Public transportation systems operate in dynamic and unpredictable environments, necessitating close monitoring of real-world operations. Integrating data into route design, scheduling, and policy formulation is essential for creating an efficient and sustainable transit network. This thesis utilises historical bus travel time data to predict future bus travel times, enhancing the analysis of network reachability.

A literature review identified four prediction models to be investigated: Historical Average, Vector AutoRegression, Random Forest, and Long Short-Term Memory deep neural network. A case study involving six bus lines in Groningen was formulated, providing the necessary KoppelVlak 6 and GTFS schedule datasets to be used as inputs for the prediction models. During model development, patterns in historical travel time data were identified, and prediction accuracy was evaluated. The output from the most accurate prediction model was then utilised as input for the reachability analysis.

The analysis demonstrated that complex machine learning models, such as Random Forest and Long Short-Term Memory deep neural networks, yielded the most accurate predictions. The time of day when the journey occurs is particularly predictive of travel and dwell times. Integrating these predictions into a reachability analysis revealed instances of increased reachability, decreased reachability, and missed transfers compared to the original schedule.

The findings demonstrate that travel time prediction models can significantly enhance reachability analyses. This more accurate representation of reachability can be used to identify systemic issues in the design of the public transportation network, allowing for interventions to improve performance.

Recent years show an increasing number of people who use regional public transport, resulting in higher pressure on the operators. This increased pressure results in more disruption for passengers. This paper aims to develop a methodology for estimating the criticality of assets in a regional public transport network, taking the Rotterdam tram system as an example. The consequences of disruptions of each asset in the network are analysed by calculating the effect the disruption has on the average path length for all passengers. Each asset in the network is disconnected one by one to simulate this breakdown. The lines used in Rotterdam are analysed using real passenger data. Results show that 'Erasmus brug' is the most critical asset in the network, as expected by experts from RET. Aside from this, the model presents that the assets around 'Rotterdam Centraal' are less critical than expected because of the alternative routing options for the trams and passengers. This highlights that using this model can give new insights into critical assets in networks.

Commercial waste collection in Dutch cities is a free market in which many waste collectors
operate independently. Collaboration among waste collectors is a relatively recent phenomenon
that is still in its infancy. This study develops a Python-based simulation model using OSMnx and
Google OR-tools to assess the potential benefits of collaboration in waste collection in Rotterdam. Four collaborative scenarios are simulated, three of them in a collaborative setting. Results show that collaboration can significantly reduce the distance traveled by vehicles and the costs for waste collectors, with smaller waste collectors benefiting the most. While full collaboration offers the greatest efficiency gains, even constrained partnerships improve operations. These findings highlight the potential for collaboration to enhance urban logistics and sustainability.

Urban waterways play a crucial role in transportation, recreation, and logistics, yet their traffic capacity remains understudied compared to, for example, road and pedestrian traffic. This paper presents a novel methodology for estimating the capacity of urban waterway networks using empirical data, with a case study focusing on Amsterdam. Existing theoretical capacity models require calibration with real-world observations, which are often lacking. This study uses empirical data to develop a macroscopic capacity estimation model based on intensity-density relationships. Due to the distinct characteristics of urban water traffic, such as reaction times, risk adversity, regulations, and the dominant use case, traditional fundamental diagram models are not trivial to apply. Instead, an intensity percentile-based approach is introduced to define capacity, complemented by an important capacity marker that determines whether the estimated capacity represents the likelihood of the capacity being the actual capacity. The methodology is calibrated using expert-identified bottlenecks and validated through expert validation. Results indicate that the developed methodology shows promise for the capacity estimation of urban canals from dynamic data. However, further refinements are needed in the input data, the calibration of the model, and improving the methodology to account for missing key influencing factors to the traffic characteristics. Still this research provides a foundation for empirical data-driven waterway traffic research, offering policymakers insights for optimizing urban water transport operations.

Increasing congestion and environmental pressures in urban logistics, alongside growing population demands, necessitate innovative solutions. This thesis tackles synchronized two-echelon routing problems within multimodal logistics, specifically exploring the potential of integrating waterborne transport. By developing optimization models and solution approaches for the comprehensive design and management of these systems—encompassing strategic location, tactical allocation, and operational routing and synchronization—the results promote a transition toward more sustainable and efficient urban logistics, ultimately fostering healthier, more livable, and economically vibrant cities.

