Shared bicycle systems have become an important element of sustainable urban mobility, helping to reduce emissions and congestion while improving first- and last-mile connectivity. However, these systems often suffer from imbalances between bicycle supply and demand, especially during peak hours, reducing user satisfaction and system reliability.

This thesis addresses these challenges by proposing a dynamic, time-adaptive repositioning strategy for continuously operating shared bicycle systems. The strategy integrates station selection, bicycle quantity decisions, and routing. It adapts the planning horizon based on the time of day using shorter routes during peak periods and longer routes during off-peak hours and at night. User satisfaction is captured through a no-service penalty, while operational efficiency is ensured through time-dependent constraints on route duration.
The model is further extended to account for mixed bicycle fleets and applies vehicle-aware repositioning to improve coordination and reduce computational complexity.

Real-world case studies in Zaragoza and Valladolid show that the dynamic strategy significantly outperforms both static and no-repositioning methods. In Zaragoza, it enabled approximately 400 additional satisfied trips per day, while in Valladolid it achieved exceeding a satisfaction level of 95% despite a high demand for electric bicycles. The dynamic approach also proved robust to a 50% increase in demand for both systems, with only minimal reductions in satisfaction levels.

In summary, this thesis demonstrates that a time-adaptive and integrated repositioning strategy can greatly enhance service levels and user satisfaction in shared bicycle systems. The approach supports broader adoption and strengthens the role of shared bicycles in sustainable urban transportation. Future research could build on these findings by targeting bottlenecks with customized strategies, managing broken bicycles, which reduce dock availability, and accounting for solar-powered stations, without a recharge option, where low battery bicycles occupy dock space.

Urban congestion is an increasing challenge for accessibility, travel times and livability. Although on-demand water cabs offer a flexible and sustainable alternative, the design of optimal mooring locations has been underexplored. To address this problem, this article presents a new method for the strategic placement of mooring locations. The proposed method involves identifying potential mooring locations and applying a customised Adaptive Large Neighbourhood Search (ALNS) to manage computational complexity. The model minimises weighted travel time and cost indicators for all origin destination pairs by selecting between public transport and water cab for each trip. The method was evaluated in a case study of Rotterdam. The findings indicate that the proposed ALNS algorithm outperforms both Large Neighbourhood Search (LNS) and Mixed-Integer Linear Programming (MILP) in computation time while simultaneously generating high quality solutions. Moreover, the model is shown to be capable of identifying an efficient network, resulting in greater coverage, shorter travel times and improved accessibility. These results demonstrate that the method constitutes a powerful tool for policymakers in designing robust, efficient and future proof water cab networks.

Meal delivery platforms operate in dynamic environments, facing complex challenges including fluctuating customer demand, deciding where and which restaurants to offer, and efficient rider allocation. This thesis introduces the Restaurant Selection and Rider Dimensioning Problem (RSRDP) by proposing an integrated optimization model that jointly determines the optimal restaurant assortments and rider allocations to maximize the platform's expected profitability, while maintaining high service quality standards.

A nested logit model captures customer purchasing behavior on the platform to model how customers choose a restaurant to order from, where customers initially choose a cuisine type and subsequently select a specific restaurant within that type. Rider operations are modeled through a spatial-temporal network, enabling optimized management of rider flows across urban zones. We formulate the problem as a Mixed-Integer Linear Program (MILP) and research advanced solution methods including Benders decomposition, column generation, branch-and-price, and heuristics. Finally, we propose our novel Iterative Assortment Generation (IAG) algorithm to solve the problem, ensuring computational feasibility for large-scale scenarios.

Computational experiments based on simulated urban delivery data illustrate that integrating restaurant selection with rider dimensioning consistently increases profitability and maintains high service quality compared to traditional methods that separate these decisions. Furthermore, we explore the impact of two distinct rider compensation policies: commission-based (payment per delivery) and fixed employment (hourly wages). Our findings reveal significant trade-offs between these compensation structures. Commission-based compensation offers greater operational flexibility, adaptability to fluctuating demands, and cost-effectiveness; however, it also leads to increased rider relocations and potentially reduced rider satisfaction due to uncertainty in workload. Conversely, fixed employment provides stable rider availability, promoting better workforce management, but potentially incurs higher operational costs and decreased responsiveness to short-term demand fluctuations.

Based on these insights, we recommend meal delivery platforms adopt integrated optimization models as part of their tactical planning to effectively balance service quality and profitability. Platforms should carefully evaluate their rider compensation policies, recognizing that the ideal choice may depend on the specific context of their operational environment, such as demand variability, competitive landscape, and workforce preferences.

