One of the challenges municipalities face when trying to prevent parking problems is the difficulty of predicting the effect possible policy implementations will have on the parking occupancies. Therefore, a conceptual model was developed that uses the interactions between parking supply, demand, and choice behaviour to forecast the parking occupancy. In this research, this model is operationalised using existing data and techniques to form an operational model framework. The framework is tested and validated using a case study on the Dutch municipality of The Hague. While there are many ways the model can still be improved and further developed, the framework developed in this thesis shows that the underlying concept is able to accurately describe the balance between parking supply and demand and, from that, predict the resulting parking occupancies. It combines several existing techniques and data sources with some new techniques, providing an initial step to a full parking occupancy model.

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.

Summary
Cities worldwide face the dual challenge of increasing density while developing sustainably. These pressures strain transportation systems, necessitating optimal performance. In the Netherlands, transport policy focuses on three main objectives: accessibility, liveability, and safety. This thesis addresses accessibility and liveability.
Liveability concerns the fit between residents and their living environment. This fit is an ecological relationship involving interactions with the built, natural, and social environments. The thesis explores two approaches to operationalising this ecological fit: static and dynamic. The static approach, exemplified by tools like the Leefbaarometer, views liveability as a measurable outcome. It considers the valuation of the environment as a proxy for fit, obtained through surveys or hedonic pricing methods. This approach is widely used in current transport policy evaluation but is limited in its ability to do ex-ante analysis.
On the other hand, the dynamic approach views liveability as an ongoing process. It considers fit the difference between what a resident expects to observe and experiences. This approach uses generative modelling, which involves causal probabilistic relationships. The manner in which these generative models are updated is unique, attempting to capture the dynamics of the reciprocal relationship between the resident and the living environment. The resident embodies the generative model, containing both internal and external states. The generative model is definitive of a resident's niche. Construction of the niche is afforded by accessibility.

Objective
The thesis aims to work towards future operationalisation of the dynamic approach to liveability. While full implementation is far beyond the scope, the objective is to understand the role of representations in defining and operationalising liveability. In the context of transport systems, representations can be understood as simplified and compressed digital twins of complicated urban environments. These representations can be used for decision-making in infrastructure planning and policy formulation.
The dynamic approach to liveability relies on the action-perception loop as its generative model. This loop means that perception is anticipated, an action in itself. Selecting optimal actions is a matter of maximising epistemic and pragmatic value. Writing a thesis is an action; hence, we follow the formal decomposition of action selection:
• Epistemic component: develop a theoretical framework, exploring the action-perception loop in liveability and the role of representations within this loop. Bridging cognitive science and transport modelling enables novel machine learning applications.

• Pragmatic component: automate the operationalisation of liveability by applying urban representation learning techniques to the province of South Holland. Our novel contribution is the developed mathematical model based on spatial convolutions. The study uses various data sources relevant to transport planning, including road networks, public transport schedules, aerial and street view images, and points of interest.

Modelling Study
Addressing the pragmatic component, we develop a novel urban representation learning technique using the H3 hierarchical hexagonal discrete global grid system (DGGS). Neural networks are used to extract representations such that each discrete hexagon is assigned a coordinate in the metric representation space. Features from different data sources are extracted using individual encoder networks. These features are then combined using a late-fusion network to obtain the final representations. We develop Ring Aggregation, a mathematical model to fuse multiple features while accounting for spatial context through sampling heuristics and spatial convolutions. The methodology considers insights from the dynamic approach to liveability, like landscapes of affordances, analogous to location-based accessibility. No operationalisation of the dynamic approach is developed in this study. Instead, this study applies the static approach and evaluates urban representations using multiple univariate linear regressions with Leefbaarometer scores as targets.

Key Epistemic Observations
• The action-perception loop is central to the dynamic approach to liveability but absent in the static approach.
• Representations play an instrumental role in the action-perception loop, informing action selection.

Key Pragmatic Observations
• Regarding sampling heuristics used to train the late-fusion network, Euclidean distance and location-based accessibility perform similarly, better preserving urban area integrity.
• The configuration of Ring Aggregation significantly impacts urban representation quality, showing heterogeneity between Leefbaarometer scores. Socially oriented scores can perform just as well with smaller receptive fields and steeper weighted average functions like exponential.
• Different data sources excel in predicting various aspects of liveability, suggesting the importance of integrated data approaches in transport policy.
• Compared to urban2vec and M3G studies, Ring Aggregation outperforms them in predicting Leefbaarometer scores across the board.