The increasing volume of global freight trade, coupled with economic growth, necessitates ongoing innovation in optimizing freight operations. Over the past decade, the concept of synchromodality has been explored to encourage a modal shift from unimodal to multimodal transport. Synchromodality, with its flexibility feature, can create more resilient freight transport systems. Various models employing different techniques have been proposed to establish a resilient synchromodal framework capable of reacting to disruptions. However, there are only few studies addressing the unknown duration of disruptions. This research proposes a learning-based modular framework comprising to capture the dynamics of disruptions in multimodal transport and learn to make more effective decisions, thus addressing the challenge of limited prior knowledge about disruptions and enabling fast responses to disruptions.

This project presents a comprehensive study on the development of Integrated Water and Land-based Transportation (IWLT) Systems for city logistics. The research addresses the growing challenges of urban traffic by proposing a decision model for multi-modal transportation systems that leverages waterways alongside traditional road networks. The problem is defined as a two-echelon multi-trip location routing problem with satellite synchronisation (2E-MTLRP-SS), incorporating capacitated vehicles, multiple depots and time constraints. A decomposition-based decision model is introduced, breaking down the problem into manageable sub-problems interconnected through synchronisation in time, space, and load. The decision model uses metaheuristics to be able to handle large-scale, realistic problems and provide feasible solutions for real-life applications. The model’s effectiveness is demonstrated through a case study in Amsterdam, showing the potential of IWLT systems to reduce congestion-related issues and improve the livability of cities. Different scenarios for the IWLT system are investigated, to assist Amsterdam's system developers in making design choices for implementation. The proposed decision model is widely applicable to multi-modal transportation systems all over the world.

There is a growing demand of various metals originating from the sustainable energy transition. The current supply and investments plans for these critical minerals are insufficient to enable this green energy transition. Other problems are the dependency of third countries and multiple drawbacks of land-mining. Deep-sea mining could be a great outcome, by collection of nodules on the seabed in the CCZ area. Different mining trials have been executed, but large-scale mining has not started yet. So far, there is no design guideline for a polymetallic nodule dry bulk (transshipment) terminal. The design and construction phase for a dry bulk terminal takes several years, therefore, the design of such a terminal should be investigated. This thesis will focus on the design of a transshipment terminal to accommodate these large-scale mining activities in the future. The logistics and location determination of this terminal are also taken into account. The thesis contains a case-study which is formed in collaboration with Allseas. For this case-study, the simulation-based approach is chosen to design the transshipment terminal. This approach integrates many stochastic variations, identifies bottlenecks, and allows for sensitivity analysis or ’what if’ scenarios. This method was compared to traditional design methods that rely on rules of thumb and practical experiences.

Multi-agent path finding (MAPF) is the task of finding non-conflicting paths for multiple agents that operate in a environment with shared resources.
Finding an optimal solution quickly becomes intractable for many applications and consequently suboptimal methods are also explored extensively in literature.
This work presents the Decentralized Optimization (DECOP) algorithm: a novel receding horizon control algorithm that exploits insights from MAPF research as well as decentralized control. In the proposed framework, each travelling agent communicates with agents in its proximity to solve a local MAPF problem that considers only a selected tractable number of agents. Inter-agent cooperation and conflict free operation are induced through applying a common local optimization policy during parallel local optimization and through a subsequent path reservation scheme based on random priorities. Inter-agent communication consists of sharing respective route alternatives from which additional information with regard to an agents' entanglement can be inferred which can also be included in the local optimization cost function.
Comparative results with other decentralized algorithms show that the DECOP algorithm yields competitive results while guaranteeing conflict free operations, with limited required communication and without the need of any training time. Among many degrees of freedom to be explored further, including information about the entanglements of an agent's route alternatives in the common policy for local optimization yields an increase in performance and suggests an increased extent of induced cooperation.

Due to the challenges of traffic congestion, pollution, and low transportation efficiency in urban areas, Shared Automated Vehicles (SAVs) are considered a solution that can integrate into existing transportation frameworks to improve traffic performance and environmental sustainability. Currently, there is a research gap in managing the fleet size of SAVs under different matching strategies. Although the literature on the operational and environmental benefits of SAVs is growing, few studies systematically analyze how different fleet management strategies affect service levels and road congestion. This thesis fills this gap by exploring the impacts of Immediate Decision and Batch Offer strategies on fleet size and their corresponding effects on urban traffic performance.