Future research could build on this study by investigating dynamic compensation schemes that adapt in real-time to observed demand conditions. Additionally, incorporating hybrid compensation schemes, combining salaried and commission-based riders, and utilizing the strengths of both policies, provides a promising research direction. Developing multi-objective models that balance profitability against service quality measures, such as timely deliveries or rider satisfaction, would further guide operational decisions and customer and rider retention. Furthermore, future research could improve the way service districts are designed. Instead of using fixed boundaries, districts could be adjusted dynamically and tailored more precisely to specific customer segments and changing demand patterns. This approach would mean that restaurant offerings can also change throughout the day to closely match customers' preferences, potentially increasing overall profitability. Furthermore, modeling couriers individually, rather than as aggregated flows, allows more precise scheduling that better accounts for realistic travel times, shift durations, and rider constraints. Although this approach increases modeling complexity and computational demands, it offers more accurate operational insights, leading to better-informed management decisions. Finally, employing heuristic-driven column-generation methods and comparing solution approaches systematically can improve scalability and accuracy.

To aid the efforts of electrifying public bus systems and to reduce the impact public transit has on emissions in cities, it is necessary for planners to be well informed when making the decision to electrify the transport system. The methodological approach shows that there is value in considering the development of a new transport system over simply electrifying an existing transport network, as there is a trade-off between investment characteristics and passenger satisfaction. Significant cost savings could be realized by jointly considering line planning, infrastructure location, vehicle, battery and fleet size. This has been investigated by developing a decision making model that selects a lineplan seeking to maximize passenger satisfaction, whilst taking into account, vehicle scheduling, battery capacity and charging station location. The model demonstrated that there is a clear trade-off between these aspects and that a multitude of pareto optimal solutions that can be obtained, depending on weighting that decision-makers assign to the objectives. The simplicity of turning the objective of this methodology into a revenue-maximization function, should make this model a suitable starting point for more serious modelling of investment decision in the future.

This research provides insight into ways to increase accessibility for Planned Special Events (PSEs) via discrete choice modelling. A special focus within this study is put into the AFAS AZ Stadium in Alkmaar. During the events held at the AFAS AZ Stadium disturbances are experienced by its visitors with regard to crowding levels and access/egress times. Quantifying the traffic volume on the infrastructure around the venue is done via revealed preference methods. Cyclists are counted via pneumatic tubes. Whereas pedestrians are measured via radio-wave sensors. To find ways to mitigate these problems, interviews are held with organisers of similar PSEs within The Netherlands.

To find the effectiveness of the obtained accessibility measures a stated preference study is held. From this study it is obtained that the transportation mode habit that one has is a key factor within the mode choice. Besides the effect on accessibility additional decision factors are discussed to find the best measure which can be applied best at the AFAS AZ Stadium. Based on the decision factors and the performance of the accessibility measures a total of six effective measures are determined.

In the airline industry revenue and inventory is managed tightly, with the intent to sell each seat at the right price to the right customer at the right time. This does not always go to plan and seats remain unsold, or demand can be greater than what is available. The question then becomes how can capacity and revenue be managed more efficiently? How can capacity and revenue be maximized to ensure that seat spoilage is minimized. This thesis takes a look at this problem and aims to solve this problem by dynamically allocating capacity as well as becoming paired with revenue management processes. By being able to move capacity around to different flights, it is believed that revenue can be maximized and that seat spoilage can be minimized, creating a more effective use of a perishable product. The goal of this thesis is to show that increases are possible, even if by small margins. This thesis looks to use real-world data to help support its findings along with a random distribution to pair alongside a Monte Carlo simulation to help ensure accuracy. In this data driven methodological approach revenue management and a capacity swapping algorithm are integrated. The revenue management module creates a series of seat inventory allocations as well as demand forecasting. This information is then fed into a simulation which will then simulate the entire booking life- cycle of a fight. Here each flight will build its passenger load and will swap aircraft with other aircraft of varying capacities when the algorithm has determined it is necessary and would create a positive increase in revenue and passenger counts. To assist in this approach a further random forest model is developed to help make informed decisions. These changes are further evaluated by the algorithm to ensure that every change that was made was beneficial and if so will accept the swap and continue to build loads as this new flight. Different case studies were developed that introduced different timing scenarios as well as different decision making tools. The two different decision making tools would use either the random forest model that was developed or a simple logic model that would look at estimated final revenue and estimated final passenger loads. The timing scenarios would look to see when swapping would be needed, whether it is a true daily swap or if swaps can be performed once a month. Ultimately there were positive results, with each scenario tested yielding positive increases in the three KPIs examined (Load Factor, Passengers, Revenue). Each scenario involved testing either the simple logic, or random forest model with varying swapping intervals. The best performing scenario was one where flights were allowed to be swapped daily using a random forest decision model. In this best scenario load factor saw an increase of 1%, the number of passengers increased 1.4%, and revenue increased 0.9%; all against the baseline scenario where zero flights were swapped. This translates into a 36,000 USD weekly increase for a small sample in a route network. Across a full route network, even greater increases can be achieved.

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.