Looking Forward
By reframing the roles of indicators, perception and preferences, this thesis concludes that static and dynamic approaches to liveability are complementary. Complementarity implies that the static approach to liveability can be used to bootstrap the development of the dynamic approach. While 'living' digital twins operationalising the dynamic approach do not yet exist, this study provides building blocks towards their development. Future directions for research and application in transport policy centre on the hypothesis that hierarchical active inference models are already transport models, just waiting to be used.

The National Model System (Landelijk Model Systeem [LMS]) is used to model and predict travel behaviour for the whole of the Netherlands. The LMS is a multimodal model that makes predictions for the main road and rail network of the Netherlands and is an important tool for policy making. One of the inputs of the LMS is data based on yearly travel surveys (Onderzoek Verplaatsingen in Nederland [OViN]).

The work in this thesis shows that the LMS is currently unable to accurately capture the effect of the differences in spatial environment effectively. The LMS uses the Degree of Urbanisation (DU) to model the spatial environment, which proves insufficient to correctly model travel behaviour.

This conclusion was reached by using a cluster analysis and propensity score matching and by comparing the ‘real’ modal split (OViN) with the ‘predicted’ modal split (LMS). All zones in the Netherlands were clustered based on characteristics of the spatial environment. This was done using the so-called D-variables.

The propensity score matching showed that the differences in modal split between the different DUs and the different clusters are caused by both demographic characteristics and differences in the spatial environment. However, the effect of the spatial environment is larger.

Policy makers and other users of the LMS should be aware that testing policies or future scenarios in the LMS that change aspects of the spatial environment might introduce additional uncertainties in the forecasts of some regions.

Sustainable taxiing using TaxiBot vehicles is expected to contribute greatly to ambitions to reduce emissions and improve air quality in apron bays for airports around the world. However, the impact of large-scale dispatch towing operations at complex airports has never been investigated. This thesis creates a realistic simulation of ground traffic at Schiphol airport based on real-life radar data for different towing policies, to assess the impact on flow and on-time performance, congestion, and emissions.

Results show that TaxiBot towing can decrease fuel consumption and associated CO2 emissions at Schiphol airport by up to 76% per towing movement, and up to 32.9% of total airport emissions. Experiments in which aircraft are allowed to start their engines during the towing movement to save time at the unloading stations, result in outbound taxiing times that are 1 to 1.5 minutes faster than the reference scenario. Inbound times were found to become 2.5 to 3.25 minutes longer, but still contribute to CO2 savings. Apron bay flow was investigated and highlighted significant time gains of three minutes per outbound aircraft. This has the potential to create additional gate capacity at the airport, but further research is needed to quantify these gains. Congestion at unloading stations is found to be minimal, especially in experiments where engines can be started during the tow. Ultrafine-particle heat maps of towing policies show that significant relocation of emissions is possible, improving air quality in bays.

Urban Speed Limit Reduction (USLR) policies, aimed at enhancing road safety and reducing emissions, pose substantial implications for public transport (PT) operations. This study evaluates the impact of implementing a USLR policy in The Hague, Netherlands, reducing speed limits from 50 km/h to 30 km/h on select streets. A multimodal simulation model was employed to quantify changes in PT travel times, operational costs, and modal share. Results indicate a modest average increase of 1.2% in PT travel times, with corresponding timetable hours (TTH) rising by 0.9% and no increase in PT modal share. This would lead to elevated operational costs without increased ridership or revenue. On a city level, the modal share of bikes increases with 0,2% while the modal share of cars decreases with 0,2%. Locally, the results show changing trip destination choice, mode choice, and route choice to circumvent the USLR policy. The findings underscore the need for targeted mitigation measures, such as traffic flow optimizations and speed segregation, to offset increased costs and maintain PT efficiency. This research highlights the nuanced trade-offs of USLR policies, providing actionable insights for urban policymakers and transport operators.

Travelling to work is one of the essential activities in individuals’ daily lives. However, commuters who depend on public transport often face significant challenges in accessing job opportunities, resulting in transport-related social exclusion risks. In recent years, shared mobility services have gained popularity as a solution, offering greater flexibility in first/last-mile segments of multimodal trips. These services have promising potential to improve accessibility for those who rely on public transport, thereby addressing issues of accessibility inequity. Such an intervention aligns well with the unique Dutch cycling culture, where cycling already accounts for a substantial portion of transit journeys. However, limited bike availability at the egress side diminishes the utilization of bikes for the last mile of a trip. To address this issue, integrating shared bikes with transit as an egress mode could be an effective intervention.