The thesis focuses on the integration of SAV with mixed mobility systems, using SAVs to entirely replace current Mobility on Demand (MoD) services, employing an agent-based simulation method to study the impacts of fleet size and matching strategies on urban traffic performance. This research employs a case study approach, selecting the Haidian District in Beijing as the research area. It uses an innovative demand generation model based on travel distances as simulation request inputs. The model utilizes travel distance distribution functions based on historical data, road network information, and taxi GPS data to generate traffic demand based on network nodes through traffic zone division, probability density calculation, and travel distribution. The model employs the K-means clustering algorithm to divide traffic areas based on the density of traffic nodes rather than specific road network conditions, a macroscopic, low-resolution method with lower computational complexity suitable for large-scale road networks.

The mode choice model in this study analyzes how passengers make decisions between different modes of travel. The model, using a discrete choice framework, studies how passengers choose between SAVs(Shared Automated Vehicles) and POVs(privately operated vehicles). Employing the Logit model, the study considers several key factors influencing travel choice, including travel time, comfort, reliability, and environmental impact, to provide mode selection in simulations. This part emphasizes the importance of travelers' subjective perceptions.

The application of simulation and the Macroscopic Fundamental Diagram (MFD) are central methodologies of this study. In the simulation analysis part, this research uses the agent-based simulation tool FleetPy, focusing on evaluating two key matching strategies: Immediate Decision Simulation (IDS) and Batch Offer Simulation (BOS). By simulating different fleet sizes and matching strategies, it explores the dispatching and operational performance of Shared Autonomous Vehicles in urban road networks and compares them with traditional taxi systems.

The findings indicate that replacing taxi systems with an SAV system can significantly improve fleet efficiency, achieving the same service level with a smaller fleet size. In both small and large fleet scenarios, the SAV system demonstrates higher operational efficiency. In scenarios with smaller fleet sizes, the Batch Offer (BOS) strategy provides higher service levels. By batch processing and optimizing multiple requests, it can effectively improve the success rate of matching and reduce waiting times, while Immediate Decision (IDS) performs better in terms of service timeliness. Simulation results also show that as the fleet size increases, the advantages of the IDS strategy become apparent but may lead to wastage of vehicle resources.

This thesis concerns the fundamental problem of learning the behavior of decisionmaking agents using only observations of how they act in different situations. As humans, we do it all the time, and have been doing it since birth: think about how a child learns to speak and walk. However, this thesis does not only focus on imitating. We go a step further and try to learn why an agent does what it does. In other words, what is the agent’s objective, which led them to act in a certain way? This is a much harder question, but also much more rewarding when answered correctly: knowing the agent’s motivation allows us not only to imitate but also to understand or even influence the agent’s behavior. For this purpose, we ask the question: what is the agent optimizing for when making decisions? For instance, imagine a consumer agent that buys a certain set of products given their budget. To model the consumer’s behavior, we interpret their action as buying the products with maximum utility, given a limited budget. Thus, learning how the consumer evaluates each product (that is, the utility of each product for the consumer) would allow us to understand, replicate, and possibly influence their behavior. Mathematically, we model the decision process of the agent as an optimization program, and we use Inverse Optimization (IO) as a tool to “reverse engineer” the agent’s optimization program from observed behavior. This thesis can be divided into two parts. First, we dive into the mathematical formalization of the IO problem. We look at the geometry of thema thematical objects emerging from IO problems, and we discuss what it means to solve the IO problem and different ways to do it, proposing tractable reformulations and efficient algorithms. In the second part of this thesis, we develop a tailored IO methodology to solve IO problems emerging from routing problems. We test the potential of our methodology for modeling human driving behavior on real-world problems using data from the Amazon Last Mile Routing Research Challenge. We achieve excellent results, showcasing the potential of IO to solve real-world problems. Additionally, we also developed Inv Opt, an open-source Python package to solve general IO problems.

Container transport requires constant improvement to be more efficient and sustainable. Improvements can take the form of innovations, investments or policies. This thesis develops choice-driven methods to enhance decision-making in intermodal freight transport by focusing on demand, supply, and competition aspects considering heterogeneous behaviors and interactions of actors. The proposed methods support policy-makers, transport companies, and planners in foreseeing the impacts of their decisions on the involved actors.

