To illustrate the method, we conducted a case study of 20 real-life rail-bound networks worldwide generated by the up-to-date general transit feed specification (GTFS) data. As a result, we were able to identify four main meaningful clusters: tram, tram-related, metro and tram and mixed modes. Although modes of transportation and the size of the network were not parts of features, they heavily influence the clusters. The proposed method show automatic and reproducible tools to empirically identify topological patterns of PTNs.

This research examined the effect of an improved road network on the factors economic growth, road safety, emissions, education, health, employment and tourism in developing countries. A case study is conducted on the country Namibia in Southern Africa. A System Dynamics (SD) model is developed to assess the effects of future scenarios in terms of road infrastructure on the mentioned factors. The SD model is based on a literature study on developing countries in general and a data analysis on Namibia in specific. The identified scenarios are (i) Namibia becoming the logistics hub of Southern Africa by 2022 and (ii) enhance tourism by improving road infrastructure between the major tourism attractions. Both scenarios lead to improvements in health, tourism and employment. The primary school enrolment rates remained the same over time for both scenarios. Road safety increases for both scenarios due to upgrades to bitumen, but the extent to which it increases depends on the extra measures that are taken with regards to road safety. It can be increased more by campaigns or more controls. Emissions go up in both scenarios, because emissions are a direct result of constructing and upgrading roads. The tourism scenario attracts more tourists because the roads between the major attractions are upgraded to bitumen. Both scenarios are realistic and might happen in the future. The scenarios can and might be combined.

In the past few years, the research and development of automated driving have made significant leaps in bringing theory into reality. Forecasts suggest that highly automated vehicles (SAE Level 4) may be on the streets and highways within the next decade. However, current road infrastructure is designed for human drivers, and may not be able to deal with the integration of highly automated vehicles. Therefore, whether to upgrade the infrastructure or not, and how to do so has become a key issue for the deployment of automated vehicles. To cope with the wide range of uncertainties involved, this study uses two of the main driving forces, namely, public expenditure and collaboration level among stakeholders to develop plausible scenarios in order to explore feasible infrastructure requirements for various future scenarios. Two rounds of interviews with manufacturers, road operators and academicians were conducted. The first round aimed at specifying potential requirements and the second one aimed at mapping requirements into the most likely scenarios. Under all plausible scenarios, clear and harmonized road signs and lane markings, High-Definition (HD) maps and Vehicle-to-Infrastructure (V2I) communication technology are found to be key requirements while dedicated lanes can only be considered under the advanced scenario (high public expenditure and high collaboration level among stakeholders). There will be a transition from physical infrastructure to digital infrastructure since the opening of public roads for automated vehicles, but physical infrastructure requirements should never be ignored before this transition is finished. Results provide guidelines to prepare roads for highly automated vehicles.

Studies have shown that car-sharing has a lot of potential. Because of this, municipalities require a tool to evaluate the effects of car-sharing. This research has developed a mode choice model that includes a car-sharing alternative considering an activity-based framework, for the city of Rotterdam.