This thesis conducted a ”what-if analysis” in the Amsterdam Transport Region to investigate the impacts on job accessibility for different commuters and the equity of the whole transportation system if shared bikes are provided at transit stations as the egress mode. The results highlight the considerable potential of this integration in improving job accessibility and promoting social equity.

Implications drawn from the analysis provide valuable insights. Statistically, groups without car access can benefit more than groups with car access from integrating transit and shared bikes, but benefits might favour high-income groups. Geographically, accessibility improvements are mainly distributed to commuters living next to transit stations, while equity improvements are concentrated in areas with dense and developed public transport systems. Additionally, commuters living close to transit stations are more likely to contribute significantly to the overall accessibility deficiency, even though they have convenient first-mile segments. These implications guide policymakers in prioritizing interventions for targeted groups or regions, and facilitating the creation of a more equitable transportation system.

Delayed maintenance is a major issue in Europe, including the Netherlands. The city of Amsterdam recently started a large-scale maintenance project to renew (renovate or replace) many bridges: an issue that other cities will have to face soon. Decision-making for bridge programming (deciding which bridges should be renewed simultaneously using what strategy) is a contemporary issue. The current decision-making in Amsterdam is not structured; as a result, not all options have been considered, and only a few factors are taken into account. Consequently, this programming has resulted in massive costs and project delays. From the literature review, it can be concluded that no DSS exists that considers all criteria, is suitable for simultaneously planning multiple bridges, and is user-friendly for municipalities. Since it is essential to make an informed decision, there is a need to develop a method to look at decision-making in an integral and systematic sense. Therefore a DSS is designed in this study to support this bridge programming. The DSS will systematically show which main effects play a role, the options a municipality can choose, and their advantages and disadvantages. Moreover, it will reveal the trade-offs a municipality will have to weigh when making its choice. The DSS was designed using a case study, namely a part of Amsterdam. In the end, the expert evaluation showed that the DSS is plausible and leads to meaningful insights into decision-making.

Break bulk terminal operators are constantly in search for improving the performance of their operations. The logistical processes associated with these operations are complex and identifying where improvements have the largest impact on performance therefore is as well. In this paper a combination of system thinking and discrete-event-simulation methods is used to analyse and mimic the loading procedure of deep-sea vessels in the harbour of a large steel manufacturer. A set of alternative resource and machine configurations is tested with a built simulation model to measure the potential performance increase compared to their current operations performance. Results provide insights for the decision-making process in which the manufacturer has to decide what investments have to be made in the future.

Recent years have seen a rise in computation power, storage and opensource data has become more accessible. This results in new opportunities for microsimulation models. Most of these models require a realistic population but the data for this is usually not available. Therefore, population synthesis is used to generate the statistical equivalent of the real population. Since the majority of trips, tours and activity schedules are centered around the home end. It is also important to not only generate a synthetic population but to also attach these households to actual houses. A geodata source is needed for this and OpenStreetMap can be a viable data source. This research focuses on developing a proof of concept of a method for population synthesis at the fine scale of neighborhoods and using OpenStreetMap data to add spatial units to this population. The methodology is developed through literature and implementation in a case study.

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order methods. However, if this method is adopted, the customers of SITA can not receive their productions within the expected periods of time. Besides this, SITA also can not get the procurement discount from the suppliers if they only purchase a low volume and do not make changes. In order to solve this problem, demand forecasting is conducted using the historical sales data of the TS6 kiosk. Through the literature review, suitable qualitative forecasting methods and quantitative forecasting methods which were mainly used in the FMCG industry are got together and combined to increase the forecast accuracy in this research to come out with the final forecast result of the TS6 kiosk. This research also explores the possibility of demand forecasting for the slow-moving consuming industry. By successfully conducting the final forecast, the result can help SITA to shorten procurement lead time so as to meet customers’ expectations as well as save total costs.
KeyWords: Demand Forecasting, Quantitative Forecasting Methods, Qualitative Forecasting Methods, ARIMA, SARIMA, Residual Analysis.