A comprehensive understanding of shippers’ preferences can help transport freight forwarders create targeted transport services and enhance long-term business relationships. Nevertheless, limited research examined the benefit of considering shippers’ preferences in the decision-making of synchromodal transport planning and the collection of relevant data is still not straightforward.
This research proposes an innovative framework to learn shippers’ preferences in synchromodal transport operations and optimize transport services accordingly. A preference learning method is developed to capture shippers' preferences through pairwise comparisons of transport plans. In order to model the underlying complex nonlinear relationships and detect heterogeneity in preferences, artificial neural networks are employed to approximate shippers' utility for a specific plan. Based on the learned preference information, a synchromodal transport planning model with shippers’ preferences (STPM-SP) is proposed, with the objectives of minimizing the total transportation cost and maximizing shippers’ satisfaction. An Adaptive Large Neighborhood Search algorithm is developed for solving this optimization problem. This algorithm takes into account the two different objective functions and searches for Pareto solutions to the planning problem.
A case study is conducted based on the European Rhine-Alpine corridor to demonstrate the feasibility and effectiveness of the proposed methodological framework. Basic discrete choice models, binary logit models, are used as benchmarks for preference learning and the synchromodal transport planning model without preferences (STPM) is used as the benchmark for planning. The results show that the proposed preference learning method has better predictive power than the baseline model, achieving higher accuracy and lower variation. With the consideration of shippers’ preferences, STPM-SP can significantly increase shippers' satisfaction with transport services. Scenarios with different types of preferences are tested and results show that the average of maximum improvements in satisfaction reached 37.76%. This research contributes to learning shippers' preferences in the transport operation process and highlights the importance of incorporating these preferences into the decision-making process of synchromodal transport planning.

In the upcoming years the Municipality of Amsterdam has to cope with renovation works to approximately 200 kilometers of quay walls and 800 bridges. These projects need to be executed in the least burdensome manner, which is why executing parties are obliged to perform their construction logistics via waterways instead of the road. Stakeholders want to have more insight into the consequences of using waterborne construction logistics and what the possible choices and decisions are within the system. In this research a waterborne construction logistics system is presented from which waterborne scenarios can both be compared with each other and with the conventional road modality. A decision support tool is developed and used to quantify these consequences and it can be concluded that water is always the preferred alternative when looking at movements and vessel/vehicle kilometers through the city, which is for both indicators a decrease of approximately 80%. From this analysis can be concluded that in the most optimistic scenario it is possible for waterborne logistics to become less expensive than road transport, but under the conditions that the average transported load and vessel speed are rather high. It is therefore recommended to further investigate the optimum between vessel capacity, loading rate and vessel speed and for the Municipality of Amsterdam to involve executing stakeholders to contribute to reaching this optimum.

In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control the trade-offs between customer attractiveness and profitability with demand management. As there are parametric and non-parametric models, including Machine Learning, to identify the behaviour, they will be compared to define which model represents customer behaviour best. Based on the F1-score, it is evaluated that the Multinomial logit (MNL) and Neural Network (NN) models outperform the created Benchmark model, derived from assumptions from the data, to ensure added value in predicting performance. Since it is found that the advanced predictive models can provide a better understanding of customer behaviour and identify critical customer characteristics, it will be tested whether these models enhance the simulation closer to reality or produce similar results to the Benchmark model, and if so, whether they can be used to optimise the offer strategy. In essence, does the learned behaviour impact the routes and number of offered slots, and can it be utilised to optimise the provided set of offers? A simulation tool is conducted to determine this, which uses real-selected customer time slots to assess any differences from reality and collect key performance indicators (KPIs) for evaluation. The results of the simulations show that incorporating advanced choice models in the simulations adds value and brings the outcomes closer to reality. As a result, a first attempt is made with two test scenarios to determine whether the advanced choice models can be used to optimise the offer strategy. Promising outcomes are found by analysing the results. However, further research is needed to assess the exact impact of the strategies. In future research, it is recommended to determine the influence of using more historical data and data from other companies in combination with other choice models. Also, it is recommended to consider different strategies for optimising the offer set based on incentives or other features.

In current literature, the addition of environmental cost such as fuel consumption or emission is already prevalent. However, the studies aim to improve the behavior of the vehicle while little attention is paid to the holistic view of a city and over-polluted arcs. In an attempt to address this gap, this research aims to develop policies specifically for taxi and ride-hailing services by using a DARP that considers congestion and emission.

Three policies were tested on the busiest area of Jakarta as case study; (1) Routes without consideration of congestion, which is used as means for comparison and base line, (2) Routes optimized on lowest congestion, which is one proposed approach, and finally (3) Routes optimized on lowest emission, which is the second proposed approach and is to understand the difference between optimizing on congestion, or optimizing on emission as a derivative of congestion. For each of these policy, the DARP is given two scenarios; (1) travel times without consideration of congestion, and (2) congested travel times. This is to clearly compare the policies and the routes of each policy. Finally, each scenario is tested for three instance sizes; 5, 30, and 50.