This study researches how station-based bike-sharing can be implemented in strategic transport models. Implementing station-based bike-sharing is a challenging topic, as it requires to combine two modelling techniques. The first is modelling transport tours to make sure that a person who rents a bike returns the bike to the same station. The second challenge is to model multimodality, because shared-bike are always used in combination with other modes. The most prevalent approach on the market to combine the topics is the two-step mode-choice approach. However, the two-step mode choice approach has three important limitations to model SB-bike-sharing. Therefore a new approach is proposed in this study; “The tour-based mode-chain and station choice approach”. Based on a small-scale theoretical case-study the new method gives promising results regarding modelling the combination of tour-based-mode-choice and multimodality.

To address the increasing passenger demand in the coming years and make public transport less crowded and delayed, insights into predicted passenger flow are needed. A wide range of studies has used and validated that smart card data can be one of the sound bases for predicting short-term passenger demand. However, it also has several disadvantages, such as the relatively long collection time, the insufficiency to reflect the relationship between passenger behavior and ridership. Trip planner data, which emerged as a type of real-time transit information, could reduce the perceived waiting time of passengers and increase the transit ridership due to the improved satisfaction. Combining these two types of data could potentially cater to the interest of operators in matching the vehicle supply and passenger flow demand at an operational level. Our results show that it is novel and useful to incorporate trip planner data in short-term ridership prediction, however, entirely based on this kind of data would be inaccurate. Random Forest Regression outperforms the other six models that we have selected. The request-related features (variables) can take up 20% of the importance of short-term ridership prediction.

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.

The impact of the traffic measure package (TMP) configuration on performance outputs of a behavioral policy measure (BPM) is in practice predominately estimated, but not yet substantiated. This was also the case for TMPs implemented in ‘Beter Benutten Vervolg’ (BBV), which was a program that executed hundreds of policy measures to get car users to avoid peak. That is why the research objective of this study is to gain insight into the impact of TMP configuration on the performance outputs of BPMs. The research methods for this study included a literature study, a practical knowledge study, a travel behavior questionnaire analysis and a data analysis of BPM data from BBV. The insights gained from the literature study and the practical knowledge study led to the formation hypotheses on a TMP configuration that could positively impact the performance outputs from a BMP within that package. These hypotheses were then tested using both the travel behavior questionnaire data and BPM data from BBV. Performing the two data analyses on the hypotheses, it is likely that when a BPM interacts with an IPM that incentivizes a switch to the same alternative mode, that BPM would have a higher degree of participation than an independently implemented BPM. It is also possible that a BPM in such a TMP configuration has a higher activity than an average BPM. Due to both performance outputs being positively impacted by the TMP configuration, it is assumed that the effectiveness from such a BPM would also be positively impacted by such a configuration. For the second hypothesis it is likely that when a BPM interacts with another BPM that incentivizes the switch to a different alternative mode, that BPM would have a lower degree of participation than an independently implemented BPM. It is however probable that a BPM in such a TMP configuration has a higher activity than an average BPM. Due to the degree of participation being negatively impacted and the activity being positively impacted by the TMP configuration it is not yet clear if the effectiveness of a BPM would be positively or negatively impacted by such a configuration. The results of this study led to two main policy implications. First, accompany a BPM with an infrastructural improvement that incentivizes the switch to the same mode.
Car users would be more likely to participate in such a BPM and when they participate there is an indication that the activity in avoiding peak is also higher. Both of these performance outputs being positively impacted would also lead to a higher effectiveness of this BPM. Second, offering multiple options for the car users to avoid peak. Less car users would probably participate in such a BPM. The car users that do participate probably have a higher activity in avoiding peak. It is still unclear if how the effectiveness of such a BPM is impacted.