The results show that optimizing on congestion (Policy 2) offers most benefit in travel time and a slight improvement in total emission, where as optimizing on emission (Policy 3) offers a decrease in emission of 49% when compared to the base line and only a slight increase in travel time (on average between 0:30 - 1:30 per request).

In conclusion, this research demonstrates that incorporating congestion and emission variables in DARP can lead to significant reductions in transport-related CO2 emissions, while maintaining an efficient transportation system. This work also provides insights for policy makers on how to design and implement effective congestion and emission mitigating policies and strategies.

In recent years, shared mobility systems have had a growing presence in cities all over the world. This is understandable given its numerous advantages such as the reduced need for personal vehicle ownership, reduced traffic congestion and emissions, increased parking efficiency, and cost savings for users. Overall, shared mobility systems offer the potential to revolutionize transportation, providing individuals with more options and helping to create more sustainable, livable cities. For shared mobility systems to fully deliver their benefits, vehicle availability must be maintained at the right place and time. If the vehicle distribution is not optimal, it may lead to overcrowding and shortages which in turn will discourage usage and lead to reduced revenues for the operator. Therefore, ensuring proper balancing of supply and demand is crucial for the success of the shared mobility service. One way to balance supply and demand is through physically rebalancing vehicles within the service area. In this study, a simulation-based optimization model is created and used to determine the optimal rebalancing operations while quantifying system improvement. A case study is conducted using real data from the Dutch moped sharing provider Felyx to examine the impact of performing rebalancing operations in Eindhoven throughout May ’22. The results demonstrate a potential increase in profit of up to 2.06%. By performing the recommended rebalancing actions several times a week in each city where the operator is active, a significant amount of extra profit can be made. This additional profit will even rise as the usage of shared mobility rises in general.

This study focuses on two-echelon synchronized logistics in a multimodal distribution network for integrated road-based and waterborne transportation motivated by practical city logistics challenges. To evaluate the feasibility and requirements of a multimodal transport system as a potential future logistics solution for supplying hotels, restaurants, and cafés (HoReCa) in the city center of Amsterdam, we propose a methodology to evaluate the effect of various satellite service network designs, fleet sizes of vessel and street vehicles, and city access time window constraints (permitted working hours that can be set by the municipality through policies). To tackle the complexity of the problem, a decomposition-based heuristic is developed to solve large-size instances for a rich 2E-VRP variant. The decomposition-based heuristic consists of three main phases: a location phase for determining satellite locations, an allocation phase for establishing assignments of satellites and customers, and a routing phase for solving the routing problem given operational settings. In addition, we propose various modeling approaches to represent system designs with different operational limitations in practice. First, we consider two transshipment types: one-to-many and one-to-one transshipments. The transshipment type is related to satellite resources in terms of space availability and lifting capabilities. Secondly, we propose two simulation-optimization frameworks for iteratively configuring the workload of and workforce at satellites. We demonstrate the methodology by conducting a case study for supplying over 1600 HoReCa in the city center of Amsterdam. The results indicate that balancing the workload of satellites is critical for scenarios with few permitted working hours. By configuring the workforce at satellites, a good balance between the number of vessels, street vehicles, and street-level distance is found for scenarios with more permitted working hours. We show that the transshipment type significantly affects the service level and system requirements to meet customer demand, with a sensitivity analysis on transshipment times at satellites indicating significant benefits for reduced transshipment time in the case of one-to-one transshipments. Moreover, the computational experiments show several trade-offs between the required number of satellites, vessels, street vehicles, and permitted working hours to meet customer demand.

Airports and airlines are examining and committing to the electrification of Ground Support Equipment (GSE). To be able to estimate the required quantity of eGSE, the charging requirements of eGSE, the change of airport electricity requirements, and the scheduling possibilities of eGSE charging for the existing turnaround procedures, a model was developed to simulate and optimize the GSE operations at airports. This was done by means of a Task Scheduling Problem (TSP), that is optimized using Mixed-Integer Linear Programming (MILP). A case study was performed on KLM's GSE fleet at Amsterdam Airport Schiphol. Based on this, it was concluded that there is no difference in the capacity that can be achieved for GSE types that can last an entire day on a single battery charge. However, another group of GSE types experiences battery depletion before the day concludes, requiring measures to maintain the capacity. The results indicate the model's suitability for strategic decision-making. Next to that, the model is effective on an operational level. The use of the model has the potential to make the use of resources in the operation more efficient.