Automated vehicles (AV) are expected to change our future way of transportation and bring many positive effects, such as better road/vehicle safety, reduced environmental costs but also increased on-board productivity. The commonly adopted approach to capture the effect of increased on-board productivity is to reduce the ‘penalty’ associated with travel time. This implies that people will show less aversion to longer travel times (i.e. congestion), prioritise arriving close to the preferred arrival time and thus, increase the peak congestion. However, a distinction should be made between the type of performed activity, since this could likely affect the departure time preferences and thus can have an impact on the congestion pattern. Previous work has shown that travellers with fully automated vehicles in which home activities could better be performed, preferred to depart earlier, and conversely travellers with automated vehicles in which work activities could better be performed, preferred depart later. In addition, the results suggested that automated vehicles could likely increase severe congestion in the future. However, these insights have been obtained with the use of a theoretical single link setting. Up to this point differentiation between activities has not yet been investigated for real-life road networks in which route choice can be incorporated. Therefore this research aims to investigate network effects of activity based departure time choice with fully automated vehicles. It uses the extended α-β-γ scheduling preferences by B. Pudāne and implemented these within a traffic simulation model. This model was assigned to a case study network of the ‘Haaglanden’ region. Results showed that travellers which use AVs to engage in home activities, increase congestion more to the beginning of the morning peak. Vice versa, travellers which use AVs to engage in work activities move congestion more to the end of the morning peak. Travellers which use AVs for both home and work activities would increase congestion in the middle. Considering route choice, a shift in vehicle-kilomtres (VKM) could be observed from the main road network to the underlying road network with the introduction of AVs. This has a negative impact regarding traffic safety, noise and air pollution. Although one might argue that future AVs will have increased safety levels and a more eco-friendly way of driving. Lastly, it was observed that with mixed traffic situations, non-AV users were ‘forced’ to shift their departure times out of the peak moments, move to less congested periods. Conversely, AV users were less affected by these longer travel times and prioritised their preferred arrival times. It is recommended that further research is conducted to include the dynamic modelling element of blocking back. In addition, the extended α-β-γ model parameter values should be assessed to arrive at more substantiated values. Moreover, it suggested to further investigate the impact of differentiation towards road capacities for AVs. Furthermore, this study did not investigate heterogeneity among travellers’ preferences, nor did it take into account endogeneity through which people may assign themselves to a certain type of activity. It is suggest to take these aspects into account in further research. This study assumed full automation, whereas it would be interesting to investigate the effects with lower automation as well. Lastly, an important extension of this research would be to also capture the effect of value of travel time differentiation with respect to route choice, as this study did not differentiate in probabilities to choose a specific route per vehicle type.

This report is written with the aim to evaluate a possible port expansion in the port of San Vicente, Chile. In order to achieve this a thorough analysis on the port operations, hydrodynamical analysis using Delft3D and a bottleneck analysis on the infrastructure have been executed. A preliminary design for the port expansion is created. This design focuses on the land reclamation, the new revetment and the new port layout.

Flooding events not only cause physical damage to the road infrastructure and roadside systems, yet they potentially lead to significant traffic disruptions over the network which have a large impact on the sustainability of urban cities. According to several climate change scenarios, flooding events as a result of intense precipitation are expected to continue into the future becoming more frequent and severe. Therefore, it is essential that engineers design and manage transport networks as efficiently as possible under adverse weather conditions in order to build more resilient transport systems. To do so, professionals need to understand and analyse the impacts of flooding on transport networks. Modelling the impacts of flooding events on the road network is nowadays possible by the use of integrated simulation techniques that combine a flood simulation model with a microscopic traffic model. With these modelling techniques, the geographic location, severity and other impacts of flooding on the road network can be predicted and studied. Nevertheless, in order to perform a consistent traffic simulation under flooding conditions, a thorough analysis of driving behaviour under such conditions needs first to be accomplished, which is a research field that has been scarcely investigated thus far. Therefore, the main contribution of this thesis is to provide a systematic methodology for studying driving adaptation effects under flooding conditions at two complementary levels, namely microscopic and macroscopic level. This consists on a multilevel approach that uses microscopic traffic data of vehicles driving through flooding to extend the study to a macroscopic level. A video analysis procedure called the `The 3-Step Video Analysis Approach´ (3SVAA) is developed by the author to extract microscopic traffic parameters from video recording of vehicles driving under different flood depths. This methodology is implemented through an empirical study that analyses two videos of vehicles crossing waterlogged stretches part of the A94 and A93 major roads in Scotland (UK). First, the 3SVAA is introduced as a suitable data collection technique to extract microscopic traffic variables (i.e. vehicle's speed, time headways and spacing) from video recording of vehicles driving through flooding. More specifically, these parameters are obtained from the vehicle's trajectories in the space (m)-time(s) diagram, which is the main outcome of this video analysis approach. Subsequently, by applying a multilevel analysis, the study can be extended to a macroscopic level by correlating macroscopic variables (i.e. average speed, flow and density) with their microscopic counterparts through the fundamental relationship of traffic flow. This allows to estimate part of the fundamental diagrams of vehicles travelling under different flood depths, and therefore to study flood impacts on free flow speed and capacity.